VOLUME 115 NO. 5 MAY 2015

The SAIMM in conjunction with theDepartment of Mining Engineering, University of Pretoria, presents the

and spatial information applications in the mining industry Conference 2015

University of Pretoria15–16 July 2015

Conference Announcement

For further information contact:Conference Co-ordinator, Camielah JardineSAIMM, P O Box 61127, Marshalltown 2107

Tel: (011) 834-1273/7Fax: (011) 833-8156 or (011) 838-5923

E-mail: camialah@saimm.co.zaWebsite: http://www.saimm.co.za

BACKGROUNDVirtual Reality (VR) is a continuouslyevolving technology where a user inter-acts with a three-dimensional computer-simulated environment, which the userperceives as comparable to real worldobjects and events. As computer hard-ware and software technology have im-proved, the ease with which interactivesimulations can be developed and de-ployed has improved significantly andlower cost, high-quality developmenttools have become available. VR appli-cations in education and training havebecome increasingly popular in the con-text of effective knowledge transfer. Al-though VR technology has improvedsignificantly over the last few years, itspotential advantages with specific refer-ence to Safety Health and the Environ-ment (SHE) and mine planning anddesign, are relatively unknown.

Mining technical information is threedimensional. Miners think in pictures oftheir holes in the ground. Recent ad-vances in open standard spatial data for-mats have opened up new and highlyvisual opportunities for integrated infor-mation management. Routine reportingand analytical exercises create new lev-els of insight into mining issues and chal-lenges.

The ability to integrate mining tech-nical data irrespective of source (323 andcounting) opens up the possibility of in-tegration with commercial information,access to readily available analyticaltoolsets and therefore has conse-quences across the enterprise.

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ii MAY 2015 The Journal of The Southern African Institute of Mining and Metallurgy

OFFICE BEARERS AND COUNCIL FOR THE2014/2015 SESSION

Honorary PresidentMike TekePresident, Chamber of Mines of South Africa

Honorary Vice-PresidentsNgoako RamatlhodiMinister of Mineral Resources, South AfricaRob DaviesMinister of Trade and Industry, South AfricaNaledi PandoMinister of Science and Technology, South Africa

PresidentJ.L. Porter

President ElectR.T. Jones

Vice-PresidentsC. MusingwiniS. Ndlovu

Immediate Past PresidentM. Dworzanowski

Honorary TreasurerC. Musingwini

Ordinary Members on Council

V.G. Duke T. PegramM.F. Handley S. RupprechtA.S. Macfarlane N. SearleM. Motuku A.G. SmithM. Mthenjane M.H. SolomonD.D. Munro D. TudorG. Njowa D.J. van Niekerk

Past Presidents Serving on CouncilN.A. Barcza J.C. Ngoma R.D. Beck S.J. Ramokgopa J.A. Cruise M.H. Rogers J.R. Dixon G.L. Smith F.M.G. Egerton J.N. van der Merwe G.V.R. Landman W.H. van NiekerkR.P. Mohring

Branch ChairmenDRC S. MalebaJohannesburg I. AshmoleNamibia N.M. NamateNorthern Cape C. van WykPretoria N. NaudeWestern Cape C. DorflingZambia D. MumaZimbabwe S. NdiyambaZululand C.W. Mienie

Corresponding Members of CouncilAustralia: I.J. Corrans, R.J. Dippenaar, A. Croll,

C. Workman-DaviesAustria: H. WagnerBotswana: S.D. WilliamsUnited Kingdom: J.J.L. Cilliers, N.A. BarczaUSA: J-M.M. Rendu, P.C. Pistorius

The Southern African Institute of Mining and Metallurgy

PAST PRESIDENTS

*Deceased

* W. Bettel (1894–1895)* A.F. Crosse (1895–1896)* W.R. Feldtmann (1896–1897)* C. Butters (1897–1898)* J. Loevy (1898–1899)* J.R. Williams (1899–1903)* S.H. Pearce (1903–1904)* W.A. Caldecott (1904–1905)* W. Cullen (1905–1906)* E.H. Johnson (1906–1907)* J. Yates (1907–1908)* R.G. Bevington (1908–1909)* A. McA. Johnston (1909–1910)* J. Moir (1910–1911)* C.B. Saner (1911–1912)* W.R. Dowling (1912–1913)* A. Richardson (1913–1914)* G.H. Stanley (1914–1915)* J.E. Thomas (1915–1916)* J.A. Wilkinson (1916–1917)* G. Hildick-Smith (1917–1918)* H.S. Meyer (1918–1919)* J. Gray (1919–1920)* J. Chilton (1920–1921)* F. Wartenweiler (1921–1922)* G.A. Watermeyer (1922–1923)* F.W. Watson (1923–1924)* C.J. Gray (1924–1925)* H.A. White (1925–1926)* H.R. Adam (1926–1927)* Sir Robert Kotze (1927–1928)* J.A. Woodburn (1928–1929)* H. Pirow (1929–1930)* J. Henderson (1930–1931)* A. King (1931–1932)* V. Nimmo-Dewar (1932–1933)* P.N. Lategan (1933–1934)* E.C. Ranson (1934–1935)* R.A. Flugge-De-Smidt

(1935–1936)* T.K. Prentice (1936–1937)* R.S.G. Stokes (1937–1938)* P.E. Hall (1938–1939)* E.H.A. Joseph (1939–1940)* J.H. Dobson (1940–1941)* Theo Meyer (1941–1942)* John V. Muller (1942–1943)* C. Biccard Jeppe (1943–1944)* P.J. Louis Bok (1944–1945)* J.T. McIntyre (1945–1946)* M. Falcon (1946–1947)* A. Clemens (1947–1948)* F.G. Hill (1948–1949)* O.A.E. Jackson (1949–1950)* W.E. Gooday (1950–1951)* C.J. Irving (1951–1952)* D.D. Stitt (1952–1953)* M.C.G. Meyer (1953–1954)* L.A. Bushell (1954–1955)

* H. Britten (1955–1956)* Wm. Bleloch (1956–1957)* H. Simon (1957–1958)* M. Barcza (1958–1959)* R.J. Adamson (1959–1960)* W.S. Findlay (1960–1961)

D.G. Maxwell (1961–1962)* J. de V. Lambrechts (1962–1963)* J.F. Reid (1963–1964)* D.M. Jamieson (1964–1965)* H.E. Cross (1965–1966)* D. Gordon Jones (1966–1967)* P. Lambooy (1967–1968)* R.C.J. Goode (1968–1969)* J.K.E. Douglas (1969–1970)* V.C. Robinson (1970–1971)* D.D. Howat (1971–1972)

J.P. Hugo (1972–1973)* P.W.J. van Rensburg

(1973–1974)* R.P. Plewman (1974–1975)

R.E. Robinson (1975–1976)* M.D.G. Salamon (1976–1977)* P.A. Von Wielligh (1977–1978)* M.G. Atmore (1978–1979)* D.A. Viljoen (1979–1980)* P.R. Jochens (1980–1981)

G.Y. Nisbet (1981–1982)A.N. Brown (1982–1983)

* R.P. King (1983–1984)J.D. Austin (1984–1985)H.E. James (1985–1986)H. Wagner (1986–1987)

* B.C. Alberts (1987–1988)C.E. Fivaz (1988–1989)O.K.H. Steffen (1989–1990)

* H.G. Mosenthal (1990–1991)R.D. Beck (1991–1992)J.P. Hoffman (1992–1993)

* H. Scott-Russell (1993–1994)J.A. Cruise (1994–1995)D.A.J. Ross-Watt (1995–1996)N.A. Barcza (1996–1997)R.P. Mohring (1997–1998)J.R. Dixon (1998–1999)M.H. Rogers (1999–2000)L.A. Cramer (2000–2001)

* A.A.B. Douglas (2001–2002)S.J. Ramokgopa (2002-2003)T.R. Stacey (2003–2004)F.M.G. Egerton (2004–2005)W.H. van Niekerk (2005–2006)R.P.H. Willis (2006–2007)R.G.B. Pickering (2007–2008)A.M. Garbers-Craig (2008–2009)J.C. Ngoma (2009–2010)G.V.R. Landman (2010–2011)J.N. van der Merwe (2011–2012)G.L. Smith (2012–2013)

Honorary Legal AdvisersVan Hulsteyns Attorneys

AuditorsMessrs R.H. Kitching

Secretaries

The Southern African Institute of Mining and MetallurgyFifth Floor, Chamber of Mines Building5 Hollard Street, Johannesburg 2001P.O. Box 61127, Marshalltown 2107Telephone (011) 834-1273/7Fax (011) 838-5923 or (011) 833-8156E-mail: journal@saimm.co.za

The Journal of The Southern African Institute of Mining and Metallurgy JUNE 2015 ▲iii

ContentsJournal Commentby G.L. Smith . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPresident’s Corner by J.L. Porter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Spotlight: SANCOT Newsby R. Tluczek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viThe SAIMM Young Professionals’ Council (SAIMM-YPC)by T. Mmola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Predicting the probability of Iron-Rich Ultramafic Pegmatite (IRUP) in the Merensky Reef at Lonmin’s Karee Mineby D. Hoffmann and S. Plumb. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465Tough choices facing the South African mining industry by A. Lane, J. Guzek, and W. van Antwerpen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471Crush pillar support – designing for controlled pillar failure by M. du Plessis and D.F. Malan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481The application of pumpable emulsions in narrow-reef stopingby S.P. Pearton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489Corrosion resistance of laser-cladded 304L stainless steel enriched with ruthenium additions exposed to sulphuric acid and sodium chloride media by J. van der Merwe and D. Tharandt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499Fire and brimstone: The roasting of a Merensky PGM concentrateby R.I. Rambiyana, P. den Hoed, and A.M. Garbers-Craig. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507Strategic and tactical requirements of a mining long-term planby B.J. Kloppers, C.J. Horn, and J.V.Z. Visser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515

Integration of imprecise and biased data into mineral resource estimatesby A. Cornah and E Machaka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523Stochastic simulation for budget prediction for large surface mines in the South African mining industryby J. Hager, V.S.S. Yadavalli, and R. Webber-Youngman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531Q-coda estimation in the Kaapvaal Cratonby D.J. Birch, A. Cichowicz, and D. Grobbelaar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541Geometallurgical model of a copper sulphide mine for long-term planningby G. Compan, E. Pizarro, and A. Videla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549Introduction to the production of clean steelby J.D. Steenkamp and L. du Preez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557

International Advisory Board

R. Dimitrakopoulos, McGill University, CanadaD. Dreisinger, University of British Columbia, CanadaE. Esterhuizen, NIOSH Research Organization, USAH. Mitri, McGill University, CanadaM.J. Nicol, Murdoch University, AustraliaE. Topal, Curtin University, Australia

VOLUME 115 NO. 6 JUNE 2015

Editorial BoardR.D. BeckJ. Beukes

P. den HoedM. Dworzanowski

M.F. HandleyR.T. Jones

W.C. JoughinJ.A. LuckmannC. MusingwiniR.E. Robinson

T.R. StaceyR.J. Stewart

Editorial ConsultantD. Tudor

Typeset and Published byThe Southern African Instituteof Mining and MetallurgyP.O. Box 61127Marshalltown 2107Telephone (011) 834-1273/7Fax (011) 838-5923E-mail: journal@saimm.co.za

Printed by Camera Press, Johannesburg

AdvertisingRepresentativeBarbara SpenceAvenue AdvertisingTelephone (011) 463-7940E-mail: barbara@avenue.co.zaThe SecretariatThe Southern AfricanInstitute of Mining andMetallurgyISSN 2225-6253 (print)ISSN 2411-9717 (online)

THE INSTITUTE, AS A BODY, ISNOT RESPONSIBLE FOR THESTATEMENTS AND OPINIONSADVANCED IN ANY OF ITSPUBLICATIONS.Copyright© 1978 by The Southern AfricanInstitute of Mining and Metallurgy. Allrights reserved. Multiple copying of thecontents of this publication or partsthereof without permission is in breach ofcopyright, but permission is hereby givenfor the copying of titles and abstracts ofpapers and names of authors. Permissionto copy illustrations and short extractsfrom the text of individual contributions isusually given upon written application tothe Institute, provided that the source (andwhere appropriate, the copyright) isacknowledged. Apart from any fair dealingfor the purposes of review or criticismunder The Copyright Act no. 98, 1978,Section 12, of the Republic of SouthAfrica, a single copy of an article may besupplied by a library for the purposes ofresearch or private study. No part of thispublication may be reproduced, stored ina retrieval system, or transmitted in anyform or by any means without the priorpermission of the publishers. Multiplecopying of the contents of the publicationwithout permission is always illegal.

U.S. Copyright Law applicable to users Inthe U.S.A.The appearance of the statement ofcopyright at the bottom of the first page ofan article appearing in this journalindicates that the copyright holderconsents to the making of copies of thearticle for personal or internal use. Thisconsent is given on condition that thecopier pays the stated fee for each copy ofa paper beyond that permitted by Section107 or 108 of the U.S. Copyright Law. Thefee is to be paid through the CopyrightClearance Center, Inc., Operations Center,P.O. Box 765, Schenectady, New York12301, U.S.A. This consent does notextend to other kinds of copying, such ascopying for general distribution, foradvertising or promotional purposes, forcreating new collective works, or forresale.

GENERAL PAPERS

PLATINUM CONFERENCE PAPERS

VOLUME 115 NO. 6 JUNE 2015

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iv MAY 2015 The Journal of The Southern African Institute of Mining and Metallurgy

P yrometallurgical operations are essentiallyconcerned with the high-temperatureprocessing of materials. These chemical

processes can be extremely complex, involvingreactions between gas, solids, liquid slag, and liquidmetal (and sometimes other phases as well). Inaddition to the chemistry, consideration must also begiven to the energy supply, containment, and flow ofthe various streams. Pyrometallurgical processes mustbe evaluated at many stages during their developmentand design, and when operating changes areintroduced. Because experimental work inpyrometallurgy is expensive, a system should becharacterized as thoroughly as possible beforeexperimental work is undertaken. Computer simulationallows the requirements of a particular process to bedetermined quickly and reliably.

Pyrometallurgy is at least 6000 years old, with theoldest known smelters being used for the production ofcopper in the Middle East. Mathematical modelling, atleast in its simplest form, is much older than this. Theoldest known mathematical artefact is considered to bethe Lebombo bone – a 35 000-year-old baboon fibuladiscovered in a cave in the Lebombo Mountains inSwaziland in the 1970s – that has a series of 29notches that were deliberately cut to help to calculatenumbers and perhaps also measure the passage oftime. The abacus dates from as early as 2400 BC inBabylon, and was also found in China, Egypt, Greece,and Rome, and used by the Aztecs.

The earliest recognisable computers were the MarkI (1944) and Mark II (1945) developed at HarvardUniversity. These electromagnetic computers are seenas the first universal calculators (even though theylacked branching) and operated at three calculationsper second. The first electronic general-purposecomputer, ENIAC (Electronic Numerical Integrator AndComputer) was built in 1946 at the University ofPennsylvania. ENIAC could do simple addition orsubtraction of two ten-digit numbers at the rate of5000 per second, or could do 357 multiplications persecond. ENIAC weighed 27 tons, measured 2.4 m × 0.9m × 30 m, and consumed 150 kW of power. A rather

daring prediction was made in the March 1949 issue ofPopular Mechanics: ‘Where a calculator on the ENIAC isequipped with 18,000 vacuum tubes and weighs 30tons, computers in the future may have only 1,000vacuum tubes and perhaps weigh 1½ tons.’

In my own lifetime, I progressed from using logtables, then a slide rule, to an early scientific calculatorin high school. At university, I started programming inFortran on a mainframe computer that used punchedcards (until the late 1970s). I then used aprogrammable calculator with less than one kilobyte ofmemory to design heat exchangers (an oddly namedpiece of equipment: even if there was a caloric-likesubstance called ‘heat’, it certainly isn't exchanged)and cyclones. When I started work, early chemicalequilibrium calculations were carried out (slowly andexpensively) via a Saponet satellite link to the F*A*C*Tthermodynamic database (the predecessor of FactSage)hosted on a mainframe computer at McGill Universityin Montreal. At that time, calculations were limited inthe number of elements that could be accommodated,so the time was ripe for a desktop computer programthat could be applied to more complex systems.Pyrosim computer software for the steady-statesequential-modular simulation of pyrometallurgicalprocesses was initially developed at Mintek in 1985 ona 64 kB Apple II computer (1 MHz), and was presentedat the APCOM 87 conference in 1987. Pyrosim movedto an MS-DOS version in 1988 when it was first usedin industry. This software was originally developed tosimulate various process routes for the production ofraw stainless steel, but the structure was kept generalenough to allow it to be used to calculate predictivesteady-state mass and energy balances for a very widerange of processes. Pyrosim thermodynamic modellingsoftware was eventually installed at 95 sites in 22countries on 6 continents.

Desktop computing improved rapidly, with aroughly 1500-fold increase in speed and storagecapacity, from the Apple II to a fast Pentium processor.A typical Pyrosim simulation would have taken over anhour on an Apple II, but just over three seconds on a33 MHz i486 computer (1200 times faster). A study of

Journal CommentPyrometallurgy modelling

‘It is unworthy of excellent men to lose hours like slaves in the labour of calculation which could safely be relegated to anyone else if machines were used.’

Gottfried Wilhelm Leibniz (1646 - 1716)

‘The computer is incredibly fast, accurate, and stupid. Man is unbelievably slow, inaccurate, and brilliant. The marriage of the two is a force beyond calculation.’

Leo Cherne (1912-1999)

The Journal of The Southern African Institute of Mining and Metallurgy MAY 2015 ▲v

practical desktop supercomputing was carried out in1993, using a real-world benchmark, written in ‘C’, tosolve a finite-volume energy-transfer problem in two-dimensional cylindrical geometry, using theTriDiagonal Matrix Algorithm. The chosen referencecomputer was a 50 MHz i486 computer (nominallyrated at 30 MIPS, or 30 million instructions persecond). At that time, the Pentium processor (rated at100 MIPS) was not yet commercially available. Usingthis practical test, it was found that the i486-50 was 80times faster than the original IBM PC (4.77 MHz with8087 coprocessor), and the well-established Cray 2supercomputer was in turn 5.25 times as fast as thei486-50. This indicated that the Cray was only abouttwice as fast as a Pentium for this type of problem. TheCray 2 was rated at 1600 MFlop/s (million floating-point operations per second). Since that time, thecomputing speed of the world's fastest supercomputerhas increased exponentially, from about 124 GFlop/s(124 × 109) in 1994 to about 34 PFlop/s (34 × 1015)in 2014. A typical personal computer in 2014 was ableto run at about 500 GFlop/s – much faster than theworld's fastest supercomputer from 1994.

If we assume that computer power will continue toincrease in the way it has done for the past twentyyears, it is interesting to consider what might becomepossible in years to come. For example, a fully resolvedmodel of an industrial-scale electric arc (say 50 kAover about 0.5 m) is expected to require over 200PFlop/s (about half a million times that of thecomputing power of today's personal computers). Atcurrent growth predictions, this should be achieved onthe world’s fastest computer in 2017, on the 500thfastest computer in 2024, and on the desktop in 2037.

This edition of the Journal contains a selection ofpapers from the SAIMM Pyrometallurgical Modellingconference. These papers should provide a goodsample of the current activities in this very dynamicfield. One of the challenges encountered in modellingpyrometallurgical processes is the chemical complexityof some of the feed materials and products. How bestto represent coal has challenged modellers for years,but a systematic approach to this has now beenadopted. Thermodynamic modelling has been appliedto chemical reaction systems using diverse feedmaterials and reductants to produce a wide variety ofmetals, from platinum to clean steel. Techno-economicmodels bring in the economic aspects too. The extremeconditions in pyrometallurgical reactors have alsoproved challenging, involving not only very high

temperatures, but jets of air at sonic velocity have beenused in converting of PGM matte; and comparisons ofmodelling and industrial trials have been made. Fluidflow analysis has become more widespread and hasbeen used to model the flow in tundishes andconverters. A multi-physics approach, involving theinteraction between concentration fields and amagneto-hydrodynamic description of an electric archas been used to study the effect of dust particles in afurnace. Modelling of gas-solid reacting systems hasbeen used to study the sintering of iron ore, as well asrotary kilns for direct reduction.

Pyrometallurgy superficially appears primitive andlittle changed from hundreds of years ago, but is one ofthe most challenging areas to understand and model.The simultaneous effects of very high temperatures,energy transfer, fluid dynamics, electromagnetics,phase changes, multiphase flow, free surface flow,particulate materials, and thermochemistry will providemuch to interest pyrometallurgical modellers of thefuture. The dramatic increases in computing powermake it possible to carry out different modellingapproaches that earlier generations could only havedreamed about.

R.T. Jones

Journal Comment (continued)

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vi MAY 2015 The Journal of The Southern African Institute of Mining and Metallurgy

SpotlightSANCOT 2015 Conference

The South African Committee on Tunnelling (SANCOT) held its annual conference from 23 to 24 April 2015 under the theme of‘Mechanised Underground Excavation in Mining and Civil Engineering’. The conference took place at the beautiful Zulu

Kingdom, Elangeni Hotel in Durban, South Africa. This conference was in response to the civil and mining industry being under immense pressure to deliver projects fast,

efficiently, and as safely as possible. Mechanized underground excavation and support installation is proving to be an invaluableand cost-effective tool in project execution. Technology exists for the mechanized excavation of tunnels from as small as 300 mm toin excess of 18 m in order to access orebodies, build roads, railways, facilitate the installation of utilities, construct storage cavernsfor gas and oil, etc.

The conference was attended by various stakeholders involved with underground excavation, both locally and internationally,including the following:

� Shaft-sinking companies� EPCM executives and management� Civil construction companies� Local municipalities and water authorities� Engineering design and consulting companies� Project management practitioners� Technology suppliers and consumers� University students.Following the presentation of papers on 23 and 24 April, on 25 April delegates embarked on a technical site visit which

presented them with the opportunity to walk through the tunnel underlying the Durban Harbour entrance, visit the adjoining pumpstation, and enjoy a one- hour cruise around the harbour.

From the point of view of the SANCOT Young Members’ Group (SANCOT-YMG) technically, the conference was a success. Itwas most encouraging to see this youth forum receiving a warm welcome and support from all stakeholders at the conference. Asalready identified, young professionals can make valuable contributions in the civil and mining industries. SANCOT has made acommitment to support and encourage the participation of young professionals in all its organizational activities. This way, theyoung professionals are also able to make a meaningful contribution towards their academic, professional, and technicaldevelopment.

Primarily, SANCOT-YMG’s focus is to ensure that there is alliance and synergy, both with its mother bodies, which are SANCOTand SAIMM, and with other related organizations and professional bodies on various aspects that affect young professionals. Thisincludes the execution of the mandate as adopted from the resolution taken at the International Tunnelling and Underground SpaceAssociation (ITA) General Assembly of 2014 in Iguassu, Brazil, which sets out the following aims:

� To provide a technical networking platform within the ITA for young professionals and students� To bridge the gap between generations and to network across all experience levels in the industry� To promote awareness of the tunnelling and underground space industry to new generations� To provide young professionals and students with a voice in the ITA, including the Working Groups� To look after the next generation of tunnelling professionals and to pass on the aims and ideals of the ITA.As a representative of one of the 71 Member Nations in the ITA, SANCOT is expected to participate in all the Association’s

activities. What is of particular interest in the forthcoming ITA’s international events is that, going forward, the young professionalgroups from each of the Member Nations will now be able to take part in the ITA’s activities. SANCOT-YMG will also be taking part,and will be represented by its current chairman, Mr Lucky Nene, at this year’s International Tunnelling and Underground SpaceAssociation Young Members Group (ITA-YM) session of the ITA’s 41st General Assembly and Congress, taking place between 22and 28 May 2015 in Dubrovnik, Croatia.

The upcoming annual meetings of the ITA General Assembly will be held at the following venues:� Dubrovnik, Croatia: 22–28 May 2015, during the ITA-AITES WTC 2015 ‘Promoting Tunnelling in South East European

Region’� San Francisco, USA: 22–28 April 2016, during the ITA-AITES WTC 2016 ‘Uniting our Industry’� Bergen, Norway: 9–16 June 2017, during the ITA-AITES WTC 2017 ‘Surface Problems – Underground Solutions’.Young professionals in the tunnelling space, including those who would like to participate in general and offer assistance in the

sustenance of this young professional forum, are invited to contact the SANCOT–YMG chairman directly onLucky.Nene@aecom.com or via Raymond van der Berg on raymond@saimm.co.za.

L. Nene

The Journal of The Southern African Institute of Mining and Metallurgy MAY 2015 ▲vii

Can you believe that it is May 2015 already? Nine times out of ten, when people of an oldergeneration are asked this question it results in an impromptu discussion includingremarks such as ‘where has the time gone?’ or ‘time seems to go by faster as you get

older’. All of which is nonsense from Einstein’s perspective. However, I do have my ownanswer to these questions and it is simple. When I was in my 20s it would probably take mefive seconds to tie my shoelaces and run up a flight of stairs. Now it takes me five minutes toput on my slippers and haul myself up to the next floor – a hard reality but true for most! So

it is that we find ourselves in May and I had planned to write an update of the SAIMM activities afterthe first six months – well eight months have flown by and I must keep my promise to myself and the Journal.

In many respects the mining industry has not had a good 2014/2015. The overall contraction in the industry filters throughto all levels of activity, and the SAIMM is not immune to this process. We have seen quite a dramatic drop-off in attendance atconferences, and several planned events have had to be cancelled by our Technical Programme Committees. As I have writtenpreviously, curtailment in discretionary spending by mining companies is quite understandable. However, it is regrettable that itis the development of our young engineers that suffers the most in this respect, in that they are not able to have theopportunities of either writing and presenting papers or attending conferences and developing their network of industrycontacts.

It is important to note that, based on wise investment decisions some years ago, the SAIMM is well positioned financially tocontinue operations without compromising our quality of work or various initiatives in the industry. This Council-sanctionedstrategy is a cornerstone of the SAIMM’s ability to grow within southern Africa. However, we work in a dynamic environment,and a meeting has been scheduled whereby the operation and business model of the Technical Programme Committees will bereviewed to ensure that the SAIMM remains viable and competitive under the current circumstances.

On a much more positive note we have good progress to report on the following activities:

Mining Dialogues 360°The Mineral Economics Division of the SAIMM had been given a mandate to look at the economic issues that impacted on theminerals industry in South Africa. A report was prepared in this context a number of years ago and MD360° was established toengage with government, mining companies, and other stakeholders. An Advisory Committee of eminent persons has beenestablished by Mr Mike Solomon and chaired by the SAIMM President in order to engage the above groups. The AdvisoryCommittee’s objective is to assist in developing a set of agenda items that could be tabled at a national level to raise researchfunding in the same way as the Resource Nationalism initiative was funded.

Investment CommitteeAs mentioned above, the SAIMM’s investments have performed well and provide long-term stability to the organization.However, Council requested that Dr G. Smith, Sam Moolla, and I investigate, through independent financial advisors, whether ornot we still have the appropriate investment strategy in terms of markets, bonds, and property. This exercise in nearly completeand will be reported at the next Council meeting.

Regional Development ManagerSome of you may not be aware that the SAIMM has employed a Regional Development Manager in a part-time position. Overseveral years now we have been working on establishing sustainable branches in Zambia, Zimbabwe, Namibia, Botswana, andthe DRC. Considerable effort has been expended on this by the Johannesburg office as well as our Officers and Members inthese countries. In order to meet the increasing demands of supporting these new organizations and to ensure that themarketing of the SAIMM is managed evenly across the region, Council approved the employment of a dedicated andknowledgeable person. Malcolm Walker has filled this role since October 2014 and has actively supported the SAIMM office. Hehas specifically focused on reaching out to all of the appropriate universities in the region and we have had an excellentresponse.

SAMREC and SAMVAL COMMITTEE (SSC)The SSC has been fulfilling an important role for the Johannesburg Stock Exchange for several years. Over this time thestructure has developed and matured, and the Geological Society of South Africa and the SAIMM have met during the year todetermine what (if anything) needs to be done to further support the initiative. This resulted in the establishment of anappropriate budget and an updated Terms of Reference for the SSC. The final drafts of these documents will be tabled at the SSCmeeting on 28 May 2015.

One of the structures that is required by both the by-laws of the SAIMM and the ToR of the SSC is an effective process forthe handling of complaints about professional conduct. This had not received the appropriate attention of the SAIMM in thepast, and a duly elected and appointed Complaints Committee and Ethics Committee will be approved at the next Councilmeeting.

There are many other items to comment on relating to the Young Professionals Committee, our relationship with ECSA, etc.,but I fear I am running out of space and … time. I only type slowly!

J.L. PorterPresident, SAIMM

President’s

Corner

▲

viii MAY 2015 The Journal of The Southern African Institute of Mining and Metallurgy

Jos Lurie, an honorary life Fellow of the Southern African Institute of Mining and Metallurgypassed away on Monday 27 April 2015 at the age of 86 after his fight with cancer.

From his early childhood in Maclear in East Griqualand Jos challenged the world and showed hisleadership qualities in the Scouts and through independent camping adventures. His youthfulexperiences are recorded in an unpublished book he wrote in the last full year of his life, entitled ‘AnExciting Life’, which I was privileged to proofread. This was typical of Jos recording all his activitiesand contributions made during his ‘exciting life’.

His first employment was as a filing clerk, followed by a job as a trainee draftsman – he qualifiedin 1947. He progressed through his own efforts to the Department of Trigonometrical Survey inMowbray, Cape Town, where he extended his skills to surveying and obtained distinction in thesurvey course ,including photogrammetry. While in Cape Town his adventurous spirit prevailed, withweekly climbs of Table Mountain.

His survey excellence was built up in Zululand and the Cape and culminated in his obtaining employment in 1954 as anengineering surveyor in Kenya to set up irrigation schemes. During this 4-year period Jos learned to fly small planes and hadthe opportunity to climb the Kilimanjaro parasite volcanic peak of Kibo (5890 m), the highest altitude in Africa, in ‘recordtime’, accompanied by only one other friend. He was always an adventurer!

Jos met and married Brenda, his wife of 57 years, in 1958. Brenda was an English nursing sister from Surrey, who wasin the middle of a nursing contract in Kenya at that time. Their honeymoon took them on an adventure in a Land Roverthrough Central Africa during turbulent political times before they decided to return to SA. In Grahamstown, Jos studiedgeology while being supported by Brenda, and graduated BSc (Hons) with distinction in 1961.

In 1963 Jos chose the option of education as a future career and joined the staff of the Witwatersrand Technical Collegeas lecturer in geology and surveying. This has been his life and enthusiasm ever since.

Jos was passionate about vocationally based technical engineering education in particular. He was a competent practicaland theoretical geologist, gemmologist, and surveyor of the highest order. He was the Head of the School of Mines from 1980for more than 20 years, after which in semi-retirement he continued active engagement up until 2014. He was a highlyrespected academic, a prolific author, and researcher whose works will no doubt continue to influence the world. He was welltravelled and shared most of his travels with Brenda by his side.

Jos was an honorary Life Fellow of the SAIMM, which recognized his service to the Institute from 1980 to his ‘firstretirement’ in 1990. He never gave up his interest in education, with courses being offered up until late 2014 when he firstbecame ill.

During his tenure at Technikon Witwatersrand he carried out a specialist study on the Pilanesberg on rare earth deposits,culminating in a PhD award in 1974. Again, the support of Brenda was always recognized by Jos. He remained anacknowledged expert in this field throughout his life, and has left comprehensive materials to support future possible miningventures.

Such activities not being enough to satisfy Jos, he set to writing technical books for publication, which included:

� History of Mining and Metallurgy at Technikon Witwatersrand (1980)� SA Geology for Mining, Metallurgical and Hydrological and Civil Engineering (1977) (11 editions)� Technikon Witwatersrand – A History 1925–2000� Symetrix Dynamic 3D Model Maker (2012)� Symmetrical Polyhedra (2008)� Lurie and Ponelat’s Catalogue of Symmetrical Polyhedra (2008)� The Pilanesburg Alkaline Complex, Geology, Geochemistry and Economic Potential (2008).

He had more than 15 published technical papers and reports as well as many conference presentations across the world,ranging from hydrology in Kenya to microscopic mineral analysis, a new football (just prior to the SA World Cup activities in2010), and synthetic gems.

The record (unpublished) of his ‘Exciting Life’ will be a tribute to him and is a fitting end to not only an exciting life, buta life of contribution and success and an inspiration to those that follow him.

He is survived by his wife Brenda and their son Ross and daughter Wendy.

P. Knottenbelt

ObituaryJoseph Lurie

The Journal of The Southern African Institute of Mining and Metallurgy MAY 2015 ▲ix

ELECTRA MINING BOTSWANA1–3 SEPTEMBER 2015

Industry and Government support for Electra Mining Botswana

Leading local and international suppliers to the industrial and mining sectors will gain valuable exposure to the southern African mining market at Electra Mining Botswana 2015. Exhibitors at the show will have the opportunity to network with key decision-makers from Botswana’s growing industrial and mining sectors,

position their company brand, grow their client base, and launch new products and services directly to their target market.

Botswana’s thriving mining and industrial sectors makes it the natural choice for companies such as Joy Global(Africa), Verder Pumps, Filtration Africa, Air Liquide Botswana, Franklin Electric, Becker Mining South Africa,Jachris Hose and Couplings, Schnieder Electric South Africa, Charnaud, Tubestone, and AC/DC Dynamics, amongmany others, to exhibit at Electra Mining Botswana.

In addition to leading companies participating at the show, the Government of Botwsana is also backing theevent.

‘We’re proud to announce that the Botswana Ministry of Minerals, Energy and Water Resources has confirmed its endorsement of Electra Mining Botswana,’ says Gary Corin, Managing Director of Specialised Exhibitions Montgomery, organizers of the show. ‘The Ministry has expressed the importance of the exhibition to the mining,construction, and power generation industry because it brings major technology partners and suppliers together.’

Onthusitse H. Melaetsa, Permanent Secretary at the Ministry of Minerals, Energy and Water, says that ‘the eventwill assist the mining industry to strengthen relationships, source and view the latest technologies available to thesector as well as source project finance and funding.’

‘This event fills a vacuum that existed in Botswana,’ says Mmetla Masire, Coordinator Relocation &Opportunities at the Ministry of Minerals, Energy and Water. ‘It now brings a show that is dedicated and specializedin the mining, power generation, and related industries and I would highly recommend it to potential exhibitors andthose wanting to see the latest offerings in the market.’

Also endorsing Electra Mining Botswana is Botswana Chamber of Mines CEO Charles Siwawa, who says that‘the objective of the show is to assist the mining industry in strengthening stakeholder relationships. In addition tothe exhibits there will be interesting seminars taking place concurrently during the three-day exhibition.’

The exhibitor profile will include mining and related products, industrial engineering and manufacturing,general engineering and manufacturing, electrical engineering and power generation, materials handling, safety,health, and environment, and construction. The show will also feature exciting free-to-attend seminars that willprovide a wealth of information and interactive discussion.

‘The exhibition embraces mining, industrial, power generation, and construction and will play a vital role ingrowing Botswana’s already thriving mining and industrial economy, bringing related industries together at one location for visitor and exhibitor convenience,’ concludes Corin.

Taking place from 1–3 September at the Gaborone Fair Grounds in Gaborone, the power hub of Botswana, the exhibition is organized through a joint venture between Soapbox Communications, a local Botswana company, andSpecialised Exhibitions Montgomery, a South African company with over 42 years of exhibition experience.

For more information, contact Charlene Hefer, Portfolio Director: Mining and Industrial, Specialised ExhibitionsMontgomery, at email charleneh@specialised.com

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PAPERS IN THIS EDITIONThese papers have been refereed and edited according to internationally accepted standards and are accredited

for rating purposes by the South African Department of Higher Education and Training

These papers will be available on the SAIMM websitehttp://www.saimm.co.za

Pyrometallurgy PapersRepresentation of coal and coal derivatives in process modellingby J.A. Theron and E. le Roux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339This paper provides guidelines on mass and energy balance modelling involving coal and coal derivatives. Several potential pitfalls of such modelling processes are outlined. It is recommended that an ‘enthalpy correction value’ is incorporated in energy balances involving combustion, devolatilization, or conversion of coal and coal derivatives.

Sonic injection into a PGM Peirce-Smith converter: CFD modelling and industrial trials by D.K.Chibwe, G. Akdogan, G.A. Bezuidenhout, J.P.T. Kapusta, S. Bradshaw, and J.J. Eksteen . . . . . . . . . . . . . . . . . . . 349Western Platinum conducted a numerical assessment, followed by a full-scale industrial evaluation, of implementing sonic injection into a Peirce-Smith converter as part of its operational improvement and energy reduction initiatives. The paper discusses the key findings in understanding plume extension, velocity distribution, shear wall stress analysis, and phase distribution characteristics in the system.

Physical and numerical modelling of a four-strand steelmaking tundish using flow analysis of different configurationsby J.H. Cloete, G. Akdogan, S.M. Bradshaw, and D.K. Chibwe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355The flow behaviour in a four-strand tundish was investigated using a ½-scale water model as well as numerical modelling. The numerical and physical model were used to characterize the residence time distribution and calculate the properties pertaining to the tundish flow under three different tundish configurations.

Modelling of fluid flow phenomena in Peirce-Smith copper converters and analysis of combined blowing conceptby D.K. Chibwe, G. Akdogan, P. Taskinen, and J.J. Eksteen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363This investigation consists of a numerical and physical modelling exercise on flow patterns, mixing, solid-liquid mass transfer, and slag-matte phase distribution in a 0.2-scale cold model of an industrial Peirce-Smith converter. Mass transfer measurements indicated that the fluid flow in the converter is stratified. Blowing configuration and slag volume have significant effects on mixing propagation, wave formation, and splashing. A combined blowing configuration using top-lance and lateral nozzles is proposed to increase process efficiency.

The recovery of platinum group metals from low-grade concentrates to an iron alloy using silicon carbide as reductant by W. Malan, G. Akdogan, S. Bradshaw, and G.A. Bezuidenhout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375Base metals and PGMs can be recovered in an iron alloy by silicon carbide reduction of a low-grade platinum concentrate with converter slag additions. Integrating such a process into the matte-based collection process could be considered as an alternative in the future smelting of low-grade UG2 concentrates.

Value-in-use model for chlorination of titania feedstocksby S. Maharajh, J. Muller, and J.H. Zietsman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385A techno-economic model was developed to describe the chlorination process for TiO2 pigment production and estimate the process variables at steady state. The model can be used to quantify the effects of using different feedstocks.

Interaction of dust with the DC plasma arc – a computational modelling investigationby Q.G. Reynolds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395A continuum model was developed for dust transport in the arc region of DC plasma arc furnaces. Qualitative case studies produced a number of practical suggestions for furnace operation, including increased dust capture by the bath when feed ports are located closer to the electrode, and the possible effects of feed segregation in the furnace freeboard based on dust particle size and density.

A finite difference model of the iron ore sinter processby J. Muller, T.L. de Vries, B.A. Dippenaar, and J.C. Vreugdenburg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409A model of the iron ore sintering process has been developed that uses the finite difference method (FDM) to evaluate the behaviour of specific iron ore sinter feed mixtures. The model aims to predict sinter quality, including chemical quality and physical strength, as well as key sinter process performance parameters such as production and fuel consumption rates.

Modelling and optimization of a rotary kiln direct reduction processby H.P. Kritzinger and T.C. Kingsley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419This paper illustrates the application of numerical optimization techniques in combination with a rotary kiln model in the interrogation of a generic iron-ore reduction process. The fundamental modelling concepts are explained, followed by a description of the optimization approach.

PAPERS IN THIS EDITIONThese papers have been refereed and edited according to internationally accepted standards and are accredited

for rating purposes by the South African Department of Higher Education and Training

These papers will be available on the SAIMM websitehttp://www.saimm.co.za

General PapersEquipment selection based on the AHP and Yager’s methodby M. Yavuz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425Loader equipment for the Aegean Lignite Colliery was selected by using both the analytic hierarchy process and Yager’s method. A sensitivity analysis was applied for each method in order to see how the selection criteria affect the final decision. The advantages and disadvantages encountered during the application of each decision-making process are presented.

Pre-sink shaft safety analysis using wireline geophysicsby N. Andersen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435This paper outlines how rock mass characteristics, in-situ rock stress, rock strength, hydrological characteristics, and structural parameters can be determined using wireline logging of a vertical, pre-sink diamond drill-hole. The ‘stick plot’ method is introduced as a reporting method that combines all geotechnical parameters applicable to the stability of the proposed shaft into an easily readable format.

Hydraulic support instability mechanism and its control in a fully-mechanized steep coal seam working face with large mining heightby Y. Yuan, S.H. Tu, F.T. Wang, X.G. Zhang, and B. Li. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441This paper focuses on the analysis of hydraulic support instability (HSI) types, the key parameters and techniques to control HSI in fully-mechanized mining faces with large mining height, the establishment of a model of HSI, and a multi-parameter sensitivity mechanical model of different HSI types on a longwall coal mining face.

Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analysesby H. Jang, E. Topal, and Y. Kawamura. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449Models for the prediction of unplanned dilution and ore loss are established using multiple linear and nonlinear regression analyses, as well as an artificial neural network (ANN) method. It is shown that the proposed ANN model can be directly used as a practical tool to predict unplanned dilution and ore loss, which will not only enhance productivity, but also be beneficial for stope planning and design.

Numerical simulation of multiphase flow in a Vanyukov furnaceby H.L. Zhang, C.Q. Zhou, W.U. Bing, and Y.M. Chen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457A multiphase model of the flow in a Vanyukov furnace was built using computational fluid dynamics (CFD) software and solved with the volume of fluid (VOF) and k - ε model. The proposed model can be used to predict the multi-phase movement, slag/air fluctuation, vortex formation, and the effects of changes to the structural and operational parameters, and provides a method for optimization of the furnace structure and operating conditions.

IntroductionCoal is possibly the most heterogeneoussubstance used in the minerals industry,especially that sector involving processing athigh temperatures. Coal derivatives includecoke, char, tar, pitch, and coal gas which areimportant commodities in industry.

Coal is used extensively in themetallurgical industry for widely different

purposes, e.g. electrical power generation,reductant manufacturing (coke and char), aswell as gasification, which allows the value ofcoal to be extended to a wide range of chemicalproducts. Pulverized coal injected into the blastfurnace (for the purpose of decreasing cokeconsumption) is subjected to significantlydifferent conditions than pulverized coal usedfor power generation. Reductants such asanthracite fed to an open arc smelting processare subjected to rapid rates of heating. Lumpcoal fed into the Corex (Institute for IndustrialProductivity, 2012a), smelt-reduction, orrotary kiln direct reduction (DR) processes(Institute for Industrial Productivity, 2012b) issubjected to reducing conditions as well as arelatively rapid rate of heating.

It is therefore important to understand thephysiochemical properties of coal as they relateto different applications in order to extractmaximum value from available coal resources.Process modelling plays an important role inextracting this value through a detailedunderstanding of the quality and physio-chemical properties of coal and its derivatives.This paper highlights some of the relevantissues and potential pitfalls in the modelling ofcoal, focusing on the application of proximateand ultimate analysis of coal, with specificreference to oxygen content, and the effect ofdifferent assumptions regarding its represen-tation on the calculated calorific value (CV).

Current evaluation methods for coalinclude proximate analysis, total sulphur, andCV. Proximate analysis includes moisture,volatiles, ash, and fixed carbon yields. (Notethe preference for the word ‘yields’ in thiscontext, as opposed to ‘contents’. That isbecause coal actually contains minerals andnot ash. Ash is the remnants of minerals after

Representation of coal and coalderivatives in process modellingby J.A. Theron* and E. le Roux*

SynopsisThis paper provides guidelines on performing mass and energy balancemodelling involving coal and coal derivatives. Usually, the inputs to apyrometallurgical process would be specified in terms of elements andcompounds. Reliable thermochemical data is more widely available forspecies involving uniquely defined, relatively smaller molecules. However, inthe case of coal, the molecules are extremely large and not uniquely defined.Consequently, modelling processes involving coal and its derivatives involveseveral potential pitfalls. These are outlined in the present paper.

It was found that coal proximate analysis should not be regarded asabsolute; it could vary with several parameters, including heating rate. Formodelling, the use of ultimate analyses should be considered a preferableoption to proximate analyses, where ‘fixed carbon’ and ‘volatiles’ are notdefined in terms of chemical composition. Significant errors could beincurred if the larger molecules are neglected during calculation of thecalorific value (CV) of coal gas (the gas liberated when coal is heated in theabsence of oxygen).

For elemental analysis determination, the oxygen content (which iscalculated by balance) should be checked to ensure it is within the expectedrange. For representation of sulphur in coal, one should avoid double-counting due to SO3 in the ash analysis.

Potentially, oxygen in coal could be represented as O2, H2O, CO, or CO2.However, use of some of these species without considering the experi-mentally determined gross CV leads to significant errors in the energybalance. If coal enthalpy is calculated from elemental analyses withoutcorrection, representation of coal oxygen as H2O(l) gives reasonableaccuracy. Coal volatiles could be represented by a complex mixture ofcompounds, even using different oxygen-containing species than these four,provided the enthalpy is corrected.

It is recommended that an ‘enthalpy correction value’ be incorporated inenergy balances involving combustion, devolatilization, or conversion ofcoal and coal derivatives, e.g. coke, char, or tar. That would imply thatproximate analysis, elemental analysis, as well as the gross CV would berequired for all solid or liquid coal-derived substances being modelled. Noother correction due to carbon being present in a form other than graphiteshould be used, as that would imply double-counting some effects.

Keywordscoal properties, process modelling, proximate analysis, energy balance,enthalpy.

* Exxaro Resources Limited, South Africa.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. This paperwas first presented at the, PyrometallurgicalModelling Principles and Practices, 4–5 August2014, Emperors Palace Hotel Casino ConventionResort, Johannesburg.

339The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

ISSN:2411-9717/2015/v115/n5/a1http://dx.doi.org/10.17159/2411-9717/2015/v115n5a1

Representation of coal and coal derivatives in process modelling

complete oxidation of the coal. Most often these analyses arenot adequate to fully describe the behaviour of the coal in thespecific process. Technologists, especially in the fields ofpower generation and coke-making, properly consider theresults of a number of additional tests, e.g. elementalanalyses, ash analysis, ash fusion temperature, and coalpetrography. Coke-makers consider coal plasticity tests (e.g.Gieseler fluidity and dilatation), and those involved in powergeneration consider coal reactivity, especially from drop tubeoven results.

The use of mathematical modelling in metallurgicaland chemical processesMass balance modelling is simply based on the law of conser-vation of mass. Although the concept is simple, there arepotential pitfalls associated with these calculations, especiallyfor coal. The same applies to energy balances, which arebased on the enthalpy function. Considerable time haselapsed since the initial development of the field of thermody-namics and introduction of, for example, the concept of Gibbsfree energy (Bodner Group, 2014). Nowadays, exploiting theconcept of Gibbs free energy minimization is easier thanpreviously, due to the availability of useful databases,software, and computers.

Usually one would specify an input to a pyrometallurgicalprocess in terms of elements and compounds. However, inthe case of coal the molecules are extremely large, and notuniquely defined. Reliable thermochemical data is morewidely available for species involving uniquely defined,relatively simpler molecules.

Nevertheless, it is essential to understand the limitationsof mathematical modelling. The quality of the output dependson the following:

➤ Accuracy of inputs➤ Understanding of the process➤ Validity of assumptions➤ Intent of the model➤ Inherent limitations of modelling➤ The level of skill of the person undertaking the task,

including the use of checks and balances➤ The time allowed for execution of the task, which if not

enough, could force the person to make unjustifiedassumptions.

Proximate analysis

Moisture contentPreparation of a new coal sample would include air-dryingthe coal at ambient temperatures or at elevated temperaturesnot exceeding 40°C (SANS 589:2009, 2009). The apparentlydry coal obtained is referred to as ‘air-dry’. The mass lossfrom wet coal to ‘air-dry’ coal is referred to as ‘free moisturecontent’. A sample of air-dry coal, after appropriate samplepreparation techniques (including milling), is heated to atemperature of between 105ºC and 110ºC, and the associatedmass loss is referred to as ‘residual or inherent moisture’.The sample is then referred to as ‘dry’ or ‘absolutely dry’.Total moisture content of the coal sample is calculated asfollows:

%Total moisture = %Free moisture + %Inherent moisture [1]

Some coals liberate additional moisture (water of crystal-lization) when heated to temperatures higher than about105ºC. This moisture reports as part of the volatile content. Inthe case of highly porous coal derivatives such as coke or char,the drying method prescribes temperatures between 120°C and200°C, with additional care to ensure that no further mass lossoccurs before recording the mass (SANS 579:2005, 2005).

Proximate analysis calculationProximate analysis is usually carried out on an air-dry coalsample. Results include inherent moisture, volatiles, and ashyields, while the balance is allocated to fixed carbon, with allvalues expressed on an air-dry basis, as follows (SANS17246:2011, 2011):

%Fixed C = 100% - (%Moisture + %Volatiles + %Ash) [2]

Note that sulphur remaining in the residue would report to‘fixed carbon’. The values on an air-dry basis could beconverted to an absolute dry base, e.g. by applying thefollowing conversion:

%Fixed C(dry) = %Fixed C(air dry) / (1 – (% Inherentmoisture)/100) [3]

During modelling and reporting of modelled results, careshould be exercised to specify all analyses as ‘wet as-received’,‘air-dry’, or ‘dry’ and to use the appropriate conversion factorsto avoid errors.

Coal volatilesDuring determination of the volatile content of coal, a smallmilled sample is inserted in a crucible and covered by a lid. Thecrucible is loaded into a muffle furnace at 900ºC for a durationof 7 minutes (SANS 50:2011, 2011). The mass lost, aftersubtracting the moisture content, is referred to as ‘volatilescontent’ or volatiles yield.

For determination of ash yield, the coal is subjected tooxidizing conditions. The temperature is limited to 850°C tominimize the loss of volatile compounds such as K2O andNa2O.

According to Rosenqvist (1974), ‘The proximate analysisgives the percentage of “moisture”, “volatile matter”, “fixedcarbon” and “ash”. Each of these is determined bystandardized procedures, and different values would beobtained if different procedures were used.’ Procedures werestandardized by ISO, ASTM, and other organizations.

Some operators who are less familiar with coal could treatthe results from proximate analyses as if they were absolute.However, considering the quote from Rosenqvist andobserving the extent to which actual processes differ from theprocedures for proximate analysis, behaviour not in line withproximate analysis should actually be expected. Therefore itshould not be surprising if the actual performance of coal, e.g.during charring or coke-making, deviates from what would beexpected if the characteristic were calculated on the basis of theproximate analysis.

One example of actual results deviating from proximateanalysis data is the observation that rapid heating tends toincrease the volatile yield. Most noticeably, that decreases theyield of fixed carbon (Niksa, 1995).

Composition and representation of volatilesCoal volatiles are partly organic and partly inorganic in origin.Water vapour and carbon dioxide from clay and carbonate

▲

340 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

minerals are the main inorganic volatiles. Some effects ofinorganic volatiles are as follows:

➤ They do not contribute combustible species that couldincrease the CV

➤ Their liberation could be associated with endothermicreactions, which would neutralize part of theexothermic reaction heat from combustion

➤ They could adversely affect ignition of the coal.Not all the water from clays is liberated at approximately

105ºC, as the remainder (crystal water) is bound in chemicalcompounds that decompose at higher temperatures. Thesevolatiles of inorganic origin are referred to as ‘inert volatiles’(Porter and Ovitz, 2014). Coal volatiles could also besubjected to secondary reactions after initial liberation – forexample, in the case of tar from slot coke-making ovens, inwhich thermal cracking occurred during contact with surfacesat a higher temperature than that of initial release. Thermalcracking (Chiu and Hong, 1983) involves the formation ofpyrolytic carbon, smaller molecules (such as H2), andmodified hydrocarbon compounds. Pyrolytic carbon isdeposited in the coke pores during carbonization, whichincreases the yield of fixed carbon significantly (Chiu andHong, 1983). If the process being modelled involves thermalcracking, appropriate assumptions would be required, whichshould be based on evidence from experimental work or plantobservations.

Condensable hydrocarbons in coal volatiles represent amodelling challenge. Condensable hydrocarbons containnumerous compounds, for which detailed analyses areseldom available. Tars in particular contain numerouscompounds with very large molecular structures and whichare thermally unstable. Representation of tar with a simplerspecies or mixture of species of smaller molecular mass withwell-known thermodynamic data is advantageous. In thisregard it could be mentioned that benzene (C6H6) has a highcarbon content of 92.3%, in the same order of magnitudethan typical tar from slot coke-making ovens. For quickcalculations where approximate results are required, benzenecould be used to represent tar. Light oils, also referred to as

BTX, contain benzene, toluene, and xylene. As benzenepredominates in the composition of these light oils, it couldalso represent this group of compounds where approximateresults are required (Powell, 1945).

Representation of volatiles for coal gas CVcalculation

The large molecules of condensable hydrocarbons do notmake a major contribution to the volume of coal gas.However, they do make a significant contribution to both theCV and carbon content of the gas. According to Powell,typical coke oven gas from a by-product coke oven containsabout 0.65% by volume light oils (after tar removal, butbefore BTX removal) (Powell, 1945).

In Table I, composition by volume of coal gas from atypical by-products coke-making oven, given by Powell, ispresented, together with the percentage contribution of eachindividual component of that gas analysis to the calculatedCV of the gas mixture (23.93 MJ/Nm3 in this instance). Ifonly the contributions of hydrogen, carbon monoxide, andmethane are taken into account, the calculated gas CV wouldbe only 82.5% of the accurate value, assuming that the givencalculated value is the accurate one. Light oils, whichrepresent only 0.65% of the volume of the gas, contributealmost 4% of the gas CV.

Similarly, in Table II, composition by volume of coal gasfrom low-temperature carbonization (Powell, 1945) ispresented. This gas has an even higher CV of 27.5 MJ/Nm3,compared to 23.9 MJ/Nm3 for by-product coke oven gas.Similar to the case of low-temperature carbonization,hydrogen, carbon monoxide, and methane contribute only67.8% of the gross CV. The contributions, especially bysubstances containing at least two carbon atoms, are largeand omission of them would results in significant errors. Inthe case of low-temperature carbonization gas, light oilsrepresent about 1.5% of the gas volume; however, theycontribute almost 8% of the gas CV.

Representation of coal and coal derivatives in process modelling

341The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

Table I

Coal gas composition from by-product coke oven and its contribution to gross calorific value (Powell, 1945)

Species in unwashed coke oven gas from by-products plant Volume % of % Contribution % Combined gas species to gross CV contribution to gross CV

Hydrogen H2 56.70 30.24Methane CH4 29.60 49.17Nitrogen N2 0.90 0.00 82.5Carbon monoxide CO 5.70 3.05Carbon dioxide CO2 1.70 0.00Ethylene C2H4 2.45 6.45Ethane C2H6 1.28 3.72Propylene C3H6 0.34 1.31Propane C3H8 0.80 0.33Butylene C4H8 0.16 0.81 13.59Butane C4H10 0.02 0.11Acetylene C2H2 0.05 0.12Hydrogen sulphide H2S 0.70 0.73

Oxygen O2 0.00 0.00 0.00Light oils C6H6* 0.65 3.96 3.96

Total percentage 100.33 100.00 100.00Gas CV including all components: 23.93 MJ/Nm3

*: Benzine represents light oils.

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Representation of coal and coal derivatives in process modelling

Liberation of volatiles at high temperaturesThe residue from the proximate analysis test in which‘volatiles’ were determined is by no means free from volatileelements like H, O, N, and S. Most of the remaining volatileelements are liberated only at temperatures higher than900ºC, and even exceeding 2000ºC. In a Lurgi packed-bedgasifier sampling campaign, Bunt and Waanders found that10% of the coal nitrogen, as well as 0.75% of coal hydrogen,remained in the residue after reaching a temperature of atleast 1000°C (Bunt and Waanders, 2008).

For a detailed mass balance on a process that producesreductants, or even one where coal is converted to char forconsumption in the process itself, such as the DR kiln, theextent of liberation of H, O, N, and S, as well as theremaining carbon that is associated with the volatiles, shouldbe carefully considered.

If coke or char is heated to very high temperatures(higher than e.g. 1500ºC) in the absence of oxygen to effectgraphitization or to drive off the remaining H, N, S, and O,the ash composition could also be expected to change.Volatile oxides (such as K2O and Na2O) would be driven offto a large extent. Most remaining ash oxides could react withcarbon at these temperatures, forming volatile oxides such asSiO(g) and Al2O(g). A notable exception to this behaviour isiron, which does not readily form a volatile reaction product.Under such conditions, iron would exist in the reductant asmetallic iron or perhaps FeS (instead of an oxide of iron).That explains why the ash composition of graphite has asignificant or dominant Fe2O3 content (during determinationof the ash composition, iron or FeS would oxidize to formFe2O3). Another effect of these high-temperature reactions isthe consumption of some carbon in forming these volatileoxides.

Ultimate (elemental) analysisThe char residue from the proximate analysis test fordetermination of ‘volatiles’ is by no means free from volatileelements like H, O, and N. Therefore ‘fixed carbon’ is notdefined in terms of its elemental composition. As we haveseen, ‘volatiles’ is also not defined in terms of elementalcomposition.

For mass and energy balance studies it is advisable tobase calculations on the ultimate analysis instead of theproximate analysis of the coal. Reasons for this include thefollowing:

➤ The percentage fixed carbon could be a function ofcertain parameters such as heating rate, gas pressure,and secondary reactions involving the volatiles

➤ The composition of ‘fixed carbon’ is not defined in anelemental sense, as it could contain residual sulphur,hydrogen, nitrogen, and oxygen

➤ The composition of ‘volatile matter’ is also not definedin an elemental sense, as it could contain numerouscompounds in varying quantities.

Ultimate analysis involves determination of the carbon,hydrogen, nitrogen, and total sulphur content of air-dry coal.Ash (from the proximate analysis) is also used in theformula. Oxygen is determined as the balance of theelemental analysis, as follows:

%O = 100% - % Inherent moisture - %Ash - %C - %H -%N - %S [4]

Note that neither oxygen nor hydrogen contained in theinherent moisture, nor in the ash-forming oxides, form partof the reported coal oxygen content (ASTM Designation D 3,n.d.).

For elemental analysis done on a Leco apparatus, it isimportant to ensure the apparatus is calibrated with a sample

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342 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table II

Coal gas composition from low-temperature carbonization and its contribution to gross calorific value (Powell,1945)

Species in unwashed coke oven gas from by-products plant Volume % of % Contribution % Combined gas species to gross CV contribution to gross CV

Hydrogen H2 33.5 15.5Methane CH4 32.0 46.8Nitrogen N2 1.1 0.0 67.8Carbon monoxide CO 11.7 5.4Carbon dioxide CO2 11.0 0.0Ethylene C2H4 0.92 2.11Ethane C2H6 3.80 9.61Dimethylethylene C4H8 0.51 2.25(3 isomers)Propylene C3H6 0.87 2.90Propane C3H8 1.33 4.79 24.31Butane C4H10 0.28 1.31Isobutane C4H10 0.09 0.421,3–Butadiene C4H6 0.002 0.01Acetylene C2H2 0.00 0.00Hydrogen sulphide H2S 1.00 0.91

Oxygen O2 0.00 0.00 0.00Light oils C6H6* 1.498 7.94 7.94

Total percentage 100.33 100.00 100.00Gas CV including all components: 27.53 MJ/Nm3

*: Benzine represents light oils.

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that more or less resembles the sample being tested. If, forexample, an anthracite sample is analysed while abituminous coal reference sample is used, significant errorcould occur. Such an error is often evident from the oxygencontent (which is calculated by difference), which cansometimes be reported as a negative value. Figure 1 showstypical oxygen contents of South African coals plus fourimported hard coking coals (Schoeman and Boshoff, 1996),and Table III contains the proximate and ultimate analyses,as well as the calorific values, of this set of data. The lowestoxygen contents were found for anthracites, ranging fromzero to 2%. Note, however, that oxygen contents up to about4% are not uncommon for anthracitic coals. Hard cokingcoals generally have relatively high rank and high vitrinitecontent (Falcon, 2013) and low oxygen contents , of theorder of 2–4%.

Other South African bituminous coals have oxygencontents of generally between 7% and 11%. High-inertinitecoals from the Free State have relatively high oxygencontents of about 9%, and are also noteworthy due to highash (a relatively high ash content implies a relatively lowcarbonaceous content).

If the ultimate analysis is used to represent coal or coalderivatives entering a process, additional care would have tobe taken, especially regarding sulphur and oxygen.

Representation of sulphur in coalAs mentioned previously, ‘fixed carbon’ would also includesulphur reporting in the residue of the proximate analysistest. Under the oxidizing conditions in a pulverized fuelboiler, sulphur could be oxidized to SO2, which generallyinvolve exothermic reactions. However, under the reducingconditions prevailing in the blast furnace or Corex processes,sulphur cannot be oxidized to SO2, but forms H2S instead.Consequently, oxidation of that part of the ‘fixed carbon’cannot contribute to any exothermic heat generated for thoseprocesses. Sulphur in coal can exist in pyritic, sulphatic, andorganic forms. Under oxidizing conditions, organic andpyritic sulphur would oxidize exothermally to SO2. However

sulphatic sulphur is already in an oxidized form. Sulphaticsulphur does not constitute a high percentage of the sulphurin South African coals (Schoeman and Boshoff, 1996).

It is advisable to use the total sulphur content from theelemental analysis as input sulphur into a model. Using theSO3 from the ash analysis could imply that part of thesulphur would be double-counted. On the other hand,omitting SO3 would cause missing mass in the mass balance,which is probably preferable to double-counting sulphur. SO3in the ash is particularly related to calcium, which combinessulphur as CaSO4. Calcium sulphate forms during the ashingof the coal for ash composition determination; however, itwould not necessarily form in any given process. Generally,CaSO4 would form at temperatures of around 900°C underoxidizing conditions.

If coal is devolatilized instead of combusted, reducingconditions prevail and pyrite would decompose at temper-atures in the range 300–600ºC (Rausch, 1975) according to:

FeS2 → FeS + ½ S2(g) [5]

This would affect the analysis of forms of sulphur, a topicthat is beyond the scope of this paper.

Presumably, basing a representation of coal on themineralogical analysis would be the ultimate goal. Thepercentage of mineral matter in the coal could be linked byapproximation with the ash, as well as certain otherparameters, by the King-Maries-Crossley formula (Karr,1978), provided those parameters are available:

MM = 1.09A +0.5Sp + 0.84CO2 – 1.1SO3 + 0.5Cl [6]

whereMM is mineral matter (% by mass)A is ash content (% by mass)Sp is pyritic sulphur in the coal (% by mass)CO2 is carbon dioxide that originates from the mineralmatter in the coal (%)SO3 is sulphur trioxide in the ash (%)Cl is the chlorine in the coal (%).

Representation of oxygen in coalSouth African coals have too high an oxygen content to beignored for all calculations involving elemental analysis. Inprinciple, representing the oxygen as O2, can be considered,or replacing some of the hydrogen or carbon of the coal toform compounds such as H2O, CO, or CO2. According toPowell (1945), free oxygen is present in coal gas only as aresult of air leakage or introduction of air after the gas hasleft the carbonization chamber. Relatively small amounts ofoxygen could also be associated with air originally present inthe voidage between the coal particles plus that in the poresor adsorbed within the coal during charging. Therefore, fromthese considerations, assuming the coal oxygen to be O2seems to be the least accurate assumption.

The Dulong formula (Rosenqvist, 1974) allowsestimation of the gross CV of coal as calculated from theelemental analysis:

NCP = 81C + 340(H – O/8) + 22S [7]

whereNCP is gross CV in kCal/kg (1 kCal = 4.184 kJ)C, H, O, and S are the mass percentages of these elementsin an absolute dry coal sample.

Representation of coal and coal derivatives in process modelling

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 343 ▲

Figure 1 – Oxygen contents of South-African coals and four importedhard coking coals (Schoeman and Boshoff, 1996)

Representation of coal and coal derivatives in process modelling

The formula implies that oxygen decreases the contri-bution of hydrogen to the gross CV. This suggests thatoxygen is combined with hydrogen in coal, probably asH2O(l), or at least the thermal effect thereof seems to be anacceptable approximation.

Several proposed molecular structures of coal have beenpublished by various authors (Given, 1960; Wiser, 1973). Ifwe consider a proposed structure of vitrinite, we observe thatmost oxygen is present in C–O–H, less in C–O–C bonds, andsome C=O bonds are also found (Shinn, 1984). From such astructure oxygen would not be liberated as O2.

From the vitrinite structure in Figure 2, the coal wouldpreferably be represented by a small number of species withless complex molecular structure and known thermodynamic

properties. Even studying the type of bonds does not helpmuch in suggesting which species would best representoxygen in the coal. However, whatever species are chosen,the mass balance should be satisfied (it should reflect theelemental composition of the coal) and the energy balanceshould be satisfied. Potentially one could choose any one ofO2, H2O, CO, or CO2.

The effect of the choice of a compound to representoxygen in coal on the results obtained is illustrated by thesimple hypothetical ‘process’ in Figure 3. Table IV gives theassumed composition of the hypothetical coal used in thisstudy. The hypothetical coal is simplified by assuming it isdry and the ash has only one constituent: SiO2. One kilogramof this coal is oxidized with 2.25 kg oxygen to yield an off-

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344 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table III

Proximate and ultimate analyses and calorific values of South African coals and four imported hard coking coals(Schoeman and Boshoff, 1996)

Proximate analysis (air-dry base), mass% Gross CV, Ultimate analysis (air-dry base), mass %Moisure Volatile Ash Fixed (air-dry base), C H N S (total) O

matter carbon MJ/kg

A 21 6.0 12.1 79.8 30.31 79.56 2.64 1.96 0.81 0.83B 2.4 7.7 15.7 74.2 29.17 75.07 2.85 1.56 0.43 1.99C 1.7 9.1 14.5 74.7 29.78 76.42 3.18 1.93 0.86 1.41D 1.8 7.7 11.6 78.9 30.44 79.34 3.20 2.10 1.44 0.52E 1.9 5.3 8.1 84.7 32.07 83.66 2.84 1.80 1.17 0.53F 1.6 5.4 7.0 86.0 32.46 85.09 2.87 1.80 0.88 0.76G 1.5 31.0 11.7 55.8 30.53 74.61 4.82 1.95 1.44 3.98H 1.1 21.8 13.1 64.0 30.77 76.07 4.38 1.92 0.66 2.77I 0.7 24.0 8.8 66.5 32.94 80.99 5.08 2.08 0.58 1.77J 1.1 30.8 4.5 63.6 33.54 82.30 5.35 1.09 1.96 3.70K 0.7 27.8 9.7 61.8 32.45 79.64 5.11 1.62 1.05 2.17L 0.7 22.9 9.5 66.9 32.51 80.37 4.89 1.78 0.56 2.21M 0.8 22.0 16.8 60.4 30.38 73.33 4.40 1.67 0.63 2.37N 5.9 19.3 37.4 37.4 16.27 43.76 2.66 1.07 0.41 8.80O 6.8 20.5 38.7 34.0 15.59 41.12 2.71 1.00 0.50 9.17P 2.2 26.4 34.6 36.8 20.43 50.63 3.56 1.17 0.82 7.02Q 2.9 24.9 14.8 57.4 27.30 70.10 3.78 1.40 0.71 6.31R 2.9 24.2 14.7 58.2 27.40 71.90 3.86 1.50 0.67 4.47S 2.4 23.4 11.9 62.3 27.95 72.38 3.90 1.63 0.66 7.13T 4.2 23.6 25.1 47.1 21.94 57.91 3.25 1.42 0.96 7.16U 4.9 22.6 26.1 46.4 20.87 54.92 3.06 1.29 0.68 9.05V 4.5 24.3 21.9 49.3 22.23 59.07 3.30 1.33 1.12 8.78W 3.2 26.8 12.4 57.6 27.98 71.31 4.27 1.73 0.78 6.31X 3.1 25.9 13.5 57.5 27.89 70.79 4.30 1.75 0.49 6.07Y 2.6 31.7 10.5 55.2 29.56 72.69 4.81 1.85 0.52 7.03Z 2.7 23.9 14.6 58.8 27.39 70.17 4.07 1.55 0.57 6.34AA 2.4 29.2 7.2 61.2 30.69 77.03 4.62 1.82 0.41 6.52AB 2.0 27.0 15.3 55.7 27.47 69.42 4.24 1.67 0.30 7.07AC 2.0 24.9 17.1 56.0 26.51 66.77 4.05 1.60 1.08 7.40AD 2.9 27.2 17.7 52.2 25.94 66.40 4.00 1.71 1.58 5.71AF 2.6 27.1 15.2 55.1 27.16 68.64 4.10 1.76 0.65 7.05AG 4.3 25.6 19.5 50.6 23.96 61.78 3.72 1.39 0.95 8.36AH 2.2 20.8 32.3 44.7 20.86 53.77 3.09 1.27 0.90 6.47AI 3.6 27.0 14.6 56.4 26.68 67.78 4.11 1.62 0.39 7.90AJ 3.0 25.4 13.3 58.3 27.44 69.20 4.06 1.55 0.58 8.31AK 4.0 24.1 18.4 53.5 23.89 62.20 3.33 1.40 0.83 9.84AL 3.5 22.3 13.1 61.1 27.06 70.29 3.80 1.64 0.84 6.83AM 2.9 18.9 14.9 63.3 27.03 70.09 3.62 1.69 0.63 6.17AN 4.8 24.2 25.3 45.7 21.83 56.19 3.42 1.30 1.19 7.80AO 2.7 26.6 15.1 55.6 27.65 69.29 4.22 1.64 0.63 6.42AP 3.2 31.3 9.7 55.8 28.94 71.65 4.57 1.80 0.58 8.50AQ 3.2 24.2 23.7 48.9 23.14 59.32 3.59 1.45 0.68 8.06AR 2.1 28.5 12.0 57.4 28.23 71.60 4.33 1.67 1.23 7.07AS 3.1 35.2 9.4 52.3 29.27 70.20 4.92 1.39 0.88 10.11AT 2.6 37.0 9.5 50.9 29.79 73.86 5.27 1.52 0.99 6.26AU 2.5 36.9 10.1 50.5 29.16 70.44 4.74 1.49 0.97 9.76AV 2.9 35.6 10.0 51.5 29.43 72.09 4.82 1.44 1.06 7.70

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gas containing surplus O2. The hypothetical heat load of theprocess is 25.00 MJ. The remaining heat is allocated to theflue-gas. The flue-gas temperature is calculated for differentassumptions. Flue-gas temperatures were determined for coaloxygen represented as:

➤ O2➤ H2O(l)➤ CO➤ CO2.

In principle, coal could be represented by a mixture ofspecies, including oxygen-containing species other thanthose from list above. However, the list was chosen toillustrate a concept that could be applied to any validselection of species. The Ex MenteTM Easy Thermo programwas used to perform the equilibrium calculation.

Table V gives the resulting flue-gas temperatures fromthis study. For the four cases, the flue-gas temperature variedbetween 807°C and 1128°C, which suggest that the choice of

Representation of coal and coal derivatives in process modelling

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 345 ▲

Figure 2 – Vitrinite coal structure (Shinn, 1984)

Figure 3 – Hypothetical ‘process’: illustration of study on assumptionsof a compound to represent oxygen in coal

Table IVHypothetical example of coal oxygen representation by different compounds

Table V

Flue-gas temperatures from representation of coal oxygen by different molecules in the hypothetical example

Assumption 1: O2 Assumption 2: H2O Assumption 3: CO Assumption 4: CO2

Flue-gas temperature, °C 1128 807 1006 909

Representation of coal and coal derivatives in process modelling

the compound to represent oxygen in coal has a significantinfluence on the accuracy of modelling.

From fundamental analysis of the problem, there are atleast four different reasons why any representation of coalcould be inaccurate as far as the energy balance is concerned.

➤ Amorphous carbon in coal is at a higher free energy aswell as enthalpy state compared to graphite. This isillustrated in Figure 4. The difference of about 5kcal/mole C (1 kcal = 4.184 kJ) equates to about 1.0MJ/kg for coal containing 60% C, or up to about 1.5MJ/kg for higher grade coals (having higher carboncontent). Coke carbon is associated with a free energystate intermediate between coal and graphite. Formodelling purposes, thermodynamic values for graphiteare generally available and used

➤ Depending on the assumption of how oxygen in thecoal is represented, significant differences in calculatedenergy requirement or consumption for the sameprocess could occur

➤ Energy is required for liberation of the organicvolatiles. However, this value is usually not known fora specific coal

➤ Energy is consumed during pyrolysis of inorganiccompounds such as clays and carbonates (associatedwith ‘inert volatiles’, as discussed).

For pyrometallurgical modelling involving solid carbon, aswell as for tar, results from the bomb calorimeter can be usedto calibrate the enthalpy balance. These results represent realbehaviour, while calculated results could be inaccurate aspreviously explained. Such a calibration procedure shouldrectify inaccuracies resulting from all the reasons mentioned,including the choice of a compound to represent oxygen incoal.

It is hereby suggested that the calculated CV should becompared with the experimentally obtained CV, and thebalance used as a correction for the enthalpy of the coal orcoal derivative. In principle, this approach is also shared byPeacey and Davenport (1979) for the purpose of modellingthe injection of coal and other hydrocarbons into a blastfurnace. The balance between calculated CV and gross heat ofcombustion yields the heat of formation of a hydrocarbonfrom the elements. That indicates that the assumption ismade of O2 representing oxygen in coal, instead of e.g. H2O(l)which, from the discussion above seems to be a more logicalchoice.

Subsequently, the effects of determining the correctionvalue were studied for each of the four cases (where fourdifferent species were selected to represented oxygen in coal)to determine the effect of using each correction value on thepredicted flue-gas temperature of the hypothetical process(see Table VI). The strategy involved calculating a coal

enthalpy correction value as follows:

Coal enthalpy correction value = Gross CV – CalculatedCV [8]

The unit of this correction value would be MJ/kg. Byadding the enthalpy correction value (which could be eitherpositive or negative) to the calculated CV, the CV would becorrected to represent the experimentally determined grossCV. For the present hypothetical study, the coal enthalpycorrection value was calculated for each of the four cases,where oxygen in the coal was represented by differentspecies. Subsequently, the coal enthalpy (where carbon isassumed to be graphite by default) was corrected by thecorrection value, and the corrected flue gas temperatures werecalculated (Table VI).

A different coal enthalpy correction value is obtained forevery compound assumed to represent oxygen. However, thecorrected off-gas temperature is the same for all the differentassumptions, namely 1074ºC. Since the values, including thecoal CV, used in this example are hypothetical, this does notprovide proof of which compound best represents oxygen incoal.

Clearly. whatever the choice of the compound to representoxygen in coal, the correction value yields the same correctflue-gas temperature, as the correction value itself dependson the choice of a compound to represent oxygen.

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346 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 4 – Standard free energies of formation of various carboncompounds relative to graphite (Rosenqvist, 1974)

Table VI

Results from hypothetical example, including use of the coal enthalpy correction value

Assumption 1: O2 Assumption 2: H2O Assumption 3: CO Assumption 4: CO2

Flue-gas temperature before correction, °C 1128 807 1006 909Coal enthalpy correction value, MJ/kg coal 0.247 -1.183 -0.306 -0.737Flue-gas temperature after correction, °C 1074 1074 1074 1074

Nevertheless, it seems important that the correction yields arelatively accurate result. One could refer to the correctionterm as the ‘energy of decomposition of the volatiles’.However, that would suggest that, after devolatilization, nocorrection term would be required, while the form of carbon(graphite vs. amorphous carbon or coke) is still ofimportance. To designate the correction term, ‘heat offormation of a hydrocarbon’ (the approach by Peacey andDavenport, 1979) would yield an accurate result. However,the representation of oxygen as O2 could seem conflicting, asstudies suggest that oxygen would not be liberated from coalas O2 (except for minute quantities originating from the poresin the coal).

Although H–O bonds are not the only way oxygen iscombined in coal (Figure 2), representing oxygen in coal byH2O(l) seems to be a logical choice. The question arises aboutthe general accuracy of predicting the CV of South Africancoals by calculating from elemental analysis without usingthe correction procedure. Data for the same set of coals as forFigure 1 was used for this purpose. The theoretical CV wascalculated by the following formula, which was derived fromthe heats of combustion of the species in this study:

CV(calc) = 0.3276*%C + 1.4179*%H - 0.1787*%O +0.0926*%S [9]

whereCV(calc) is the calculated gross CV of the coal on an air-drybase (MJ/kg)%C: % carbon (air-dry base)%H: % hydrogen (air-dry base)%O: % oxygen (air-dry base)%S: % total sulphur (air-dry base).Note that Equation [9] is not the result of multiple

regression analysis. For the calculation it was assumed theoxygen in coal is present as H2O(l) and carbon is present asgraphite, according to the results depicted in Figure 5.Generally, good agreement between calculated and actualgross CV for all the different coals was found. Nevertheless,percentage errors up to 2.9% were found for this set of coalanalyses. In order to increase the accuracy of the energybalance so that it is equal to that obtained from the calori-metric technique, the enthalpy correction term must beutilized.

A positive coal enthalpy correction value could beinterpreted from the effect of a carbon form with a higherenthalpy than graphite outweighing the effect of decompo-sition of the volatiles, and vice versa. The purpose of thecorrection value is to correct the predicted CV to a value equalto that determined in the bomb calorimeter. If the processbeing modelled involves coal derivatives like coke, char, ortar, it would be advisable to determine the gross CVs of thosecoal derivatives and calculate enthalpy correction values forthem, to be incorporated in the respective energy balances.

It is important to note that, apart from the coal enthalpycorrection term, no additional correction value, as suggestedby Figure 4, should be used, as that would represent double-counting of effects, which would introduce an error in thecalculation process.

Another study was undertaken on the present set ofSouth African coals by representing coal oxygen as O2instead of H2O(l), according to Figure 6. This assumption

causes the coal calorific values obtained for the Free Stateand Mpumalanga coals to be over-estimated. Similar studiesfor oxygen represented as CO or CO2 were not included.

Note also that the study is limited to South African coals.It is possible that selecting a different compound to representoxygen could lead to slightly more accurate predicted CVs fordifferent coals. Nevertheless, the recommendation todetermine the CV of the coal and coal derivatives and correctthe enthalpy by comparison with the gross CV is a generalone.

ConclusionsCoal proximate analysis should not be regarded as absolute;it could vary with several parameters, including heating rate.For modelling, the use of ultimate analysis should beconsidered a preferable option to proximate analysis, where,‘fixed carbon’ and ‘volatiles’ are not defined in terms ofchemical composition. Significant errors could be introduced

Representation of coal and coal derivatives in process modelling

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 347 ▲

Figure 6 – Calculated vs experimental gross CVs for South African coalsand four imported hard coking coals (oxygen assumed to be present asO2)

Figure 5 – Calculated vs experimental gross CVs for South African coalsand four imported hard coking coals (oxygen assumed to be present asH2O(l))

Representation of coal and coal derivatives in process modelling

if the larger molecules are neglected during calculation of thecalorific value (CV) of coal gas (the gas liberated when coal isheated in the absence of oxygen).

For elemental analysis, the oxygen content (which iscalculated by balance) should be checked to ensure it iswithin the expected range. For representation of sulphur incoal, one should be careful to avoid double-counting due toSO3 in the ash analysis.

Potentially, oxygen in coal could be represented as O2,H2O, CO, or CO2. However, use of some of these specieswithout considering the experimentally determined gross CVintroduces significant errors in the energy balance. If coalenthalpy is calculated from elemental analyses withoutcorrection, representation of coal oxygen as H2O(l) givesreasonable accuracy (up to 2.9% error for a set of SouthAfrican coals). Coal volatiles could be represented by acomplex mixture of compounds, even using different oxygen-containing species other than the abovementioned four,provided the enthalpy is corrected.

It is recommended that an ‘enthalpy correction value’ isincorporated in energy balances involving combustion,devolatilization, or conversion of coal and coal derivatives,e.g. coke, char, or tar. That would imply that proximateanalysis, elemental analysis, as well as the gross CV would berequired for all solid or liquid coal-derived substances beingmodelled. No other correction due to the form of carbon beingdifferent than graphite should be used, as that would implydouble-counting some effects.

References

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CHIU, Y. and HONG, M. 1983. Influence of volatile matter and deposited carbon

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KARR, C. 1978. Analytical Methods for Coal and Coal Products. 2nd edn.

Academic Press, New York.

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ultimate analysis. Combustion and Flame 100. The Combustion Institute.

Elsevier. pp. 384–394.

PEACEY, J. and DAVENPORT, W. 1979. The Iron Blast Furnace Theory and Practice.

Pergamon Press, Oxford. p. 211.

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2014].

POWELL, A. R. 1945. Gas. Coal Carbonization – Preparation and Properties.

Wiley, New York. pp. 929–940.

RAUSCH, H. 1975. The behaviour of sulphur during direct reduction of iron ore

using coal. Stahl Eisen, vol. 95, no. 26. pp. 1266–1272.

ROSENQVIST, T. 1974. Principles of Extractive Metallurgy. McGraw-Hill

Kogakusha, Tokyo.

SANS 17246:2011, 2011. ISO 17246:2010: South African National Standard

Coal – Proximate Analysis. s.l. SABS standards division.

SANS 50:2011, 2011. ISO 562:2010: South African Standards Hard Coal and

Coke – Determination of volatile matter. s.l. SABS Standards Division,

Pretoria.

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SANS 589:2009, 2009. ISO 589:2008: South African national standard Hard

coal – Determination of total moisture. s.l. SABS Standards, Pretoria.

SCHOEMAN, J. and BOSHOFF, H. 1996. Analysis of coal product samples of

producing South African collieries. Bulletin no. 110. CSIR, Pretoria.

SHINN, J. 1984. From coal to single-stage and two-stage products: a reactive

model of coal structure. Fuel, vol. 63, no. 9. pp. 420–426.

WISER, W. 1973. Coal Catalysis. Electrical Power Research Institute, Santa

Monica, CA. ◆

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348 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

IntroductionDespite lengthy operational experience,understanding of the mode and principle ofPeirce-Smith converter (PSC) operation has notchanged significantly. Some modifications tothe typical PSC have been adopted, one notableexample being the Hoboken converter, whichis fitted with a siphon that permits process gascollection without atmospheric dilution (Bustoset al., 1995). Hoefele and Brimacombe (1979)allude to historical conservatism, rather thantechnological limitations, as the reasons forresistance to change.

Small versions of copper-nickel PSCs areused in platinum group metals (PGMs)smelters for removing Fe and S chemicallyassociated with Cu-Ni mattes rich in PGMs.Lonmin plc operates a PGM PSC approximatelyone-third the working volume of a typicalcopper-nickel PSC. Due to the bubbling regimeresulting from subsonic flow conditionscurrently employed in these operations,

common problems are encountered. Theseinclude tuyere blockages (which necessitatefrequent punching operations); high refractorywear in the tuyere region; substantialsplattering and splashing, which generatesignificant amounts of reverts (Richards et al.,1986; Wraith et al., 1994; Kapusta, 2010) andalso cause operational downtime withintermittent off-stack periods for cleaning theconverter mouth and aisle; and reducedoxygen efficiency, which is attributed to thepunching operation as a result of substantialair losses due to leakages, limiting theconverter capacity or the reprocessing ofreverts and dusts. These process inefficienciesare accompanied by energy inefficiencies or’excess’ power consumption related topunching machines, leaks at the tuyere bodydue to punching (wasted blower air), andunreacted injected air.

The conversion process occurs in a high-temperature environment in a refractory-linedsteel shell vessel, which precludes visualobservation and experimentation. In order todelineate critical process parameters, physicaland numerical modelling techniques have beendeveloped. Physical models with differentliquids simulating matte and slag have beendeveloped to study gas plume, splashing,mixing, phase distribution, and mass transferphenomena (Hoefele and Brimacombe, 1979;Richards et al., 1986; Chibwe, Akdogan, andEksteen, 2011; Chibwe, Akdogan, Aldrich, andEric, 2011; Chibwe, Akdogan, Aldrich, and

Sonic injection into a PGM Peirce-Smithconverter: CFD modelling and industrialtrials by D.K.Chibwe*, G. Akdogan*, G.A. Bezuidenhout†, J.P.T. Kapusta‡, S. Bradshaw*, and J.J. Eksteen*‡‡

SynopsisPeirce-Smith converters (PSCs) are extensively used in the copper, nickel,and platinum group metals industries. The typical converting operationinvolves lateral purging of air into molten matte through a bank oftuyeres. This blowing operation occurs at low pressure from the blowers,resulting in a bubbling regime that is considered inefficient from both aprocess and an energy utilization perspective. Inherent drawbacks alsoinclude recurrent tuyere blockage, tuyere punching, and low oxygenefficiency.

Western Platinum embarked on a full-scale industrial evaluation ofgenerating a jetting regime by using sonic injection. Prior to industrial-scale tests, a numerical assessment to ascertain the feasibility ofimplementing sonic injection into a PSC was conducted. The work includedflow characterization at high-pressure injection achieving sonic velocity atthe tuyere exit. The 2D and 3D simulations of the three-phase system werecarried out using the volume of fluid method together with the RKEturbulence model to account for the multiphase and turbulent nature ofthe flow.

This paper discusses the key findings in understanding plumeextension, velocity distribution, shear wall stress analysis, and phasedistribution characteristics in the system. Plant trials are also discussedwith reference to the commercial aspects of a full-scale implementation ofsonic injection in the smelter.

KeywordsPeirce-Smith converter, sonic injection, CFD modelling.

* Process Engineering Department, University ofStellenbosch.

† Process Division, Lonmin Western PlatinumLimited, Marikana.

‡ 3BBA, Montréal, Canada.‡‡ Department of Metallurgical Engineering, Curtin

University, Perth, Australia.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. This paperwas first presented at the, PyrometallurgicalModelling Principles and Practices, 4–5 August2014, Emperors Palace Hotel Casino ConventionResort, Johannesburg.

349The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

ISSN:2411-9717/2015/v115/n5/a2http://dx.doi.org/10.17159/2411-9717/2015/v115n5a2

Sonic injection into a PGM Peirce-Smith converter: CFD modelling and industrial trials

Taskinen, 2011). Richards et al., (1986) concluded that themain cause of splashing was the development and intensifi-cation of slopping resulting from the manifestation of auninodal wave. Their analysis showed that gas-liquidcoupling increases with tuyere submergence depth, hence thereduction in splashing. For the small working volume PSCused in the PGM industry, tuyere submergence is shallowrelative to that in Cu-Ni PSCs (Brimacombe et al., 1984). Anypossible injection consideration in the small PSC should takethis limitation into account.

PSC campaign life is dependent on the integrity and stateof the refractory in the converter. Due to subsonic flowconditions, the refractory in the tuyere line has commonlybeen observed to deteriorate much faster than in the rest ofthe converter. Three mechanisms of refractory wear havebeen identified: chemical corrosion, thermal spalling, andmechanical wear (Gonzalez et al., 2007). Goni et al. (2006)quantitatively estimated that 35–65% of refractory wear in aPSC is due to chemical and thermomechanical processes. Thistype of refractory erosion is the result of a combination of gasdynamics in the proximity of the tuyere nozzle where hightemperature gradients exist, and the punching operation,which generates mechanical shock.

Brimacombe et al. demonstrated at both the laboratory(Hoefele and Brimacombe, 1979; Brimacombe and Hoefele,1980; Brimacombe et al., 1990) and plant scales(Brimacombe et al., 1984; Bustos et al., 1987) that sonicinjection (jetting regime) into copper or nickel converterscould reduce or eliminate the above-mentioned process andenergy inefficiencies. In 1979, Hoefele and Brimacombecarried out the first experimental studies on sonic injectioninto a PSC using air-water, air-ZnCl2, and air-Hg systemscoupled with plant trials. Pressure measurements in bothlaboratory experiments and plant trials showed that only theair–mercury system had the same bubble frequency as theplant, indicating the importance of the gas–liquid densityratios on the dynamics of submerged injection processes.Strikingly improved penetration of gas into liquid wasobserved at sonic conditions. Subsequent plant trials withstraight-bore tuyeres designed for sonic flow were conductedat the ASARCO smelter in the USA (Brimacombe et al.,1984), the Toyo Smelter in Japan (Kimura et al., 1986), andthe Noranda and INCO copper smelters in Canada (Bustos etal., 1987). The salient points from the above work were asfollows: the horizontal penetration force is relatively lowcompared to the buoyancy force exerted by the bath; thestability of the tuyere accretions formed depends onconverting cycle; and punchless operation is possible at

higher injection pressure. Based on the understanding ofaccretion formation and stability, coupled with the processbenefits of sonic injection, the Air Liquide Shrouded Injector(ALSI) technology was developed (Bustos et al., 1995). WithALSI technology, air oxygen enrichments between 30% and40% have been achieved without detrimental refractoryerosion. Commercial implementation of ALSI technology wasinaugurated at the Falconbridge smelter in Canada (Bustos etal., 1999) and later notable applications included the ThaiCopper Industries smelter (Kapusta et al., 2007).

Lonmin is interested in implementing such technology ona commercial scale. Prior to implementation, key processaspects needed to be evaluated, amongst them slopping,splashing and mixing characteristics, refractory integrity, andthe possible extent of air penetration into the bath in theserelatively small converters with shallow tuyere submergence.A realistic presentation of such a system needed to bedeveloped in order to obtain conclusive interpretations forinitial trials. Moreover, a rigorous system developmentsatisfying the geometry and dynamic similarity was alsoneeded.

For this purpose, characterization of the dynamics of thethree-phase (air, matte, and slag) flow in the PSC used atLonmin was conducted at high air pressure injectionachieving sonic velocity at the tuyere tip by using CFDsimulations. The 2D and 3D simulations of the three-phasesystem were carried out using the volume of fluid (VOF) andrealizable turbulence models to account for the multiphaseand turbulent nature of the flow respectively. These modelswere implemented using the commercial CFD numerical codeFLUENT. The simulations from the current investigationrevealed both qualitative and quantitative results of flowcharacteristics in the converter, which paved the way forwardin planning the trials and selecting the converter to equipwith sonic tuyeres. The full-scale plant trials have beensuccessfully completed with promising results.

Numerical simulationsIn this work, 2D and 3D simulations were carried out basedon a slice model of the Lonmin PSC. Table I gives thedimensions of the actual converter and slice model.

The computational domain was discretized into smallcontrol volumes for the calculations. Very fine meshes in thetuyere region were necessary to accurately capture the flowpattern. Domain decomposition was done in order to facilitatemesh multiple methods with local control for the creation of aconformal hybrid mesh as shown in Figure 1.

Modelling was done on an Intel® Core ™ i7 CPU with 3.46GHz processor and 8.0 GB installed random access memory(RAM). The commercial CFD code ANSYS FLUENT, version14.0, was used for the calculations on a high-powercomputing (HPC) cluster with an installed capacity of eight2.83 GHz processors per node with 16 GB of RAM. In thispaper, simulations conducted at midway through a typicalblow will be presented, as this period accounts for more than85% of the converting cycle time. In order to reduce thecomputational time during the simulations, the flow in thesonic tuyere was not included but simulated separately, andthe flow conditions at the tuyere exit were taken as the inletboundary condition of the computational domain. This valuewas calculated using the isentropic flow theory. Only two

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350 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table I

Lonmin converter and slice model dimensions

Dimensions System

Lonmin coverter Slice model

Diameter inside refractory (mm) 2248 2248Length inside refractory (mm) 3658 165Tuyere inner diameter (mm) 48 32Number of tuyeres 18 1Average tuyere spacing (mm) 165 -

simulations were conducted with a 300 mm tuyere pipecoupled to the converter to visualize the development of airflow into the converter. A segregated solver with an implicitapproach was used to calculate the pressure, velocity,turbulence, and density through solving unsteady andcompressible flow conservation-governing equations, namelycontinuity, momentum, and energy. In order to account forthe multiphase nature of the flow, the VOF model was used.The interfacial behaviour of air, matte, and slag was capturedby this model using a compressive discretization scheme.This is accomplished by surface tracking of the phaseinterfaces in the system through solution of the VOFcontinuity equation. In the model, the different phases aretreated numerically as interpenetrating continua, thusinevitably introducing the concept of phasic volume fractionwhere the volume fractions in each computational cell sum tounity. The effects of turbulence on the flow field inside themodel were incorporated by using the realizable k−ε model,which offers improvements in the overall energy transfer.The flow conservation-governing equations, the VOFequation, and turbulence model equations were solved withFLUENT version 14.0. This package is a finite-volume solverusing body-fitted computational grids. A coupled algorithmwas used for pressure-velocity coupling. A compressiveinterface capturing scheme for arbitrary meshes (CICSAM)discretization was used to obtain face fluxes when thecomputational cell is near the interface using a piecewise-linear approach. This scheme was necessary due to the highviscosity ratios involved in this flow problem (ANSYS, 2011).A time step of 0.0001 seconds was used and found to besufficient for maintenance of numerical convergence at everytime step and stability. Convergence of the numerical solutionwas determined based on surface monitoring of integratedquantities of bulk flow velocity and turbulence and scaledresiduals of continuity, x-, y-, z-velocities. The residuals ofall quantities were set to 0.001, and the solution wasconsidered converged when all the residuals were less thanor equal to the set value.

Results and discussion

CFD modellingFrom the numerical simulations conducted in this work, the

computed plume extension for current (subsonic) andenvisaged sonic operation are plotted in Figure 2. Adimensionless parameter (x/de) where x is the exit jetdistance (in millimetres) and de is the exit tuyere diameter (inmillimetres) was used to visualize the extent of the plumepenetration into the converter.

In Figure 2, the plume extension into the bath forsubsonic and sonic conditions is indicated by ‘Plume Sonic’and ‘Plume Subsonic’. According to these results, plumesonic penetration into the bath is four times longer than thatof plume subsonic. The extension of the plume region intothe converter away from the tuyere exit area is essential as itprovides extra volume for chemical reactions to take place. Intheir mass transfer studies of the PSC, Adjei and Richards(1991) concluded that the substantial part of the chemicalreactions in the converter is likely to occur in the tuyereplume region.

Also, the simulations reveal that the bath circulatoryvelocity outside the plume region is approximately 0.27 m.s-1

for both flow conditions. These results are in consistentagreement with the assumption made by Bustos,Brimacombe, and Richards (1988) in their development of amathematical model for accretions growth in PSCs for

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351The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

Figure 1 – Computational grid used showing fine tuyere elements

Figure 2 – Axial velocity distribution on the tuyere centre axis forsubsonic and sonic flow conditions

Sonic injection into a PGM Peirce-Smith converter: CFD modelling and industrial trials

subsonic and sonic operations. Figure 3 shows the velocityvector distribution around the tuyere exit for both flowconditions. It can be observed that the sonic injection plumeextends further into the bath, and with a higher velocity,than with subsonic operation. Lower velocity regions areevident further away from the plume.

Figure 4 shows the phase density distribution forsubsonic and sonic flow conditions. A high air volume regionin front of the tuyeres can be seen for sonic flow conditions,compared with subsonic flow. This is consistent with theresults shown in Figures 2 and 3. Due to the agitation in theregions in front of the tuyeres, a strong emulsification exists,resulting in high reaction rates in the zone. This is inagreement with the observations by Rosales et al. (1999) intheir study of fluid dynamics in a Teniente converter.

The effects of bath circulation and bath density on thewalls of the converter were evaluated by calculating wall

shear stress along the converter wall boundaries. Figure 5shows the wall shear stress distribution for subsonic andsonic flow conditions. Near the tuyeres, for subsonic flowconditions a maximum wall shear stress of 200 Pa wasobtained compared to 125 Pa for sonic flow. This suggeststhat sonic injection could reduce the refractory wear due tomechanical erosion around the tuyere region. At the wallopposite to the tuyeres line, the stress is higher for sonicinjection due to the propagation of waves further from thetuyeres, which carry energy to the opposite sidewalls. This isdesirable for achieving better mixing conditions in theconverter, whereas with subsonic conditions energy isinstantly dissipated just above the tuyeres as shown inFigure 3, which might lead to increased refractory erosion.

Plant trialsPlant trials were conducted to demonstrate the feasibility ofhigh-pressure sonic injection technology into relatively smallPSCs. Once the target total flow rate of compressed air intothe converter and the number of sonic tuyeres had beenfinalized, sonic tuyeres were designed and dimensioned. Allof the necessary equipment for the supply and control of thecompressed air flow to the converter was also sourced inpreparation for the trials. A new reline was installed and thepunching machine was removed. The sonic tuyeres wereinstalled using the same tuyere body as for normal operation.SCADA programming, alarms, and control set-points werethen carefully evaluated and implemented to ensure the safeand controlled operation of the converter during the sonicinjection trials.

The main purpose of high-pressure injection is thedevelopment of a different flow regime in the tuyere regionthrough manipulation and designing of the blowingconditions and configuration. In this work, the dimensionlessparameters – namely tuyere flow Mach number and injectedair specific mixing power (εm) – were the main criteria for PSCmanipulation and design for sonic injection. The injected airspecific mixing power (εm) is given by:

[1]

where εb is the specific mixing power due to buoyancyand εk the kinetic energy. The mathematical expressions aregiven in Equations [2] and [3]:

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352 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 4 – Phase distribution density contours for subsonic and sonicflow conditions

Figure 3 – Velocity vector distribution around the tuyere exit for (a)subsonic and (b) sonic flow conditions

Figure 5 – Wall shear stress distribution for subsonic and sonic flowconditions

[2]

[3]

whereW is the effective bath weight (kg)Q is the total gas flow rate (Nm3s-1)Pa is the atmospheric pressure (kPa)A is the total tuyere cross-sectional area (m2)ρb is the bath density (kgm-3)Hs is the injection submergence (m) The PSC blowing conditions are given in Table II.From Table II, it is evident that as the blowing conditions

changes from subsonic to sonic regime, the ratiochanges in such a manner that the flow is dominated bykinetic specific mixing power at sonic operation.

Before the sonic injection trials began it was found thatthe main characteristics of subsonic injection were the highvariability of both the air flow rate and the injection pressure,as shown in Figure 6. This high variability is a directconsequence of the blocking accretions that form, resulting inlower flow and higher pressure, and the unplugging of thetuyeres by punching, resulting in a sudden higher flow andlower pressure.

In contrast, as shown in Figure 7, the air flow rate duringsonic injection is less variable compared to subsonicinjection. A more stable air flow rate is one of the expectedbenefits of sonic injection. The flow rate curve shows asignificantly reduced variability compared to the blow shownin Figure 6. Even more significant is the stability of the sonicinjection pressure. The stability of both the flow rate andpressure demonstrated that the new operating strategy wassuccessful. Controlled splashing was also accompanied by astable flow rate and pressure of compressed air, as illustratedin Figure 7. Also, the maximum refractory wear rate rangedbetween 10.3 and 11.1 mm per blow, which corresponds to37 to 40 blows per campaign, or a 34% reduction inrefractory wear with sonic injection compared with conven-tional subsonic injection. These measurements of refractorywear, although conducted over a short period of time or ashort number of blows, still provide an industrial validationof the theory that the accretions formed during sonic injectionare indeed protective rather than disruptive.

When operated in sonic mode, the converter capacity toreprocess reverts was found to increase by as much as 200%compared to that with the low-pressure bubbling regime,

owing to the relatively higher oxygen efficiency. In summary,sonic injection offers significant flexibility for periods of highproduction of furnace matte – reducing the revertreprocessing rate to take full advantage of fast sonic blows –or for periods when a high reverts reprocessing capacity isneeded. Table III highlights some of the benefits of using asonic regime in the converter.

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The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 353 ▲

Table II

PSC blowing conditions

Number Tuyere Tuyere εm

of internal Mach (kWt-1)tuyeres diameter number

(mm)

Subsonic converter 18 48 0.32 6.73 4.87Sonic converter 8 32 1.0 6.79 1.35

εbεk

Table III

Comparison of sonic and subsonic trials at Lonmin

Factors Operation

Subsonic (current) Sonic

Punching operation Yes NoneOxygen efficiency, % 65 92Converter campaign, cycles 26 37–40Scrap reprocessing, ton 2.97 9.30In-stack time, min 469 359

Figure 7 – Flow and pressure variations for sonic blow

Figure 6 – Flow and pressure variations for subsonic (conventional)blow

Sonic injection into a PGM Peirce-Smith converter: CFD modelling and industrial trials

ConclusionsThe CFD modelling formed part of an assessment tocomplement feasibility studies of implementing high-pressuresonic injection into relatively small Peirce-Smith converters(PSCs) used at Lonmin plc prior to the plant trials. Themodelling work was carried out to characterize the fluiddynamics of three-phase (air, matte, and slag) fluid flow withhigh-pressure injection of air at sonic velocity. The modellingresults provided a basis for further development of sonicinjection into relatively small industrial PSCs, with theultimate objective of reducing energy consumption,improving process efficiency, and increasing the throughputof the converting process. The results revealed that the plumeextended into the bath approximately four times deeper atsonic flow conditions relative to subsonic flow conditions.Lower wall shear stress values for sonic flow conditionssuggest that sonic injection could prolong the refractory life.Higher pressure injection gave rise to regions of high airvolume in front of the tuyeres relative to low-pressureinjection operation. With subsonic flow the injected gasascended near the converter wall above the tuyeres for asignificant period of time and thus a high refractory wear inthe tuyere region would be expected relative to sonicinjection. These findings showed that high-pressure injectioninto PGM PSCs converters is feasible.

Following the modelling exercise, sonic injection trials atone of Lonmin’s converters were successfully completed.Punchless operation was achieved with sonic injection, withthe capacity to reprocess much larger amounts of reverts thanwhen operating in the conventional mode due to the higheroxygen efficiency in sonic mode. Sonic injection resulted in alower refractory wear per blow or per ton of matte, leading tolonger campaign cycles.

In summary, sonic injection offers significant flexibilityfor Lonmin’s converting operation by allowing operators toadjust their practice for periods of high production of furnacematte – reducing the reverts reprocessing rate to take fulladvantage of fast sonic blows – or for periods when a highreverts reprocessing capacity is needed.

AcknowledgementsThe authors acknowledge Lonmin Plc for the financing of thiswork, and express their gratitude to all personnel at Lonmin’sconverter aisle for their support and commitment to thesuccess of the plant trials. Permission from BBA and Lonminto publish this paper is also appreciated.

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KAPUSTA, J.P., WACHGAMA, N. and PAGADOR, R.U. 2007. Implementation of theAir Liquide Shrouded Injector (ALSI) technology at the Thai CopperIndustries Smelter. Proceedings of Cu2007, the Sixth InternationalCopper–Cobre Conference, Toronto, ON. Vol. III (Book 1) – The Carlos DiazSymposium on Pyrometallurgy Warner, A.E.M., Newman, C.J., Vahed, A.,George, D.B., Mackey, P.J., and Warczok, A. (eds). The MetallurgicalSociety of CIM, Montreal, Canada. pp. 483–500.

KIMURA, T., TSUYUGUCHI, S., OJIMA, Y., MORI, Y., and ISHII, Y. 1986. Protection ofrefractory by high-speed blowing in PS converter. 115th AIME AnnualMeeting, New Orleans, LA, 7 March 1986.

RICHARDS, G.G., LEGEARD, K.J., BUSTOS, A.A., BRIMACOMBE, J.K., and JORGENSEN, D,1986. Bath slopping and splashing in the copper converter. The ReinhardtSchuhmann International Symposium on Innovative Technology andReactor Design in Extraction Metallurgy, Colorado Springs, CO, 9–12November 1986. Gaskell, D.R. (ed.). The Metallurgical Society of AIME,Warrendale, PA. pp. 385–402.

ROSALES, M., FUENTES, R., RUZ, P., and GODOY, J. 1999. A fluid dynamicsimulation of a Teniente converter. Copper 99–Cobre 99 InternationalConference, Phoenix, AZ, 10–13 October 1999. Vol. VI. SmeltingTechnology Developments, Process Modeling and Fundamentals. pp.107–121.

WRAITH, A.E., HARRIS, C.J., MACKEY, P.J. and LEVAC, C. 1994. On factors affectingtuyere flow and splash in converters and bath smelting reactors.Proceedings of the European Metallurgical Conference 1994 (EMC’94),Vol. I. Society for Mining, Metallurgy, Resource and EnvironmentalTechnology (GDMB), Clausthal-Zellerfeld, Germany. pp. 50–78. ◆

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354 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

IntroductionThe metallurgical importance of tundishes hasled to much research into various aspects oftundish operation and design, and investi-gations of the flow properties and inclusionbehaviour through both physical andnumerical modelling. A large portion of theresearch has been focused on the use of flowcontrol devices to alter flow patterns andimprove the tundish performance byeliminating short-circuiting, increasing theresidence time to promote inclusion removal,and reducing variation between strands inmulti-strand tundishes (Craig et al., 2001,2003; Kumar et al., 2007, 2008; Jha et al.,2008; Tripathi and Ajmani, 2011; Zhong et al.,2007). Craig et al. (2001) worked on theoptimization of a single-strand stainless steelcaster tundish for two separate tundish config-urations – dam and weir, and baffle withangled holes and an impact pad. Significantimprovements of up to 34% in minimumresidence time were obtained for the secondconfiguration. Moreover, in 2003 Craig et al.

reported a CFD study combined withmathematical optimization to design theconfiguration of the new enlarged tundish.Design variables chosen included the positionand sizes of baffles and baffle holes andpouring box width, while the design objectivewas maximization of the minimum residencetime (MRT) at operating level and at a typicaltransition level. The study showed thatmathematical optimization techniques can becoupled with CFD techniques to obtainoptimum tundish designs with significantimprovements.

In general, most authors have assumed thetundish to be isothermal, but some havelooked at the effect of temperature gradientsdue to heat loss from the tundish (Liu et al.,2008; Mishra et al. 2012; Miki and Thomas,1999) or the effect of the cooling rate of meltarriving from the ladle (Qu et al., 2012).Modelling strategies are necessary wheninvestigating tundish behaviour because of thehigh temperature and opaque nature of thesteelmaking process, which makes performingcertain experiments on the real systemimpossible. For the physical modellingprocedure, full- or reduced-scale water models,based on Reynolds number or Froude numbersimilarity, are used (Mazumdar and Guthrie,1999). The flow behaviour is usually charac-terized by investigating the residence timedistribution (RTD) curves under variousoperating and design parameters.

In this study a physical model was used toinvestigate the effect of different turbulenceinhibitor designs on the flow properties of afour-strand trough-shaped tundish tocontribute to the current knowledge and

Physical and numerical modelling of afour-strand steelmaking tundish usingflow analysis of different configurationsby J.H. Cloete*, G. Akdogan*, S.M. Bradshaw*, and D.K. Chibwe*

SynopsisModern tundishes have evolved as vessels to serve as the final step inrefining of molten steel by removing inclusions and promoting thermo-chemical homogeneity.

In this study the flow behaviour in a four-strand tundish wasinvestigated by means of a ½-scale water model as well as numericalmodelling. The numerical and physical models were used to characterizeresidence time distribution and calculate properties pertaining to tundishflow regime. Three different tundish configurations were investigated: abare tundish with no flow control devices, a tundish with a turbulenceinhibitor, and a tundish with both a turbulence inhibitor and a dam.

The physical and numerical models showed that a tundish withoutflow control devices is prone to significant short-circuiting. A tundish witha turbulence inhibitor was shown to be successful in preventing short-circuiting and provided surface-directed flow that might assist the removalof inclusions from the melt. However, it was also observed that theupward-directed flow caused the maximum turbulence kinetic energy nearthe surface to increase dramatically. The potential for slag entrainmentshould therefore be considered during the design and operation oftundishes with turbulence inhibitors.

Keywordstundish, steelmaking, CFD, physical modelling, numerical modelling.

* Process Engineering Department, University ofStellenbosch.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. This paperwas first presented at the, PyrometallurgicalModelling Principles and Practices, 4–5 August2014, Emperors Palace Hotel Casino ConventionResort, Johannesburg.

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ISSN:2411-9717/2015/v115/n5/a3http://dx.doi.org/10.17159/2411-9717/2015/v115n5a3

Physical and numerical modelling of a four-strand steelmaking tundish

understanding of multi-strand tundish design and operation.Of specific interest is a design proposed by Kumar et al.(2008) where the use of a turbulence inhibitor with holes incombination with low dams is proposed to improve flowproperties. This study investigated whether the addition ofthe holes and dams provides significant advantage comparedwith using only a turbulence inhibitor without holes, whichwould be less expensive to construct and maintain. Anumerical model was also developed in FLUENT to simulatethe flow behaviour in the water model, improving theunderstanding of the results of the physical modelexperiments. The effect of the assumptions of symmetry anddynamically steady flow on numerical results, which werefrequently used in tundish CFD studies to reduce thecomputational time (Kumar et al., 2008; Tripathi and Ajmani,2011; Zhong et al., 2007; Mishra et al., 2012; Tripathi andAjmani, 2005; Jha et al., 2001) was also investigated. This isof importance, as there is no information on the effects ofthese assumptions on computational accuracy in the availableliterature on tundishes. The numerical model was also usedto investigate the effect of the upward flow on surfaceturbulence, since high surface turbulence creates the potentialfor slag entrainment, which will cause new inclusions to formand lower the product quality. This is therefore a factor worthconsidering when implementing the use of turbulenceinhibitors.

Experimental modellingThe physical model was constructed from clear, 6 mm PVC,with all dimensions scaled to a 1:2 ratio compared to theindustrial tundish. The four sidewalls were inclined by 10°.The dimensions of the tundish are shown in Figure 1.

The turbulence inhibitor was constructed from grey PVC,15 mm thick. The dimensions are shown in Figure 2. Theturbulence inhibitor was attached to the tank using siliconeadhesive, when required for experiments. The holes in theside of the turbulence inhibitor could be closed by insertingcorrectly sized PVC blocks into them and sealing the gapswith silicone.

The complete physical model set-up is shown in Figure 3.

To simplify construction, the water feed tank was placed tothe side of the tundish model. However, CFD simulationsshowed that by simply placing a 90° bend in the feed pipeabove the tundish, an asymmetric velocity profile woulddevelop. To rectify this, an 8 litre cubic tank, made fromstainless steel, was suspended above the inlet point at thecentre of the tundish. CFD simulations showed that thisensures an even velocity profile in the inlet pipe. The inletpipe had a diameter of 30 mm and penetrated the tundish to165 mm from the bottom of the tundish. A T-junction at thetop of the inlet pipe allowed tracer fluid to be injected into theinlet stream using a syringe. Outlet holes were drilled in thebottom of the bath. The desired flow rate, at the bath heightused, was obtained by calculating the required diameter ofthe outlet from Bernoulli’s equation. A PVC insert with thecorrect diameter was then placed into each of the holes. Themeasured flow rates in each of the outlets were within 2% ofthe desired rate. Apart from geometric similarity, the Froudenumber was used as the criterion for dynamic similarity. Inreduced-scale water modelling it is impossible to simulta-neously achieve both Froude number and Reynolds numbersimilarity. It has been shown by Sahai and Emi (1996) thatthe RTD response of tundish water models is insensitive tothe Froude number over a large range of values, and to theReynolds number, if the tundish is operated in the turbulentregime. Since tundishes are generally operated turbulently,either Froude number or Reynolds number similarity may beused to scale the flow rates for the water model. However,Froude number (Equation 1) similarity is required for themodelling of inclusions and is therefore most often used intundish physical modelling. In this study, Froude numbersimilarity was selected to enable addition of inclusions to thewater model in the future.

Table I gives a comparison of design and operatingparameters for the industrial tundish and the 1:2 watermodels. During scaling based on the Froude number, the inletflow rate of the water model was calculated so that theFroude number at the inlet is identical to that of the industrialtundish. The Froude number at the inlet is calculated as:

[1]▲

356 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure1 – Dimensions of the tundish model (mm) shown from (a) thefront view, (b) the side view, and (c) the top view

Figure 2 – Dimensions of the turbulence inhibitor (in mm) shown from(a) the front view, (b) the side view, (c) the top view, and (d) theisometric view

During the experiments, the water was first allowed torun for approximately 15 minutes at the desired flow rate toallow a steady flow to develop in the tundish model. Anaqueous potassium chloride solution (200 g/l) was used astracer. To initialize the experiment 50 ml of the tracersolution was injected near the inlet. This was done over aperiod of five seconds to prevent the tracer injection fromdisturbing the inlet velocity profile to a significant extent. Thedelay this caused was justified by the fact that for all set-ups,except for the bare tundish, the minimum residence times arerelatively large compared to this delay.

For the duration of the experiment the total dissolvedsolids in the water passing through an outlet was monitoredusing a conductivity meter. The conductivity meter wasplaced in a small container below the outlet to preventdisturbance of the flow pattern within the tundish. Thevolume of the container was minimized to allow the delaycaused by the mixing to be small compared to the residencetimes observed. The experiment was performed for only twoof the outlets, because of the symmetry of the geometry. Fiveexperiments were performed for each tundish configurationto obtain an average residence time distribution that accountsfor variation between runs.

Flow characterization Several properties were calculated from the RTD results ofboth the physical and numerical experiments to characterizethe flow in the tundish being studied. The first step was toderive the dimensionless C-curve for the tundish. Thedimensionless time, θ, was calculated as:

[2]

where t is the theoretical mean residence time, t = V/Q [3]The dimensionless concentration of strand i (the outflow

at outlet i) can be calculated as:

[4]

where V is the total volume of the tundish, ci the concen-tration in the strand, and M the total amount of tracerinjected.

Since the flow rates in all strands are equal, the overalldimensionless concentration can be simply calculated as theaverage of the individual strand concentrations. Anotherimportant characteristic property of the flow is the meanresidence time, tmean, calculated as:

[5]

To calculate the mean residence time for an individualstrand, the average concentration at the outlets, C, is simplysubstituted by the concentration at the strand beingconsidered. The tundish performance can further be classifiedby dividing the tundish into three flow volumes: the plugvolume (Vp), the well-mixed volume (Vm), and the deadvolume (Vd), which are defined according to the modifiedcombined model by Ahuja and Sahai (1986) as:

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Figure 3 – Schematic representation of the physical model set-up

Table I

Comparison of design and operating variables ofthe industrial tundish and the model

Property Industrial tundish Water model

Length (m) 3 1.5Volume (l) 2000 250Mass flow rate (kg/min) 1015 25.6Volumetric flow rate (l/min) 145 25.6Bath height (m) 0.78 0.39Froude number 1.24 1.24

Physical and numerical modelling of a four-strand steelmaking tundish

[6]

[7]

[8]The ratio Qa/Q is the fractional volumetric flow rate

through the active region and is equal to the area under theC-curve between the bounds of θ = 0 and θ = 2. After θ = 2,the tail data is considered to be only from the dead regions.

Numerical modellingNumerical simulation has several advantages over physicalmodelling. Firstly, it provides information on propertiesthroughout the flow domain. Therefore, informationregarding the velocity and turbulence kinetic energy profilescan be used to explain the RTD results obtained throughphysical modelling and to identify possible ways to improvethe system. Secondly, once an accurate model has beendeveloped, different geometries can easily be studied usingthe numerical model without making costly and time-consuming changes to a physical model. In this study thecommercial CFD package FLUENT® 14.5 was used.

A review of the literature on tundish numerical modelling(Chattopadhyay et al., 2010) showed that the majority ofauthors had used the standard k-ε model to modelturbulence. The standard k-ε turbulence model has beenshown to be reasonably accurate in predicting flow character-istics for a single-strand tundish from RTD experiments, andis still preferred above slightly more accurate models becauseof significantly lower computational times (Jha et al., 2003).For this study the realizable k-ε (RKE) model was selectedsince it provides additional accuracy for swirling flows and isonly slightly more computationally expensive than thestandard k-ε model.

At the inlet, the normal velocity, calculated from the inletarea and the volumetric flow rate, was specified, assuming aflat profile at the ladle outlet. The top surface of the modelwas assumed to be flat and frictionless. This was done bydeclaring the surface as a wall with zero shear stresscomponents in all three dimensions. The outlets werespecified as pressure outlets with zero gauge pressure. Thecombination of the bath height and outlet diameter causedthe total outlet mass flow to be equal to the inlet mass flowrate. All other surfaces were set to stationary walls, assumingthe no-slip condition. The standard wall function was used tomodel the turbulence near the walls. The flow was assumedto be symmetrical over the planes through the centre of thebath along its length and its width, hence a quarter modelwas used. Therefore, zero gradients of all properties wereassumed normal to these planes. The three-dimensionalgeometry of the fluid volume was created for the differentgeometries considered in this study and meshed using atetrahedral mesh. The mesh was refined near curved wallsand walls with close proximity to each other to produce amesh of acceptable quality. Figure 4 shows the domainmodelled with CFD.

In FLUENT, the flow and turbulence in the domain wasfirst solved for steady flow, reaching a converged solutionafter approximately 12 000 iterations. For the finalsimulations, this initial solution was then used to locallyrefine the mesh in areas with high velocity gradients, usingthe gradient adaption function in FLUENT to obtain a moreaccurate solution. Throughout this study the minimum meshorthogonal quality, a measure of the mesh quality, wasmaintained above 0.09 after gradient adaption. Generalguidelines suggest not using meshes with a minimumorthogonal quality of less than 0.05 (ANSYS, 2011);therefore sufficient mesh quality is maintained in this study.The final meshes contained approximately 2.8 million cells.The refined mesh was then used to solve for more accurateflow and turbulence solutions, requiring approximately 15 000 additional iterations for a converged solution. Thetransient and species solvers were then activated and themomentum and turbulence solvers turned off. This allowedthe transient movement of the tracer fluid to be tracked in thesteady velocity and turbulence fields. To simulate thephysical experiment, the inlet boundary condition was set toadd tracer to the tundish for five seconds, after which theaddition of tracer was stopped. The concentration of tracer atthe outlets was then monitored for two and a half times thetheoretical mean residence time of the tundish to calculate theflow characteristic properties from the RTD response.

Grid independence and validation A grid-independence study was performed to determinewhether the mesh used is sufficient to accurately solve theflow in the tundish. This was done by repeating thenumerical experiment for cases with different specified meshsizes and comparing the RTD curves. A mesh specified with aconstant size is preferred, since this is the easiest to set upand to use in different geometries. However, due to thecontrast between the fast flow near the inlet and outlets andthe very slow flow through the rest of the bath, the gradientsin the bath will also vary to a large extent. For this reason,some areas will require a very fine mesh for accurate

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358 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 4 – Illustration of the domain modelled using CFD with theimportant boundaries indicated

solutions, while most of the tundish will require only a coarsemesh. Therefore, by refining the mesh in regions with highvelocity and turbulence gradients, a more efficient numericalmodel can be obtained. It was determined that gridindependence was achieved by performing a singlerefinement, using the gradient adaption tool in FLUENT, on a10 mm mesh, effectively reducing the size of the adaptedcells to 5 mm. The velocity gradient at which adaption startswas determined by lowering the adaption threshold until thefinal solution no longer changed appreciably.

Results and discussionIn this study, aspects of the modelling procedure, such assymmetry, steady flow, and grid independence, wereinvestigated. Next, the numerical model was validatedagainst physical model results for three different configu-rations. The first set-up consisted of a bare tundish; thereforeno flow control devices were used. The second set-up used acombination of the turbulence inhibitor with holes (TID),shown in Figure 2, and a low dam, which altered the flow byforcing it over the dam. The ratio of the dam height to thebath height was 0.2. The dam was placed at X=0.39L whereX is the distance from the inlet to the dam and L is half thelength of the tundish. The final set-up included a turbulenceinhibitor, as shown in Figure 2, but with the side holes closed(TI). This allows the effect of the inclusion of the holes anddams, the design proposed by Kumar et al. (2008), to beevaluated. Finally, the numerical model results were used toevaluate the performance of the different tundish configu-rations.

Symmetry assumptionThe geometry of the tundish used in this study issymmetrical over two planes. Using this knowledge, the fluidvolume solved in FLUENT can be reduced to a quarter of itsoriginal size, reducing the computational time requiredsignificantly. Another simplification to the numerical modelthat is frequently used in tundish modelling is to assumedynamically steady flow. This assumption allows only thespecies equation, which converges much faster than themomentum and turbulence equations, to be solvedtransiently. This allows a single iteration per time step to beused, instead of approximately six to eight iterations per timestep, drastically reducing the required computational time.

Although both of these assumptions are frequently usedin tundish literature (Kumar et al., 2008; Tripathi andAjmani, 2011; Zhong et al., 2007; Mishra et al., 2012;Tripathi and Ajmani, 2005; Jha et al., 2001), there is littleinformation available about their effect on the numericalresults. For this reason, the RTD curves were compared forthree different cases: dynamically steady flow with twosymmetry planes, dynamically steady flow withoutsymmetry, and fully transient flow without symmetry. Thetundish configuration used for the comparison was theturbulence inhibitor with holes in combination with thedams. However, because of the high computationalrequirements of the cases without simplifying assumptions, itwas clear from preliminary grid-independence results thatthese solutions would not be feasible with the amount of cellsrequired to reach grid independence. Hence a mesh of 14 mmcells was used for this comparative study to allow a rough

estimate of the effect of these assumptions. The resultsshown use an average value of three simulations for eachcase, to account for variability in the results. The variabilitywas much higher than for the final grid-independent runs. Itwas argued that this was because a new mesh was generatedfor each run, causing differing numerical errors due to themesh being insufficiently fine in certain areas.

Figure 5 shows that the use of two symmetry planes doeshave an effect by slightly increasing the peak concentration.However, from knowledge gained from further grid investi-gations, it was seen that the effect of accurately resolving themesh is much more important than solving the full geometry.Therefore, the use of the symmetry assumption is justified bythe fact that it allowed the use of a finer mesh for a moreaccurate solution within acceptable computational time. Itwas therefore decided to use the model applying bothsymmetry planes and dynamically steady flow and focus onobtaining a more accurate grid-independent solution.

Flow characteristicsThe characteristic flow quantities calculated from the physicaland numerical experiments are summarized in Table II.Additionally, the RTD curves for the numerical solution forthe three cases are shown in Figure 6.

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The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 359 ▲

Figure 6 – RTD curves obtained through numerical simulation for thebare, TI, TID, and TIHD cases

Figure 5 – Comparison of average residence time distributions for threedifferent numerical configurations: dynamically steady flow with twosymmetry planes, dynamically steady flow without symmetry, and fullytransient flow without symmetry

Physical and numerical modelling of a four-strand steelmaking tundish

It is immediately apparent from the results that theintroduction of the turbulence inhibitor offers advantagesover the use of the bare tundish, as reported by Kumar et al.(2008). This is due to the fact that the turbulence inhibitorprevents short-circuiting along the bottom of the tundish byforcing upward flow. This leads to a significant increase inthe minimum, peak, and mean residence times. Sincesufficient time is necessary for the flotation of inclusions bybuoyant forces to the slag, these increased residence timesenable more inclusions to be absorbed by the slag layer. Theresult is that the number and size of non-metallic inclusionsin the steel product will be reduced, providing a product ofhigher quality and value. Additionally, the higher plug flowvolume fraction will provide less turbulent flow, whichpromotes the rising of the inclusions in the melt, while thedecreased dead volume enhances thermal and chemicalhomogeneity in the product. Also, the upward-directed flowwill help to bring the inclusions into contact with the slag forabsorption. However, the primary purpose of this study wasto evaluate the benefit of including holes and low dams to theturbulence inhibitor. The purpose of the holes is to accelerateupward flow away from the inlet region, thereby helping toeliminate dead volumes.

Comparing RTD results for the physical and numericalexperiments using the adapted mesh in Table II, a goodcorrelation was reached for the calculated flow characteristics.The average difference in the properties calculated for thethree different configurations is approximately 10%. Thelargest difference, recorded for the breakthrough time in thebare model, is easily explained by the delays in input andmeasurement in the physical model, which becomesignificant for the very short minimum time. The other valuewhere large differences are detected is the peak time for thecases using a turbulence inhibitor. The numerical modelpredicts longer times for the peak concentration to bereached, which in turn translates into larger plug flowvolumes and lower dead volumes. What is of importance isthat despite these differences, general trends between thedifferent configurations are the same for the physical andnumerical model. The numerical model predicts the muchlower breakthrough time due to short-circuiting in the bareset-up, as well as the small amount of short-circuiting for thecase with the turbulence inhibitor with holes. Also, the baretundish clearly has much lower plug flow and larger deadvolumes than the configurations using turbulence inhibitors.It can therefore be concluded that the numerical model is

acceptable for predicting the flow properties of differenttundish configurations. This is particularly the case whenconsidering the assumptions employed to reduce thecomputational time: symmetry, dynamically steady flow, anda flat, frictionless surface.

A numerical simulation was also performed for the caseof a turbulence inhibitor with high dams (TIHD), where theheight of the dams was increased by 50% over the TID case.Comparison of the flow characteristic properties in Table IIshows that the higher dam is in fact able to prevent theshort-circuiting in the system, since the value of thedimensionless minimum residence time increases from 0.043to 0.084. Unfortunately, this improvement comes with a cost,as the peak residence time is significantly reduced and thedead volume is increased slightly. The likely reason for thisdeterioration of the flow quality is that the higher dam causeslarger dead volumes between the turbulence inhibitor and thedams, as well as behind the dams.

It is observed that the minimum residence time for theTID case is less than half of that observed for the TI case.Therefore, a fraction of the flow will spend a very short timein the tundish, allowing larger inclusions to reach the outletwithout being removed. In conclusion, it can therefore bestated that there does not appear to be any clear evidence thatthe use of holes and dams, in combination with a turbulenceinhibitor, offers any significant benefit over using only theturbulence inhibitor.

The short minimum residence time for the TID case ismost likely due to the dams being too low to completelyeliminate short-circuiting of the flow passing through theholes. It was suggested that it might be possible to eliminatethis problem by increasing the height of the dams. Industrialtrials would be required to determine whether the preventionof large inclusions passing through by short-circuiting in theTID case is worth the reduction in peak residence time andincrease in the dead volume when the height of the dams isincreased. However, the TI case still performs better, withoutthe added complication and cost of more complex flow controldevices.

As a final matter of interest, the amount of turbulencepredicted near the surface by the numerical model wasinvestigated for the bare, TI, and TID cases. The magnitude ofthe turbulence near the tundish surface is very important dueto the potential of slag entrainment. From previousdiscussions it is also obvious that the addition of theturbulence inhibitor influences the flow near the surface to a

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360 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table II

Comparison of flow properties calculated from physical and numerical experiments

Set-up Data source θmin θpeak θmean Vp Vd Vm

Bare Physical 0.0101 0.0670 0.687 0.039 0.370 0.591Numerical 0.0077 0.0732 0.705 0.040 0.375 0.585% difference 27.5 8.84 2.64 4.76 1.19 1.08

TI Physical 0.089 0.234 0.756 0.162 0.306 0.532Numerical 0.091 0.298 0.784 0.195 0.278 0.528% difference 1.89 24.1 3.62 18.5 9.61 0.915

TID Physical 0.035 0.291 0.747 0.163 0.299 0.538Numerical 0.043 0.326 0.790 0.184 0.259 0.557% difference 18.7 11.3 5.61 12.2 14.4 3.46

TIHD Numerical 0.084 0.279 0.784 0.181 0.279 0.540

large extent by introducing upward flow in the inlet region. Itis therefore necessary to evaluate the effect of the upwardflow on surface turbulence, because entrainment due toincreased turbulence may offset any benefits of longerresidence times and increased plug flow gained by theaddition of the turbulence inhibitor. It should be noted thatwithout a separate study to determine the dependency of slagentrainment on turbulence conditions, the discussion

regarding entrainment in this study is qualitative in nature.Nevertheless, the changes in flow along the longitudinalsymmetry plane can be observed in Figure 7, and it can beseen that the upward-directed flow away from the inlet doesincrease. Comparing the turbulence kinetic energy values ona plane 2 cm below the surface of the tundish in Figure 8, itcan be seen that the turbulence inhibitor changes the natureof the surface turbulence significantly. Firstly, it is noted that

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Figure 8 – Comparison of turbulence kinetic energy value near the surface of the tundish for (a) the bare tundish, (b) the TI configuration, and (c) the TIDconfiguration

Figure 7 – Comparison of velocity vector plots along the longitudinal symmetry plane for (a) the TI configuration and (b) the TID configuration

Physical and numerical modelling of a four-strand steelmaking tundish

regions with slightly more turbulence develop at the far endfrom the inlet in all three configurations due to a circulationformed at the surface after flow is turned back at the backwall. However, this discussion will focus on the area near theinlet, where more surface turbulence occurs. In the baretundish a high-turbulence region is formed near the sidewallsclose to the inlet due to the flow rising along the walls. Whenthe turbulence inhibitor is introduced, the high-turbulenceregion is centred on the inlet due to the rising flow from theturbulence inhibitor. It is important to note that the maximumvalue of turbulence (2.17×10−4 m2/s2 ) is significantlyincreased from the bare case (9.67×10−5 m2/s2). It istherefore recommended that surface turbulence should beconsidered when changing the design of a tundish to includea turbulence inhibitor. It would be valuable to study whetherthe turbulence generated near the surface would be sufficientto entrain slag droplets, either by physical or numericalmodelling.

Visual comparison between the TI and TID cases in Figure8 shows that the areas of higher turbulence expand veryslightly with the addition of the holes and dams, with theaverage turbulence kinetic energy increasing from 6.01×10−5 m2/s2 to 6.49×10−5 m2/s2. The most likelyexplanation for this increase is that due to the increasedstrength of the circulation pattern at the top of the tundish forthe TID case, more turbulence is generated when the flowrising from the inlet joins the flow from this circulationpattern.

ConclusionsA reduced-scale water model of a four-strand tundish wasdeveloped. The model was then used to perform RTDexperiments to determine the flow behaviour of threedifferent tundish configurations: a bare tundish with no flowcontrol devices, a tundish with a turbulence inhibitor, and atundish with both a turbulence inhibitor and a dam. Anumerical model was also developed to simulate thebehaviour of the water model to gain a clearer understandingof the physical model results. The numerical model wassuccessfully validated against the physical model results.

A bare tundish was proven to be insufficient in providinggood flow properties for tundish operation. Short minimum,peak, and mean residence times will limit inclusion removal,while a lack of strand similarity will reduce the quality of theproduct. High dead volumes will also prevent adequatemixing for homogenization and low plug flow volumes willlimit inclusion removal. Both configurations with turbulenceinhibitors were shown to decrease short-circuiting, decreasethe dead volume, increase the plug flow volume, and increasethe mean residence time. However, the addition of holes anddams to the configuration with only a turbulence inhibitorshows no clear improvement in flow properties. In fact, short-circuiting of the fluid passing through the holes decreases theminimum residence time and will allow larger inclusion toreach the mould. This short-circuiting could be prevented byincreasing the height of the dams, but this approach resultedin a lower peak residence time and slightly larger deadvolumes. It was determined that the surface-directed flowcaused by the turbulence inhibitor more than doubles themaximum turbulence kinetic energy near the surface of themelt, increasing the potential for slag entrainment. The

surface turbulence should therefore be considered duringturbulence inhibitor design.

AcknowledgementsThe authors wish to acknowledge NRF bursary funding for H.Cloete.

ReferencesAHUJA, R. and SAHAI, Y. 1986. Fluid flow and mixing of melt in steelmaking

tundishes. Ironmaking and Steelmaking, vol. 13. pp. 241–247.

ANSYS. ANSYS FLUENT Theory Guide Version. 14. 2011.

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362 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

IntroductionPeirce-Smith converters (PSCs) have been usedin the copper and PGM smelting industries formore than a century for the purpose ofremoving iron and sulphur through oxidationreactions to obtain blister copper and convertermatte respectively. This process step is referredto as conversion (Liow and Gray 1990; Real etal., 2007). The conversion process used inremoving iron and sulphur from matte is acomplex phenomenon involving phaseinteractions, many chemical reactions,associated heat generation, as well as productformation (Kyllo and Richards, 1998a). ThePSC is a cylindrical horizontal reactor (circularcanal geometry) where air at subsonic velocity(Mach < 1) is injected into matte throughsubmerged lateral tuyeres along the axis of theconverter (Gonzalez et al., 2008). Theconverting process is semi-continuous andautothermal. Since there are chemical reactionstaking place with products being formed,quality and quantity of mixing are important.

Mixing promotes chemical reactions, removingthe products from reaction sites, andminimizes temperature and the compositioninhomogeneities caused by cold solid additionsin the form of scrap, process ladle skulls,reverts, and fluxes, which are inherent to theconverting processes. Due to generation ofturbulence in the converter, mixing may alsoaid inclusion agglomeration, coalescence, andflotation of impurities, thus improvingconverter efficiencies (Gray et al., 1984).

Mixing is important in submergedpyrometallurgical gas injection systems andhas attracted much attention. Most research onmixing and injection phenomena in gas/liquidmultiphase systems has been conducted forsteelmaking and ladle metallurgy (Castillejosand Brimacombe, 1987; Kim and Fruehan,1987; Sahai and Guthrie, 1982; Sinha andMcNallan, 1985; Stapurewicz and Themelis,1987). Turkoglu and Farouk (1991) definedmixing intensity and efficiency in terms of thetime required to achieve a well-mixed bath –which is the time required, after theintroduction of tracer, for tracer concentrationat every nodal location in the system to reach avalue that varies by no more than ±5% .

Despite substantial PSC operationalexperience, there has been insufficientresearch on the process engineering aspects.Mixing and mass transfer in the converter arekey process parameters that have been studiedvery little. Due to the similarity of the basicconcepts in ladle injection and the PSC, thecore tenets of the work on ladle injection have

Modelling of fluid flow phenomena inPeirce-Smith copper converters andanalysis of combined blowing conceptby D.K. Chibwe*, G. Akdogan*, P. Taskinen†, andJ.J. Eksteen*‡

SynopsisThis investigation consists of a numerical and physical modelling exerciseon flow patterns, mixing, solid-liquid mass transfer, and slag-matte phasedistribution in a 0.2-scale cold model of an industrial Peirce-Smithconverter (PSC). Water, kerosene, air, and sintered benzoic acid compactswere used to simulate matte, slag, injected gas, and solid additions into thePSC. The 2D and 3D numerical simulations were carried out using volumeof fluid (VOF) and realizable k-ε (RKE) turbulence models to account forthe multiphase and turbulence nature of the flow respectively. Thesemodels were implemented using the commercial computational fluiddynamics numerical code FLUENT.

Numerical and physical simulations were able to predict, in agreement,the mixing and dispersion characteristics of the system in relation tovarious blowing conditions. Measurement of mass transfer indicated thatfluid flow in the PSC is stratified. Blowing configurations and slag volumeboth had significant effects on mixing propagation, wave formation, andsplashing.

As a potential process alternative to increase conversion efficiency, wepropose a combined blowing configuration using top lance and lateralnozzles. The numerical simulations were conducted on combined as well aslateral blowing conditions, and the results of the combined concept areencouraging.

KeywordsPeirce-Smith converter, combined blowing, CFD, mixing, splashing.

* University of Stellenbosch, Process EngineeringDepartment, South Africa.

† Aalto University, Department of Material scienceand Engineering, Finland.

‡ Curtin University, Department of MetallurgicalEngineering, Australia.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. This paperwas first presented at the, PyrometallurgicalModelling Principles and Practices, 4–5 August2014, Emperors Palace Hotel Casino ConventionResort, Johannesburg.

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ISSN:2411-9717/2015/v115/n5/a4http://dx.doi.org/10.17159/2411-9717/2015/v115n5a4

Modelling of fluid flow phenomena in Peirce-Smith copper converters

been adopted in research into process characterization of thePSC in an effort to address the challenges in productivity(Gray et al., 1984; Hoefele and Brimacombe, 1979; Vaarno etal., 1998). Physical and numerical models of the PSC havebeen developed to study multiphase fluid flow phenomena(Liow and Gray, 1990, Vaarno et al., 1998; Koohi et al.,2008; Ramirez-Argaez, 2008; Rosales et al., 2009; Valenciaet al., 2004, 2006). These models have been used inpyrometallurgical operations to establish functionalrelationships of process variables such as reaction kinetics(Kyllo and Richards, 1998b), injection dynamics (Schwarz,1996; Rosales et al., 1999; Valencia et al., 2002) and fluidflow behaviour (Han et al.,2001; Real et al., 2007; Valenciaet al., 2004). Despite the amount of numerical and experi-mental work on the fundamental phenomenon of multiphaseflow, little effort has been addressed to the understanding ofthe combined effect of blowing rates and the presence of aslag phase on the overall mixing performance of theconverter. If proper mixing is not achieved in the reactor, thefundamental consequences are chemical, thermal, andparticulate inhomogeneities, resulting in undesirablevariability in the final product composition.

PSC process reactions are highly exothermic in nature andhigh temperatures can result, depending on the grade ofcharged matte. It has become common practice to add coldflux and scrap, process reverts, and ladle skulls in order tocontrol the thermodynamics of the process. The solid-liquidmass transfer step may play an important role in the processperformance and attainment of thermal and chemical bathhomogeneity. The mechanism of dissolution of the coldadditions and behaviour of active sites within the converterare not well understood. Rates of dissolution can be assumedto affect the thermal state of the converter and hence to be afactor that affects the turnaround time of the converterprocessing. Establishing a stable functional state of theconverter, and fully developed categorization of flow fields, istherefore necessary for effective process control.

As mentioned above, the literature pertaining to solid-liquid mass transfer in ladle metallurgy is fairly compre-hensive. Despite this, no in-depth studies have been foundthat address this critical subject of solid-liquid interactions inPSCs. The only source close to the subject is the study byAdjei and Richards (1991), who investigated gas-phase masstransfer in a PSC using a physical model. Their work revealedpertinent information relating to oxygen utilization efficiencyin the converter.

In the PSC, the injected air has two main functions, whichare to supply oxidant and energy to stir the bath. Energy issupplied in three forms, namely kinetic, buoyancy, andexpansion. Energy and oxidant supply affect the chemicaland physical processes in the converter such as convertingrate, oxygen efficiency, dispersion of matte and slag, mixing,heat and mass transfer, slopping, splashing, and accretiongrowth (Haida and Brimacombe, 1985; Valencia et al., 2004).Again, little effort has been addressed to the understandingof the complex phase interactions of the three-phase systemin terms of volumetric dispersion in relation to the flowconditions presented by tuyere-specific power. Dispersion is asubject that needs further understanding as substantialamounts of valuable metal are lost due to entrapment; asituation that leads to the incorporation of slag-cleaning

systems in copper production circuits (Moreno et al., 1998;Warczok et al., 2004).

In this work, firstly the dependence of mixing onvolumetric air flow rate and simulated slag quantities fordifferent matte and slag levels is investigated using acombination of physical and numerical modelling. Secondly,we aimed at monitoring different regions in the PSC for solid-liquid mass transfer analysis. This was carried out throughcalculation of the localized turbulence characteristic and masstransfer coefficient. The dependence of these two masstransfer parameters on operating system variables such as airflow rate and the presence of a second phase (slag) wereinvestigated. Thirdly, we investigated the effect of volumetricgas flow rate in the dispersion and interaction of matte andslag phases in the PSC.

Due to the scarcity of quantitative research work to dateon PSCs, an overall strategy was devised to explain andevaluate experimental results using numerical simulation ofthe converter through computational fluid dynamics (CFD)software. Vaarno et al. (1998) and Valencia et al. (2004)evaluated the applicability of mathematical formulation to thePSC process using cold model experiments and establishedvelocity vector fields. In similar studies, Vaarno et al. (1998)and Valencia et al. (2004) investigated the influence of theFroude number on bath mixing, jet stability, and splashing ina PSC using mathematical formulation and cold modelexperiments. This work presents a first attempt to study thedispersion and interaction of phases in the PSC.

In order to attain our physical modelling objectives, a 0.2-scale water bath physical model with equivalent properties tothe generic industrial PSC used in copper smelters wasdesigned using similarity principles. Geometric, dynamic, andkinematic similarity criteria were used in the design forequivalency between prototype and model, sincehydrodynamic studies on fluid flow are not concerned withthermal and chemical similarity effects (Mazumdar, 1990).The modified Froude number, which represents fluid flowdominated by inertial and gravitational forces, was used fordynamic similarity. The molten liquid phases in the real PSC(matte and slag) were simulated in the model with water andkerosene respectively due to kinematic similarity.

In support of the physical modelling work, we also usedisothermal transient multiphase 2D and 3D CFD numericalsimulations. The CFD numerical code FLUENT software wasused to solve the transient Navier-Stokes equations. Therealizable k−ε turbulent model and volume of fluid (VOF)method were used to model the turbulence nature andmultiphase flow respectively.

Experimental methods

Physical model descriptionThe physical experiments for mixing, mass transfer, andphase dispersion measurements were conducted in a 0.2-scale PVC water bath model as shown in Figure 1. Apolyvinyl chloride 2.5 inch cylindrical manifold served as areservoir for compressed air at a constant line pressuresupply of 5.5 bar. An inline VPFlowMate digital mass flowmeter, which uses the thermal mass flow principle, was usedto measure the volumetric flow rate of compressed air intothe model. The flow meter was powered with a low-voltage

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364 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

limited current power source. This water model also formedthe basis for the numerical simulations.

Similarity using dimensionless numbers is the keyfeature in the development of physical models. In the designprocess, geometry, kinematic, and dynamic similarities wereobserved through consideration of dimensionless numbers.Geometric similarity was observed using a scale factor on allphysical dimensions, and dynamic similarity achievedthrough the modified Froude number, N∗Fr, which resemblesfluid flow dominated by inertial and gravitational forces.Kinematic similarity was observed between the PSC andmodel through the Morton number, NMo, which incorporatessurface tensions, viscosities, and densities of the fluids.Modified Froude and Morton numbers are given in Equations[1] and [2] respectively:

[1]

[2]

where vt (m s-1) is the tuyere tip exit velocity, ρl (kg m-3) isthe liquid density, ρg (kg m-3) is the gas density, g (m s-2) isthe gravitational acceleration, do (m) is the tuyere diameter, μ(Pa s) is the dynamic viscosity, and σ (N m-1) is the surfacetension of liquid.

The condition of similarities yielded the dimensions,blowing conditions, and fluid physical properties assummarized in Table I.

For mixing time measurements, a tracer dispersiontechnique was used where sulphuric acid was injected in thecentre of the model at 100 mm below the water (simulatedmatte) level and monitored by a pH meter placed directlyopposite the tracer injection point at 100 mm from theconverter circular wall. The midpoint of the bath was takenas the tracer injection point for simulation of the converterinputs charging point, which is situated in the centre of anindustrial PSC. Figure 1 shows the tracer and pH meterpositioning as used in this experimental set-up. Water wasfilled to a total constant height of 270 mm, which is 39%filling capacity. Kerosene was used to simulate the slag layer.

The kerosene–to-water height ratio was varied from 0% to40% at five equidistant intervals. Air volumetric flow ratewas varied from 0.00875 to 0.01375 N m3 s-1, whichrepresents a typical scaled-down industrial operation range.A matrix of 25 experiments was designed and eachexperiment was repeated five times under the sameconditions. An average mixing time was taken, which waswithin 10% standard deviation on all experimentalconditions. The response was defined as the time taken toachieve uniform and homogeneous steady-state concen-tration of the bath after introducing a tracer. Decay in pHconcentration to a value ±0.01 pH units represented 99%mixing in this work. In the numerical simulations for mixingtime measurements, a region was adapted in the samelocation as the tracer injection point in the physicalexperiments where acid was patched with a volume fractionof 1. A custom field function was formulated at the positionanalogous to the pH position, measuring the mole fraction oftracer species concentration as a function of flow time. Thiswas achieved through solving the species transport equation.Mixing was considered complete when the species concen-tration reached a stable value.

For solid-liquid mass transfer experiments, the benzoicacid cylindrical samples were 81 mm long and 38 mmdiameter on average. To promote radial dissolution andminimize the end effects, the samples were enclosed betweentwo thin mild steel washers on both ends. The benzoic acidcompacts were mounted to a steel grid fastened with threadedrod. A total of eight samples were inserted in the convertermodel at a predetermined depth for every experimental run,as shown in Figure 2. The sample labelling convention usedhere shows sample number and submergence referencedfrom the converter bottom. Due to the shallow matte depthrelative to the sample lengths, only two sample depths wereconsidered in these experiments, namely 50 mm (H50) and90 mm (H90) from converter bottom respectively. Thesamples were introduced and immersed into the water bathafter the air volumetric flow rate had reached a steady-statevalue within ±1% of the required value and the simulatedslag thickness had been added. The samples were simulta-

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Table I

Industrial PSC and the model: fluid physicalproperties, dimensions, and blowing conditions

Similarity Dimension Industrial ModelPSC

Geometric Converter length (mm) 9140 1000Converter inner diameter (mm) 3460 690Number of tuyeres 42 7

Dynamic Volumetric flow rate (N m3 s-1) 7.55 0.0113Tuyere air velocity (m s-1) 138.5 30Modified Froude number 12.45 12.45

Kinematic Dynamic viscosity (Pa s) 0.01 (matte) 0.0009 (water)

Kinematic viscosity (m2 s-1) 0.000 002 0.000 001Liquid density (kg m-3) 4600 1000Surface tension (N m-1) 0.93 0.0728Slag density (kg m-3) 3300 774Slag/ matte density ratio 0.717 0.775Morton number 2.65 × 10-11 2.65 × 10-11Operating temperature (K) 1473 293

Figure 1 – Schematic view of 0.2-scale water bath model showing tracerand pH probe arrangement as used in the mixing experiments

Modelling of fluid flow phenomena in Peirce-Smith copper converters

neously subjected to four cycles of 900 seconds’ treatment,during which they were removed at intervals, thoroughlydried, and weighed. The weight loss was converted intoequivalent radii so as to calculate mass transfer coefficients.The air flow rates varied from to 0.0025 to 0.01125 N m3 s-1.Two simulated slag thicknesses, 54 mm and 108 mm, wereused in the experiments, representing 20% and 40% ofsimulated matte height respectively. These two simulated slagvolumes will be referred to as ‘low simulated slag’ and ‘highsimulated slag’ volumes in the text.

In the phase dispersion measurements, the physicalmodel was filled with water and kerosene to a total height of285 mm, which is 41% filling capacity. The kerosene-to-water height ratio was kept at 0.267. Air volumetric flow ratewas varied from 0.0085 to 0.0142 N m3 s-1 with five levelsrepresenting 75, 90, 100, 110, and 125% of the typicalequivalency model volumetric flow rate of 0.0113 N m3 s-1.For a specific experimental set-up run, at the end ofexperiment all syringes positioned at relevant samplingpoints as shown in Figure 3 were pulled at once and thecontents were poured into measuring cylinders. The emulsionin the measuring cylinders were given sufficient time forcomplete phase separation, and the volumes of water andkerosene were read directly. Dispersed phase hold-up wascalculated as the volumetric percentage of slag or matte withrespect to the total volume of emulsion at a certain plane. Onaverage, 20 ml of emulsion per sampling point was taken forevery run.

Numerical model description2D and 3D numerical simulations were carried out based onthe 0.2-scale water slice model of a PSC. The computationaldomain was discretized into small control surfaces/volumes

(for 2D/3D) for the calculations. Very fine meshes arenecessary to capture accurately the flow pattern. In this work,a multi-size variable mesh was used. Fine mesh elementswere employed in the matte-slag domain with the free airregion having elements approximately three times larger.Modelling was done on an Intel® Core ™ i7 CPU with 3.46GHz processor and 8.0 GB installed RAM. The commercialCFD code ANSYS FLUENT, version 13.0, was used for thecalculations on a high-erformance computing (HPC) clusterwith an installed capacity of eight 2.83 GHz processors pernode with 16 GB of RAM. The 2D and 3D domain computa-tional grids were made up of 26 492 Map/Pave quad and 313529 hexahedral elements respectively. About 99.97% and98.86% for 2D and 3D elements respectively had an equisizeskewness of less than 0.4, which translate to good meshquality, necessary for an accurate and converged solution.

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366 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 2 – Benzoic acid samples and top and side view of the spatial placements in the converter model

Figure 3 – Schematic side view of model showing sampling depths

In order to account for the multiphase nature of the flow,the VOF model was used. The interfacial behaviour of air,matte, and slag was captured by this model using acompressive discretization scheme. This is accomplished bysurface tracking of the phase interfaces in the systemthrough solution of the VOF continuity equation. In themodel, the different phases are treated numerically asinterpenetrating continua, thus inevitably introducing theconcept of phasic volume fraction where the volume fractionsin each computational cell sums to unity. The effects ofturbulence on the flow field inside the model wereincorporated by using the realizable k-ε (RKE) model.

The flow conservation governing equations, the VOFequation, and turbulence model equations were solved withFLUENT version 13.0. This package is a finite volume solverusing body-fitted computational grids. A coupled algorithmwas used for pressure-velocity coupling. A CompressiveInterface Capturing Scheme for Arbitrary Meshes (CICSAM)discretization was used to obtain face fluxes when thecomputational cell is near the interface, using a piecewise-linear approach. This scheme was necessary due to the highviscosity ratios involved in this flow problem. A time step of0.0001 seconds was used and found to be sufficient formaintenance of numerical convergence at every time step andstability. Convergence of the numerical solution wasdetermined based on surface monitoring of integratedquantities of bulk flow velocity and turbulence, and scaledresiduals of continuity, x-, y-, and z-velocities, k , and ε. Theresiduals of all quantities were set to 0.001 and the solutionwas considered converged when all the residuals were lessthan or equal to the set value.

In the numerical simulations for mixing timemeasurements, a region was adapted in the same location asthe tracer injection point in the physical experiments whereacid was patched with a volume fraction of 1. A custom fieldfunction was formulated at the position analogous to the pHprobe position, measuring the mole fraction of tracer speciesconcentration as a function of flow time through solving thespecies transport equation. Mixing was considered completewhen the species concentration reached a stable value. Forthe simulation of the phase distribution, a single airvolumetric flow rate (0.0113 N m3 s-1) was used in thetransient 3D simulation. This verification was done bycomparing the contours, measured on two planes (S, A), of

the water bath with contours of the volume fraction of mattein slag at the same plane at different volumetric flow rates.

Results and discussion

MixingMixing time was found to decrease with increasing specificmixing power for the cases with thin simulated slagthickness. For a relatively thick simulated slag layer, themixing time increased with an increase in the specific powerof mixing; this is consistent with the results obtained byValencia et al. (2004), who reported that an increase in airpower generated more turbulence in the converter, with littlebenefit in terms of mixing quality in the mean flow of thebath. Figure 4 shows turbulence kinetic energy vector plotsobtained in our study for air flow rates of 0.01125 N m3 s-1

and 0.01375 N m3 s-1 at constant slag thickness of 54 mm. Itis evident from Figure 4(b) that at high blowing rates, highturbulence is created and concentrated in the tuyere region,as compared to low blowing rates shown in Figure 4(a). Thisphenomenon results in longer mixing times for the high slagthickness, but slightly shorter mixing times for low slagthickness, as shown in Figure 5.

From the results of the physical experiments, mixing timein terms of total specific mixing power (buoyancy plus gaskinetic energy) was analysed for 27 mm and 108 mmsimulated slag thicknesses representing low slag and highslag operations respectively (Figure 5). The shorter mixingtimes obtained for low slag thickness and longer mixingtimes for higher slag thickness are attributed to thegeneration of increased turbulence in the converter, with littlebenefit in terms of mixing quality in the mean flow of thebath liquid.

Numerical simulations revealed that with thin simulatedslag thicknesses, the slag is pushed to the opposite side ofthe tuyere line with the plume region being composed ofalmost only matte, as shown in Figure 6(a). This increaseshydrodynamic pressure to the rising bubbles and henceincreases the specific energy dissipated to the liquid phase forbath recirculation. This is due to high bubble retention in theliquid, which in turn increases mixing efficiency. However,the benefits of such retention time are offset by the effects ofphase interaction, friction, and diffusion, which dissipate asubstantial amount of energy at high slag volumes. The

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Figure – 4 Turbulence kinetic energy vector plots for (a) 0.01125 N m3 s-1 and (b) 0.01375 N m3 s-1 air flow rate with 54 mm slag thickness

Modelling of fluid flow phenomena in Peirce-Smith copper converters

mechanism of momentum transfer at simulated matte(simulated) slag-air interfaces fritters away potential recircu-lation energy. At an increased simulated slag thickness of108 mm, as can be seen in Figure 6(b), the effect ofinteraction and dispersion is highly pronounced. As such,mixing in the simulated matte phase is expected to decrease.The effectiveness of the interphase exchange momentum isalso reduced due to dissipation of energy by the simulatedslag as a result of localized secondary recirculation flow,which is more pronounced at high simulated slag volumes.This observation is in agreement with the results reported byHan et al. (2001) on flow characteristics of a gas-stirred ladlemodel.

As indicated by Turkoglu and Farouk (1991), liquid bulkcirculation rate is inversely proportional to mixing time,which indicates that the bulk motion of the liquid plays animportant role in mixing, and hence that liquid recirculationrate can be used as a measure of mixing efficiency. Figure 7shows the variation of average bulk velocity and turbulencekinetic energy with simulated slag thickness. Average bulkvelocity and turbulent kinetic energy were calculated as theaverages in infinite sampling points in the simulated mattecalculation domain. It can be seen from Figure 7 that at 54

mm simulated slag thickness and above, the bulk recircu-lation velocity is greatly reduced. Moreover, turbulence wasobserved to decrease with increasing simulated slag height.Both these factors translate into increased mixing time.

In an effort to understand whether increased mixing timein the multiphase system was due to phase interactions,mixing time numerical simulations were also conducted withequivalent heights of matte only and matte plus simulatedslag, of which simulated slag was 108 mm. Numericalsimulations with only simulated matte depth displayedimproved mixing efficiency, as shown in Figure 8, wheremixing time is seen to decrease from 168 seconds (with slag)to 153 seconds (no slag). This could be attributed toimproved momentum transfer between gas bubbles and thebulk liquid due to a high gas retention time, as well as theabsence of energy dissipation in recirculation flow, and henceincreased mixing efficiencies.

It is possible to postulate that when the melt height in thePSC is generally low, the gas channels though the melt alongthe vertical sidewall of the tuyere injection nozzle axis. Inthat case, the residence time of the gas bubbles in the melt isreduced, which in turn will reduce gas-melt interactionswithin the bulk melt. Therefore, as a result of channelling,the effectiveness of the gas momentum and power transfer tothe bulk liquid flow is reduced. This adversely affects themixing, liquid-liquid, and liquid-solid mass transfer withinthe bath. On the other hand, with an increase in liquid heightin the converter, the axial plume residence time increases,which results in improved interaction between the gas andliquid. This will lead to more matte entrainment into therising plume and a stronger agitation in the bath. In order tomaintain consistent mixing power and offset the adverseconditions due to increased volumes of rising liquids, thebath height with respect to matte and slag ratio should bemonitored in order to make necessary adjustments to the gasblowing rates for energy-efficient processing.

Solid-liquid mass transfer Pyrometallurgical processes are multiphase in nature,involving gas-liquid-solid interactions. In PSC operation, the

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Figure 6 – 2D density contour plots with 54 mm (a) and 108 mm (b) simulated slag thickness at 0.01125 N m3 s-1

Figure 5 – Correlation between slag thickness, mixing time, and specificmixing power

addition of cold solids to the liquid matte in the form offluxing agents (silica sands) for slag liquidity and processscrap and reverts for temperature control is a commonpractice. It is reasonable to propose that with such practice,the solid-liquid mass transfer step may play an important rolein the performance of the process and attainment of liquidbath homogeneity. In this work, solid additions weresimulated with sintered benzoic acid compacts spatiallypositioned in the model converter. Water and kerosene wereused to simulate matte and slag respectively. Solid-liquidmass transfer was characterized by experimentallydetermined mass transfer coefficient values for benzoic acidsintered compacts and calculated dimensionless turbulencecharacteristic values. Two simulated slag thickness, 54 mmand 108 mm, were used in the experiments, representing20% and 40% of simulated matte height respectively. Thesetwo simulated slag volumes will be referred to as ‘low’ and‘high’ simulated slag.

The experimental results revealed that the dissolutionvaried with respect to all air flow rates and simulated slagvolumes considered in this study. In Figure 9, S2 lies in thesame location as S3 with respect to tuyere position but close

to the simulated slag-matte interface. The dissolution ratewas higher at S2 than at S3. On the other hand, sample S6,which is also near the simulated slag-matte interface,exhibited the second-highest dissolution rate ahead of S5.These observations indicate that there exists a circulatoryand stratified flow regime in which the velocity flow variablediffers with respect to the depth of the samples in thesimulated liquids. This observation is consistent with earlierwork by Vaarno et al., (1998), who measured experimentallyand numerically liquid velocity distributions in a water modelof a PSC. Their study identified a circulatory flow field in theconverter with higher velocities near the bath surface. In thecurrent work, this phenomenon is further attested to by thebehaviour of S7 and S8, with S8 experiencing higherdissolution rates than S7. S1 and S4 also confirmed flowstratification, with higher dissolution being experienced atS4, which is near the slag-matte interface. It is alsoinstructive to notice that both S1 and S4 had lowerdissolution rates compared to S2, S3, S5, and S6. Thisobservation serves to highlight that S1 and S4 are positionedin dead zones near the converter sidewalls in the model.

Two trends of dissolution behaviour were observed interms of K values as a function of simulated slag thicknessand air flow rate. With low slag thickness, as air flow rateincreased from 0.00875 N m3 s-1 to 0.01122 N m3 s-1, Kincreased, then decreased towards 0.01375 N m3 s-1 but stillremained higher than at 0.00875 N m3 s-1, as shown inFigure 10. This is possibly due to an increase in thefragmented body forces between the sample and emulsion,which increases the transport process as air flow rateincreases. However, the observed decrease in mass transfervalues as the air flow is increased further to 0.01375 N m3 s-1

is possibly due to shallow submergence of the tuyeres, withchannelling phenomena becoming prevalent at high air flowrates (Adjei and Richards, 1991). Channelling will cause abreakdown of energy transfer to the system, hence areduction in the transport process which results in theobserved decrease in transport variable.

With high simulated slag thicknesses, and hence highslag volumes (Figure 11), we observed a decrease in values

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Figure 8 – Numerical mixing time results for (a) 270 mm matte and 108 mm simulated slag thickness case and (b) equivalent total simulated matte depth of378 mm at air volumetric flow rate of 0.01125 N m3 s-1

Figure 7 – Variation of average simulated matte bulk flow velocity andturbulence kinetic energy as a function of simulated slag thickness at0.01125 N m3 s-1

Modelling of fluid flow phenomena in Peirce-Smith copper converters

of K as air flow was increased from 0.00875 N m3 s-1 to0.01122 N m3 s-1. It is possible that at these high slagvolumes, phase interactions and interphase friction arestrongly pronounced – so much so that fragmented bodyforces between the sample and emulsion are weakened,thereby retarding the transport process. A noticeable increasein the mass transfer parameters was observed with anincrease in air flow to 0.01375 N m3 s-1. This could be aresult of increased energy input to the system, the situationbeing sustained by slightly deeper tuyere submergencecaused by the high slag volume, which allows for effectiveexchange of momentum between air and liquid. With theabove facts in mind, it should be noted that the mass transfervalues are still lower than those at 0.01122 N m3 s-1 with alow slag thickness (Figure 10). This scenario depicts under-utilization of capacity in terms of energy at high simulatedslag volumes.

Phase dispersion Experimentally measured dispersed phase holdup (Dph) atdifferent planes in the water bath (Figure 12) was verifiednumerically with contours of volume fraction (VF) ofsimulated matte in simulated slag and simulated slag insimulated matte phase in the S and A planes respectively at0.0113 N m3 s-1 (Figure 13). The results revealed that theamount of dispersed simulated matte in simulated slag phasein the model increases with increasing air volumetric flowrate. Conversely, it has been observed that the averageamount of dispersed simulated slag in matte decreases. Thissituation could be attributable to the effects of increasedsplashing in the converter as air volumetric flow rateincreases. According to Koohi et al. (2008), the splashes inthe PSC are mainly matte constituents as slag is pushed tothe radial position (Figure 6) opposite the tuyere side,forming a plume of matte. Splashes will disperse the matteout of the converter and those with insufficient kinetic energywill fall back onto the slag in the bath, resulting in increasedmatte entrapment in the slag layer (Figure 13, S-plane).

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370 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 9 – Sample radius decay with time at 0.01125 N m3 s-1 with 54 mm simulated slag thickness

Figure 10 – Variation of mass transfer coefficients with air flow rate atlow simulated slag thickness

Figure 11 – Variation of mass transfer coefficients with air flow rate athigh simulated slag thickness

Numerical analysis of combined top and lateralblowingAs discussed in the previous sections, typical currentoperation of lateral-blown PSCs results in the commonphenomenon of splashing and slopping due to air injection.The splashing and wave motion in the converter causes metallosses and potential lost production time due to the necessityfor intermittent cleaning of the converter mouth, and thusreduced process throughput. Against such background, thepurpose of this section is to report simulation results ofcombined blowing in a PSC using CFD and to compare theresults of mixing propagation, turbulent kinetic energy, andsplashing with the conventional common practice. This wasdone by creating four different sliced PSC models. The firsttwo models represent the conventional practice, with tuyereslocated at a lateral position on the converter. The slag layerthickness will be different for these two models, representinglow and high slag volumes. The other two models also havedissimilar slag layer heights, but in these cases air will beinjected from a combined top and lateral position.Understanding the effect of combined top and lateral blowingcould help identify whether combined top and lateral blowingis feasible for industrial usage, and possibly lead toalternative technologies for increased process efficiency inindustrial PSCs.

In this work, 2D and 3D simulations were carried outbased on slice models of a typical industrial copper PSC. Due tothe mesh densities involved in these simulations, simulation ofthe entire converter was not feasible. Table II gives thedimensions, parameters for tuyere configurations, and bath(matte and slag) heights of the four different slice modelsbased on the typical industrial converter considered in thisstudy. LS1, LS2, CS1, and CS2 refer to lateral (L) blowing andcombined (C) blowing models with low slag thickness (S1) andhigh slag thickness (S2) respectively. The dimensions and

blowing parameters used correspond to those commonlyemployed in industry for lateral-blown PSCs. The velocity ofthe top-blowing air was taken to be the same as that for thelateral-injected air, as the aim of the study is to observe theeffects of different locations of blowing and variance in slaglayer height, and not the effects of inlet velocity.

The position of the top-blown lance above the moltenliquids in the converter is very important. In the presentinvestigation, the height and alignment of the top-blowinglance were selected after considering different sources in theliterature regarding the position of lances for molten baths.For instance, Marcuson et al. (1993) mentioned that thelance was susceptible to degradation and recommend a lanceheight of 400 mm above the molten bath to ensure a high gasvelocity at impact. In this study, the top-blowing lance waspositioned 500 mm above the matte.

The flow conservation governing equations, the VOFequation, and turbulence model equations were solved withFLUENT version 14.0. A Semi-Implicit Method for Pressure-Linked Equation (SIMPLE) algorithm was used for pressure-velocity coupling. A Compressive Interface Capturing Schemefor Arbitrary Meshes (CICSAM) discretization method wasused to obtain face fluxes, with a piecewise-linear approach.This scheme was necessary due to the high viscosity ratiosinvolved in this flow problem (ANSYS, 2011). A time step of0.0001 seconds was used and was found to be sufficient formaintenance of numerical convergence and stability at everytime step. Convergence of the numerical solution wasdetermined based on surface monitoring of integratedquantities of bulk flow velocity, turbulence, and scaledresiduals of continuity such as the x-, y-, z-velocitycomponents, k, and ε. The residuals of all quantities were setto 1×10-3 and the solution was considered converged whenall the residuals were less than or equal to the set value.

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Figure 12 – Dph contours of simulated matte in simulated slag on thesampling plane S at different air volumetric flow rates

Figure 13 – Contours of measured Dph compared with numerical VFcontours in the S and A planes

Modelling of fluid flow phenomena in Peirce-Smith copper converters

The solutions for the numerical simulations, based ondensity contour distribution of the liquids, are given in Figure14 for the four models developed. In the analysis of theseisosurfaces created in the middle of the models normal to thez-direction, it can qualitatively be observed that spitting andsplashing originate from the tuyere side of the converter in allcases. This is due to the rupture of large bubbles generatedfrom the tuyeres as they exit the liquid bath at the liquidsurface. In the process, stable waves are created with slagbeing pushed to the sidewalls opposite the tuyere, leaving aplume generally consisting of matte phase.

It was also observed that there is an increase in mattevelocity with combined blowing at high slag conditions,indicating the critical influence of lance height for combinedblowing. In contrast, at high slag volume (272 mm slagthickness), it can be observed in Figure 14(b) that the airfrom the top lance has a pronounced effect on the wave path,creating a vortex on impact with the bath surface. In thisinstance, the distance from tuyere tip to bath surface isapproximately 364 mm. This observation is consistent withthe recommendation by Marcuson et al. (1993) for optimallance tip to bath surface distance.

Turbulence kinetic energy distribution in gas-stirredsystems is one of the important parameters influencing themixing efficiency. It can be seen from Figure 15(a) that theaddition of top-lance blowing increases the turbulence kineticenergy in the converter free space above the liquid. In theliquid bath, turbulence kinetic energy remains relatively thesame, and thus we expect a similar overall oxidation rate inthe two systems under consideration. However, it could bereasoned that the increased turbulence kinetic energy in theconverter free space could speed up the fall of splash dropletsback to the liquid bath surface. To further illustrate the effectof slag thickness, and thus the importance of tuyere tip-bathsurface distance, velocity vector plots for 136 mm (S1) and272 mm (S2) slag thickness are shown in Figure 15(b). It isevident that the average bulk velocity is high in the case oflow slag thickness, especially in the air free space due to

underdeveloped impact of the air as a result of high tuyere tipto bath surface distance.

This phenomenon can be further illustrated by comparingthe average bulk velocity in the converter for all four models.Figure 16 illustrates the average matte bulk velocity profilesfor lateral and combined blowing. Through observation onecould infer that there is an increase in matte velocity withincreased slag height, thus indicating the critical aspect oflance height for combined blowing. This is in contrast to lowslag height models, which show almost equal matte velocitiesdue to the ineffectiveness of jet penetration at a high lanceheight.

Implications for combined top and lateral blowing onindustrial-scale convertersPSC conversion is a batch operation and meticulous control ofslag and matte volumes is currently not possible. However, inprevious studies (Han et al., 2001) it has been observed thathigh slag volumes dissipate substantial amounts of energythat would otherwise be used for recirculation and improvedbath mixing. From this study, it is quite evident that there isan incentive for combined blowing as it provides energy thatimproves recirculation of the liquid bath. The bath to lance tipheight, which affects the amount of impact energy to the bathas previous reported by Marcuson et al. (1993), is critical tothe operation of combined blowing.

ConclusionsIn this study, the influence of a simulated slag layer onmixing and phase dispersion characteristics and behaviour inan industrial Peirce-Smith converter (PSC) was studiedexperimentally, using a 0.2-scale water model, andnumerically by means of 2D and 3D simulations. The resultsfrom numerical simulation with volume of fluid (VOF) andrealizable turbulence model were found to be in goodagreement with the experimental results. The experimentaland numerical analysis results of phase distribution were infair agreement. There appears to be a critical simulated slag

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Figure 14 – Density contour plots for (a) lateral and (b) combined blowing for S1 and S2 models

thickness in the PSC model, above which increasing air flowrate results in extended mixing times due to a combination ofchannelling and secondary recirculation in the slag layer.Secondary recirculation results in dissipation of energy,leading to a reduction in the bulk fluid recirculation velocityand turbulence kinetic energy. An increased matte fraction inslag and matte systems increases mixing efficiencies,possibly due to high bubble retention. It has been shown thatthe slag layer, as well as air flow rates, has an influence onthe bulk recirculation velocity and turbulence, thus affectingthe mixing efficiency in the PSC. The dispersion of simulatedmatte in simulated slag has been found to increase withincreasing air volumetric flow rate, whereas the dispersion ofsimulated slag in simulated matte decreases. The difference isthought to be due to the complex interaction of phases interms of precipitation mechanisms, coagulation, andflotation, as well as fluid motion, in the converter. Theexperimental results were in good agreement with thenumerical simulation results in the domain of the experi-mental set-up.

Solid-liquid mass transfer phenomena were alsoinvestigated experimentally using the cold model with theobjective of spatial mapping the converter regions. The flowpattern in PSCs was found to be stratified, with high bathvelocities near the bath surface. Both air flow rate and slagquantities affect dissolution behaviour in slag-mattesystems. The solid-liquid mass transfer rates can beeffectively controlled by close monitoring of slag quantitiesand air flow rates. Dead zones associated with poordissolution rates were observed close to the sidewalls of theconverter.

As a potential process alternative to prevent metal/mattelosses due to splashing and wave motion in the converters,and hence to increase process efficiency, we studiedcombined blowing configuration in an industrial PSC with atop lance and lateral nozzles by using the 3D numericalsimulations. The results revealed that wave formation andsplashing can be reduced by employing combined blowing.Qualitative analysis of density contour plots suggests thatcombined blowing will most likely result in increasedprocess efficiency as the energy of the top-lance injected airis utilized in reacting with new surfaces and increasing thestatic pressure in the system, thereby decreasing theamplitude of standing waves and thus increasing mixingefficiency, and hence the process efficiency, in the bulkliquid bath. The study also clearly demonstrated thatcombined blowing increases turbulence in the bath, and isthus likely to increase process throughput. A quantitativecomparison of the average bulk flow liquid velocitydemonstrated that simulated slag layer thickness has agreat effect on the bulk recirculation velocity, as itinfluences the utilization of energy from the top-blownlance, thus increasing mixing efficiency. The optimalposition for the lance above the liquid bath is thus critical.As the bath height varies during the blowing cycle, it mightbe necessary to meticulously vary the lance height duringthe blowing cycle to maintain a critical height.

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Figure 15 – (a) Turbulent kinetic energy for lateral (L) and combined (C) blowing, and (b) velocity vector plots for low slag (S1) and high slag (S2) thicknessesin combined blowing models

Figure 16 – Average matte bulk velocity for lateral and combinedblowing for S1 and S2 models

Modelling of fluid flow phenomena in Peirce-Smith copper converters

AcknowledgementsThe financial support received from NRF/THRIP funding isgreatly appreciated. The authors also extend their thanks tothe technical staff in the Stellenbosch University ProcessEngineering workshop.

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Table II

Model dimensions of different blowing configurations

Parameter Typical PSC Model LS1 Model LS2 Model CS1 Model CS2

Air inlet velocity (m s-1) 136 136 136 136 136Blowing configuration Lateral Lateral Lateral Combined CombinedSlag layer thickness (mm) 136 136 272 136 272Matte height (mm) 1360 1360 1360 1360 1360Combined matte and slag height (mm) 1496 1496 1632 1496 1632Number of tuyeres 42 1 1 2 2Diameter inside refractory (mm) 3460 3460 3460 3460 3460Length inside refractory (mm) 9140 217 217 217 217Tuyere diameter (mm) 41 41 41 41 41

IntroductionSouth Africa is the world’s leading supplier ofplatinum group metals (PGMs). Most of thePGMs are contained in the Merensky and UG2reefs of the Bushveld Complex, where they areassociated with nickel- and copper-bearingminerals. In Merensky ore, the major base-metal sulphide mineral is pyrrhotite, withpentlandite, chalcopyrite, pyrite, and minoramount of other sulphides also present. InUG2 (Upper Group 2) ore the major base-metalsulphide is pentlandite. Pyrrhotite is found inmoderate amounts, and millerite and pyrite inminor amounts. Because of lower miningcosts, platinum mining is becoming moreUG2–based, and the resulting concentratescontain high levels of chromite unless blendedwith Merensky ore. It is therefore critical thatnew and improved extraction methods bedeveloped and exploited. The methods used inthe recovery of the PGMs from these oresconsist of physical concentration techniques,pyrometallurgical processing, and hydrometal-lurgical extraction of the base metals followed

by the PGMs (Jones and Kotze, 2004; Nell,2004).

During pyrometallurgical processing, thenickel-copper concentrates from the mill-floatconcentration step are smelted to bring aboutphysical and chemical changes that enablerecovery of base metals, PGMs, and othervaluable metals in crude form. In general, theidea is to melt the concentrate in a furnace toproduce a matte, which contains all thesulphides, below a carefully maintained slaglayer. This matte, which contains largeamounts of iron and sulphur, is oxidized in aPeirce-Smith converter to lower the iron andsulphur levels, while at the same timeincreasing the PGM grade. Conventional PGMmatte smelting essentially requires thepresence of a certain quantity of base metalsulphides in order to collect the PGMs in amolten sulphidic phase in the smeltingfurnace. However, the quantity of chromeoxide in the feed materials (particularly UG2)need to be strictly controlled to avoid thebuild-up of high-melting chromite spinels(Jones and Kotze, 2004; Nell., 2004).

Currently, the matte-based collectionprocess is most widely used for PGM recovery,but because PGM-containing concentrates arebecoming more enriched with UG2 concen-trates, it is expected to be integrated with orreplaced by an alternative processes. Mintekhas developed an alternative process forsmelting PGM-containing oxide feed materialsthat contain low sulphur levels, and often highlevels of chromium oxide, known as theConroast process. The process involves thesmelting of dead-roasted sulphide concentrates

The recovery of platinum group metalsfrom low-grade concentrates to an ironalloy using silicon carbide as reductant by W. Malan*, G. Akdogan*, S. Bradshaw*, and G.A. Bezuidenhout†

SynopsisThe purpose of the study was to investigate the feasibility of SiC reductionof low-grade concentrates from Lonmin’s Rowland and Easternsoperations with respect to metal fall and PGM recovery. These concen-trates are rich in SiO2 and MgO with low concentrations of chalcopyriteand Cr2O3. Pd is the most abundant of the PGMs. SiC reduction of sampleswas conducted at 1600℃ with 2.5–3.5 kg SiC per 100 kg concentrate.

PGM recoveries for Easterns concentrate were better than for Rowland.More than 85% of the Ir and Pd and almost 60% of the Pt were recoveredwith 3.5 kg SiC per 100 kg concentrate. SEM of slag samples showed littleentrainment of metallic prills compared to Rowland samples. This wasattributed to the relatively higher melt viscosities of the Rowlandconcentrate. In order to decrease slag viscosity and to enhance PGMrecovery, the FeO content of the Easterns concentrate was increased withthe addition of 10 kg converter slag per 100 kg concentrate. Ir and Pdrecoveries were increased to about 95%, while Pt recovery was around70%. On the basis of these results an optimum feed ratio betweenEasterns and Rowland concentrates and converter slag is proposed.Carbothermic reduction of the optimum charge was also compared to SiCreduction. Carbothermic reduction yielded a marginally higher metal fall;however, the calculated gas emissions and energy requirements werehigher than for SiC reduction. KeywordsSiC reduction, PGM recovery, LG concentrates, FactSage modelling.

* University of Stellenbosch Process EngineeringDepartment, South Africa.

† Lonmin Western Platinum Limited, ProcessDivision, Marikana, South Africa.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. This paperwas first presented at the, PyrometallurgicalModelling Principles and Practices, 4–5 August2014, Emperors Palace Hotel Casino ConventionResort, Johannesburg.

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ISSN:2411-9717/2015/v115/n5/a5http://dx.doi.org/10.17159/2411-9717/2015/v115n5a5

The recovery of platinum group metals from low-grade concentrates to an iron alloy

in order to generate a small amount of an iron alloy in whichPGMs and base metals are collected. A slag is also produced,which contains mostly unwanted materials and very lowlevels of residual PGMs. The desired degree of reduction iscontrolled by adjusting the carbon addition. Highly reducingconditions promote high recoveries of the valuable metals tothe alloy, but high iron collection dilutes the PGM grade ofthe alloy, which affects the PGM recoveries in the convertingand refining processes (Jones and Kotze, 2004; Nell J., 2004).

In this study, low-grade (LG) concentrates from Lonmin’sRowland and Easterns operations were smelted with SiC asreductant. The effect of process parameters such as reductantto concentrate ratio, temperature, and different reductants onmetal fall, alloy composition, slag composition, and gascomposition were investigated. FactSage modelling was alsoused to simulate the reduction process. Detailed chemical andmineralogical characterization of the feed, alloy, and slag wasconducted by X-ray diffraction (XRD), X-ray fluorescence(XRF), scanning electron microscopy (SEM), and inductivelycoupled plasma (ICP) analysis.

The primary objectives of this study includeunderstanding the reducing conditions to produce theminimum amount of alloy while maintaining a high aspossible recovery of PGMs; quantifying the deportment of thevarious elements to the alloy and slag phases, andestablishing the factors affecting the recovery of PGMs; andcomparing the gas emissions from different reductants interms of environmental impact. The benefits of the newprocess would include no constrain on the minimum quantityof base metals required in the feed material, as the PGMs arecollected in an iron-based alloy; ability to integrate therelatively large quantities of the alloy product into an existingsmelter complex, possibly through a converter; allowing forthe possibility of hydrometallurgical refining of the alloy;chromium tolerance; efficient PGM collection; fewer sulphuremissions compared to current matte-smelting processes; andpossibly an economic incentive for treating other wastematerials in a similar manner.

Materials and methods A Carbolite STF 1800 tube furnace with a programmableCarbolite controller was used for the experiments. Thealumina tube was heated by five surrounding lanthanumelements. A set point of 1600°C was set on the controller andthe measured temperature recorded by a thermocouplelocated in the furnace. The initial plan was to attach acrucible to a wire and then suspend it from the top of thefurnace at the tube outlet and lower it to the marked hot spot.However, in order to facilitate the operation, the furnace wasremoved from its original supporting brackets and fitted ontoa fork-lifter. After the adjustment, the furnace could easily bemoved up and down in vertical direction. The movement wascarefully controlled by a hydraulic lever or foot pedal(Figure 1). After the furnace had reached the set temperature,the MgO crucible containing the sample mixture was placedinto the tube furnace. Before the sample was placed into thehot zone, high-purity argon was allowed to purge into the Alwork tube at a flow rate of 5 l/min for 20 minutes. Thesample was then raised to the hot zone, and the Ar flow ratewas reduced to about 600 ml/min at the start of theexperiment. After a predetermined time of reduction, the

furnace was raised and the sample was allowed to coolrapidly under an Ar flow rate of 5 l/min for about 10minutes.

The screen analysis showed that P80 values for Easternsconcentrate, Rowlands concentrate, and SiC reductant were320 μm, 180 μm, and 380 μm respectively. The chemicalcompositions of the LG concentrates are given in Tables I and II.

Results and discussion

FactSage simulationsInitial FactSage simulations were conducted prior tolaboratory-scale experiments in order to establish a range ofoptimum conditions. Thermodynamic equilibrium results aregiven in 100 kg concentrate per mass of reductant. The

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376 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 1 – A Carbolite STF 1800 tube furnace with controller after beingmodified with a fork-lifter. (1) Fork-lift, (2) argon gas inlet, (3) gas outletwith extraction fan, (4) temperature controller

Table I

XRF results for Rowland and Easterns LG concentrates

Constituent Rowland LG concentrate Easterns LG concentrate(wt %) (wt %)

Al2O3 5.41 5.00CaO 2.72 3.03Cr2O3 2.94 4.31Fe2O3 * 12.73 14.24K2O 0.10 0.10MgO 22.79 23.05MnO 0.15 0.18Na2O 0.38 0.30P2O5 0.02 0.02SiO2 49.40 47.81TiO2 0.31 0.32LOI * 3.08 2.32Total 100.05 100.66

* LOI includes the total volatiles content of the rock (including thewater combined in the lattice of silicate minerals) and the gain onignition related to the oxidation of the rock (mostly due to Fe), Fe2O3

converted to FeO.

stoichiometric amount of SiC required per 100 kg of LGconcentrate is 0.27 kg for Rowland and 0.38 kg for Easterns.These were determined as the minimum amounts of SiCrequired to produce a molten metal phase, primarily being Fe(> 98%), given the starting oxide concentration and ease ofreduction. Experimental results are then compared withFactSage results and discussed. Without any reductant in thesystem, MgCr2O4(s) spinel accumulates, therefore a reductantmass of 3 kg SiC per 100 kg LG concentrate was chosen as abase case. During the prediction of optimum temperaturerange, the requirement was that Fe concentrations should behigh, while Cr and Si should remain low. The respectiveliquidus temperatures during SiC reduction are 1420°C forRowland and 1410°C for Easterns concentrate. The resultsindicated that Fe and Cr concentrations in the alloy shouldnot be significantly affected when the temperature increasedfrom 1400°C to 1650°C, while Si concentrations wouldincrease. The solubility of C in the alloy decreases withincreasing temperature. The metal fall is not significantlyaffected, however, and about 1600°C should yield a goodmetal fall and avoid formation of solid phases like MgSiO3(s)proto-enstatite. Therefore 1600°C was selected as thetemperature at which to investigate the SiC reduction of theLG concentrates.

The effect of reductant to concentrate ratio wasinvestigated from 0.5 kg to 4 kg SiC per 100 kg LG

concentrate. It was found that an increase in reductant masswill cause more oxides, particularly FeO, to reduce, therebyincreasing metal fall. For both concentrates a molten metalphase starts to form when 0.5 kg of reductant is present per100 kg concentrate, emphasizing the ease of reduction ofboth concentrates, and almost all Cr2O3 is reduced to CrO,thereby increasing the solubility of Cr in the slag phase. Themetal fall continues to rise and it therefore becomes easier torecover PGMs. Better metal fall for reductive smelting ofEasterns LG concentrate is expected at higher reductant toconcentrate ratios, due to the marginally higher FeO concen-tration. The molten metal phase consists primarily of Fe andother easily reduced base metals; however, some Cr and Sideport to the metal phase once the SiC to concentrate ratio isincreased. Si and Cr start to dissolve in the molten metal atsignificant concentrations when the reductant mass exceeds3 kg SiC per 100 kg LG concentrate. C concentrations in thealloy also become more significant at a higher SiC toconcentrate ratio. Cr solubility in the slag phase is greatlyaffected by the oxidation state of Cr (Nell, 2004; Jones,2009). Chrome in its trivalent state (spinel) has a very lowsolubility in the slag phase – divalent chrome, in contrast, ishighly miscible in the slag phase (Nell, 2004). The FactSagesimulation in Figure 2 shows how Cr is reduced from Cr(III)to Cr(II) as function of SiC additions to Easterns LGconcentrate charge.

The recovery of platinum group metals from low-grade concentrates to an iron alloy

377The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

Table II

ICP-MS results for PGM values (wt %) in the LG concentrates

Au % Ir % Pd % Pt % Rh % Ru %

Rowland LG concentrate <0.005 0.057 0.10 0.059 <0.005 <0.005Easterns LG concentrate <0.005 0.064 0.11 0.083 <0.005 <0.005

Figure 2 – The distribution of chromium between the phases as a function of SiC additions to Easterns LG concentrate

The recovery of platinum group metals from low-grade concentrates to an iron alloy

Experimental resultsAn alloy from each experiment was separated from the slagphase and weighed. The percentage metal fall for eachexperiment was determined as the amount of alloy formedper amount of initial concentrate. The results showed thatmetal fall was increased by a longer residence time (from 60minutes to 180 minutes) and greater reductant to concentrateratio. Some thermogravimetric analysis (TGA) experimentswere also conducted to investigate the reaction mechanismsand kinetics. The alumina pedestal with the attached cruciblecontaining the LG concentrate charge and SiC reductant wereplaced on a scale, which was connected to a computer with aVGA cable to acquire the change in mass of the concentratecharge. It was found that the mass of the crucible, refractorysupport material, and alumina pedestal remained constantthroughout the experiment. Therefore the only change inmass was that of the concentrate and reductant charge in agas-tight furnace. The mass changes on the scale were notedevery 0.5 seconds on the computer, and typical graphs ofmass loss vs time were constructed. The results showed thatno significant mass changes occurred after about 10 minutesof reduction time, indicating that equilibrium was reacheddue to the reduction of iron oxides, given their relatively highconcentration in the original concentrates. It is known thatthe iron oxide, FexOx, will be reduced before the base metaloxide, CrxOx (Chakraborty et al. 2010; Haque and Ray,1995). The results from thermodynamic modelling alsoagreed with this finding that FeO reduction needs to be nearcompletion for CR2O3 reduction to commence. If it is assumedthat Fe and Cr make up the bulk of the alloy, given therelatively low concentration of Ni- and Cu-bearing minerals,the reactions will take place in the following order:

3FeO(s) + SiC(s) → 3Fe(l) + SiO2(l) – CO(g) [1]

Cr2O3(s) + SiC(s) → 2Cr(l) + SiO2(l) – CO(g) [2]

Andrews (2008) also concluded that a longer residencetime will improve the kinetics of matte droplet settling.Furthermore, increasing the amount of SiC causes more FeOto be reduced to Fe, causing more metal prills to form andincreasing the probability of coalescence. This, once again,agrees well with previous work (Shahrokhi and Shaw, 2000;Saffman and Turner, 1956; Ammann et al., 1979). Thechemical compositions of the alloys obtained from SiCreduction of Rowland and Easterns LG concentrate are shownin Tables III and IV respectively.

Fe forms the bulk of the overall composition of all of thealloys from SiC reduction of Rowland LG concentrate. Thiscorrespond well with work done by Perry et al. (1988) andQayyam et al. (1976), who also concluded that reduction ofFeO will take place before the reduction of CrO. Cr concen-trations increase sharply when the reductant to concentrateratio is increased. When the reductant to concentrate ratio is3.5 kg SiC per 100 kg Rowland LG concentrate, Cr constitutesalmost 10% in the alloy. Cr concentrations are more than 5%in alloys from tests with different reduction times. These highconcentrations of Cr could cause difficulties duringdownstream processing, particularly during converting and inthe base metals and precious metals refineries. Ni and Sconcentrations seem to increase with residence time. Thesetwo elements most likely originate from the mineralpentlandite. Pt and Pd concentrations remain very low in allof the alloys.

Easterns LG concentrate has a higher FeO content thanRowland LG concentrate, which explains the higher Fe contentof alloy samples produced from reduction of Easterns LGconcentrate. The same observation was made from theFactSage modelling. The low Cr and Si concentrations in the

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378 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table IV

Alloy composition (by ICP-MS) from SiC reduction of Easterns LG concentrate as a function of reductantaddition and reduction time (wt %)

Reduction Fe Cr Si Cu Ni S Ir Pt Pdtime (min)

2.5 180 81.2 0.24 <0.01 3.67 <0.01 <0.01 0.34 0.02 0.083.0 180 84.5 1.47 0.05 1.47 4.49 2.04 0.8 0.52 1.243.5 180 88.6 1.33 0.15 1.08 2.81 1.52 0.8 0.65 1.26

SiCConcentrate ratio(x102)

Table III

Alloy composition (by ICP-MS) from SiC reduction of Rowland LG concentrate as a function of reductantaddition and reduction time (wt %)

Reduction Fe Cr Si Cu Ni S Ir Pt Pdtime (min)

2.5 60 81.2 0.24 <0.01 3.67 <0.01 <0.01 0.05 0.02 0.083.0 30 67.6 8.81 <0.01 2.65 <0.01 0.01 0.04 0.01 0.083.0 45 71.7 9.36 <0.01 2.36 <0.01 <0.01 0.06 0.01 0.063.0 60 81.4 2.72 <0.01 2.08 <0.01 <0.01 0.05 0.01 0.043.0 75 76.4 5.9 <0.01 2.25 <0.01 <0.01 0.05 0.01 0.063.0 90 75.1 6.12 <0.01 2.67 4.42 1.11 0.04 0.07 0.043.0 180 74.9 6.35 <0.01 2.71 4.51 1.13 0.05 0.08 0.063.5 60 65.1 9.59 0.01 2.02 <0.01 0.01 0.04 <0.01 0.05

SiCConcentrate ratio(x102)

alloy are very favourable for downstream processing becauseof the difficulties in removing these elements during convertingand refining. The Pt and Pd concentrations are higher in alloysfrom SiC reduction of Easterns LG concentrate. The better metalfall is the most likely factor to have contributed to the higherPGM concentrations in these alloys. PGM recoveries (Ir, Pd,and Pt) to the alloy phase for the respective concentrates areshown in Table V and Table VI.

From Table V it is seen that the recoveries of Ir, Pt, andPd are very low. The highest Pt and Pd recoveries weresachieved at a reductant to concentrate ratio of 3 kg SiC per100 kg Rowland LG concentrate and residence time of 180minutes. One factor that may contribute significantly to thelow PGM recoveries is slag viscosity. The recoveries of Ir, Pd,and Pt from SiC reduction of Easterns LG concentrate are

significantly better (Table VI). Ir, Pd, and Pt recoveriesincrease significantly when the quantity of reductant isincreased. Overall, Ir has the highest recovery, followed by Pdand Pt. The very low Cr and Si concentrations in these alloyswill allow for a further increase in reductant quantity andtherefore it should be possible to recover more than 90% ofPGMs by SiC reduction of Easterns LG concentrate.

The slag compositions from the SiC reduction of Rowlandand Easterns LG concentrates are shown in Tables VII andVIII respectively.

The results from Tables VII and VIII show a significantdecrease in FeO concentration compared with the initialconcentrates. The lowest FeO content corresponded to theexperiments with the highest quantity of reductant. Thecomposition of the slag has a major effect on the viscosity,

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The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 379 ▲

Table V

Recovery of Ir, Pd, and Pt from SiC reduction of Rowland LG concentrate

Reduction time (min) Ir recovery (%) Pd recovery (%) Pt recovery (%)

2.5 60 2.410 2.198 0.9313.0 75 4.449 3.233 0.863.0 90 3.957 2.637 6.693.0 180 3.816 3.516 7.5143.5 60 4.082 2.905 0.989

SiCConcentrate ratio(x102)

Table VI

Recovery % of Ir, Pd, and Pt from SiC reduction of Easterns LG concentrate

Reduction time (min) Ir recovery (%) Pd recovery (%) Pt recovery (%)

2.5 180 11.232 1.538 0.5093.0 180 69.728 62.882 34.953.5 180 94.536 86.629 59.227

SiCConcentrate ratio(x102)

Table VII

Slag composition (by XRF) from SiC reduction of Rowland LG concentrate

Reduction time Al2O3 CaO CrO* FeO* MgO SiO2 TiO2 Total

(min)

2.5 60 5.2 2.67 2.25 5.87 27.60 52.17 0.33 97.543.0 75 5.22 2.72 2.43 3.30 28.35 52.17 0.32 96.723.0 90 5.23 2.70 2.34 3.07 28.21 52.11 0.33 97.123.0 180 5.22 2.69 2.32 3.03 28.11 52.09 0.33 96.913.5 60 5.22 2.69 1.61 2.97 28.74 54.01 0.33 97.72

SiCConcentrate ratio(x102)

Table VIII

Slag composition (by XRF) from SiC reduction of Easterns LG concentrate

Reduction time Al2O3 CaO CrO* FeO* MgO SiO2 TiO2 Total

(min)

2.5 180 4.75 3.08 3.11 6.51 29.11 50.17 0.33 97.613.0 180 4.71 3.1 3.09 5.84 30.06 49 0.33 97.613.5 180 4.78 3.12 3.15 3.12 29.78 51.65 0.33 97.10

SiCConcentrate ratio(x102)

The recovery of platinum group metals from low-grade concentrates to an iron alloy

and FeO, NiO, CuO, and CrO will all aid in lowering theviscosity. Once the SiC comes in contact with these oxides, itwill reduce and settle to join the alloy phase, therebyremoving them from the slag phase and increasing theviscosity. It can be seen that the reduction process loweredthe FeO concentration compared with the initial concentrate(refer to Table I).

The viscosity module in FactSage was used to determinethe viscosities at 1600°C. At a temperature of 1600°C it canbe safely assumed that the slag is molten. The calculatedviscosities of the slags from SiC reduction of Rowland andEasterns LG concentrate at the beginning of the melts are3.74 and 3.06 poise, respectively. The significantly higherslag viscosity for Rowland LG concentrate would make itmore difficult for small metal prills to settle and coalesce.

Table IX shows viscosities of the slags at the end of themelt. The viscosities were calculated in the FactSage viscositymodule from the slag compositions in Table VII and Table VIII.

Table IX shows that the calculated viscosities of the slagsfrom all the tests are higher than the calculated viscosities atthe beginning of the melts. This is because the bulk of the Fehas partitioned into the alloy. Some Cr has also deported tothe alloy phase. The viscosities of slags from SiC reduction of

Rowland LG concentrate are significantly higher than slagsfrom SiC reduction of Easterns LG concentrate. This couldexplain why the PGM recoveries from Rowland LGconcentrate are significantly lower.

Viscosities of PGM smelting slags are reported in theliterature (Eric, 2004; Eric and Hejja, 1995) as being in therange 1.5–4 poise, which could hinder the settling of particles15 μm or smaller in size. The FeO/SiO2 ratio of the slagseems to have a major influence on the viscosity. TheRowland LG concentrate under investigation has a lowerFeO/SiO2 ratio than Easterns LG concentrate. When FeO isreduced, slag viscosity will increase and valuable metal prillswill not settle. The high initial slag viscosity will contribute toinsufficient coalescence of metal prills and phase separation.The corresponding viscosities from these experimentsconducted with Rowland LG concentrate fall mostly above therange of 1.5–4 poise. It therefore seems that high slagviscosities are the primary reason for the low PGM recoveries.Slag viscosities from SiC reduction of Easterns LG concentratedo, however, fall in the recommended range. The lowerviscosities will aid in the coalescence of metal prills, enhancephase separation, and induce good PGM recovery.

Figures 3 and 4 are SEM images of the slag phases fromSiC reduction of Rowland and Easterns LG concentrate

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380 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table IX

Slag viscosity at the end of the melt

Concentrate Reduction time (min) Viscosity (poise)

Rowland 2.5 60 3.781Rowland 3.0 75 3.992Rowland 3.0 90 4.070Rowland 3.0 180 4.107Rowland 3.5 60 4.435Easterns 2.5 180 3.280Easterns 3.0 180 3.165Easterns 3.5 180 3.438

SiCConcentrate ratio(x102)

Figure 3 – SEM image of slag phase (darker) containing metallic prills(bright spots) from Rowland LG concentrate. Smelting time 180 min,reductant to concentrate ratio 3 kg SiC per 100 kg concentrate

Figure 4 – SEM image of the slag from Eastern LG concentrate.Smelting time 180 min, reductant to concentrate ratio 3 kg SiC per 100kg concentrate

respectively, from experiments that had the same reductant toconcentrate ratio and reduction time.

Figure 3 shows a SEM image of the slag phase from SiCreduction of Rowland LG concentrate. Metal prills areentrained in the slag. These metal prills are very small andare most likely trapped in the slag phase and will never settleor coalesce with other metal prills of similar size. Studies inthe literature indicate that many small metal prills (< 2 μm)are expected never to settle under the force of gravity alone.This phenomenon agrees well with the work done byFagurland and Jalkanen (1999) and Poggie et al. (1969),who also reported that very small droplets may either settlevery slowly or may be trapped as ‘rafts’ of droplets floatingon the top surface of the slag or as droplets suspended belowsmall gas bubbles. However, the high viscosity of the slag ismost likely restricting the coalescence and settling of metalprills. An EDX spot analysis on the metal prill indicated bythe arrow detected Ir and Pd.

Figure 4 shows a SEM image of the slag phase from SiCreduction of Easterns LG concentrate. The slag phase issmoother, with no metal prills entrained. This is an indicationthat metal prills coalesced and settled well within the slagphase. From these findings it is very clear that the lower slagviscosity contributes significantly to PGM recovery from SiCreduction of Easterns LG concentrates. This corresponds tosimilar findings in the literature (Shahrokhi and Shaw, 2000;Saffman and Turner, 1956; Ammann et al., 1979).

From the results obtained experimentally, it wasconcluded that Easterns LG concentrate should form the bulkof the dry LG concentrate feed to a DC arc furnace, since ithas a higher FeO/SiO2 ratio, which is significant in reducingslag viscosity and increasing overall PGM recovery. The slagviscosities need to be kept in the region of approximately 3.2poise (average viscosity determined from experiments withSiC reduction of Easterns LG concentrate) for acceptable PGMrecoveries. Rowland and Easterns concentrates could becombined, with the main fraction being Easterns, or anothersource added that would lower slag viscosity. The slag from aPeirce-Smith converter has a high FeO/SiO2 ratio and couldbe used to reduce the slag viscosity. FactSage was used to

model such a scenario and an experiment was also conductedto back up the results. FactSage modelling was conducted at3.5 kg SiC per100 kg Easterns LG concentrate at 1600°C withvarying amounts of converter slag additions.

Figure 5 shows that addition of converter slag doesdecrease the slag viscosity. The converter slag is rich in FeOand thereby the FeO/SiO2 ratio in the slag is increased,causing the slag viscosity to decrease. However, the converterslag contains about 30 wt% SiO2, which could act to increasethe slag viscosity. Figure 5 shows that an increase inreductant will cause more FeO to be reduced, consequently asystem with 5 kg and 10 kg converter slag additions willhave a higher slag viscosity than a system with no converterslag if the reductant additions are increased past the point ofintersection. Another critical point is that the conductivityand viscosity of the slag are inversely related. A decrease inviscosity will increase the conductivity of the slag; increasingiron oxide serves to depolymerize the slag and creates sitesfor electronic conduction (Nell, 2004; Eric, 2004). It is clearfrom the findings that around 10 kg of converter slagaddition per 100 kg concentrate would decrease slag viscositywithout increasing the total quantity of slag significantly. Thealloy is also expected to have a good PGM grade. A test wastherefore conducted to compare the results from experimentsand FactSage modelling for similar conditions.

From Table X, it is apparent that FactSage predicts a slagwith similar composition. The marginally lower FeO/SiO2ratio in the slag from FactSage modelling will cause theviscosity to be somewhat higher than the slag from theexperiment. Table X also shows an improved PGM recoverywith converter slag addition, which is likely to be a result ofthe decrease in slag viscosity. The Pd and Ir recoveries arenearly 100%, compared to Pd and Ir recoveries of 94.5 and86.6% obtained experimentally with no slag addition. Ptrecovery has also improved, from 59.2% to almost 70%. Ptrecovery is expected to improve if the metal fall increases.More reductant could be added to increase metal fall andthereby increase the Pt recovery from the initial concentrate.Overall, this is this is a significant finding. A portion ofconverter slag can therefore be recycled and re-melted

The recovery of platinum group metals from low-grade concentrates to an iron alloy

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 381 ▲

Figure 5 – Slag viscosity from SiC reduction of Easterns LG concentrate as a function of reductant mass and converter slag mass

The recovery of platinum group metals from low-grade concentrates to an iron alloy

together with any LG concentrate to increase metal fall andPGM recoveries.

FactSage modelling was used to predict an optimum ratiobetween Rowland and Easterns LG concentrates. Thereductant to concentrate ratio was fixed at 3.5 kg SiC per 100kg LG concentrate and the temperature at 1600°C. FactSagepredicted that adding more than 30 kg converter slag per 100kg LG concentrate decreases the alloy to slag ratio. Thereforea 0.7–0.8 fraction of Easterns LG concentrate in the feed and20–30 kg converter slag per 100 kg LG concentrate shouldnot significantly increase furnace slag quantities and theFeO/SiO2 ratio should be adequate to sustain a slag viscosityclose to 3.5 poise. At these operating conditions more than90% of the PGMs should be recovered from a LG concentratefeed in an alloy with a high PGM grade. Cr and Si concen-trations in the alloy are less than 1% in total, and C concen-trations are less than 1%.

FactSage modelling was also undertaken to compare SiCreduction with carbon reduction. The optimum feedconditions, namely 75% Easterns LG concentrate, 25%Rowland LG concentrate, 25 kg converter slag per 100 kg LGconcentrate, and 0–4 kg reductant per 100 kg LG concentrate

at 1600°C were used in the comparison. With theseconditions, it is assumed that slag viscosity will be lowenough to ensure good phase separation and PGM recovery.The results indicated that C reduction of a LG concentratecharge results in a marginally higher metal fall. The alloycompositions were very similar and only small differences inFe, Cr, and Si concentrations were noted. Gas emissions andenergy requirements are higher for C reduction, arguably dueto C reacting endothermically with FeO to produce Fe(l) andCO(g) in contrast to SiC reacting exothermically with FeO toproduce Fe(l), SiO2(l) and CO(g).

However, it must be borne in mind that SiC is not anaturally occurring compound and is manufactured bycarbothermic reduction of silica at high temperatures, whichentails its own high level of emissions which should be addedto the emissions from SiC smelting. A preliminary FactSageanalysis was conducted for the reaction to produce SiC:

SiO2(s) + 3C(s) → SiC(s) + 2CO(g) (+ O2 → 2CO2) [3]

The resulting estimates of energy requirements and COemissions per ton of SiC product are given in Table XI.

It is seen from Table XI that not all of the SiO2 and C has

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382 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table X

Effect of adding converter slag – comparison between experimental results and FactSage modelling

Experimental with slag FactSage modelling with slag

Metal fall % 8.82 Metal fall % 10.5Alloy wt % Alloy wt %

Fe 85.3 Fe 95.687Cr 4.13 Cr 1.172Si 2.73 Si 0.0565Cu 1.10 Cu 0.982Ni 2.59 Ni 1.95S 1.53 S Not included in feedIr 0.81 Ir Not included in feed

Pd 1.21 Pd Not included in feedPt 0.65 Pt Not included in feed

Slag wt % Slag wt %Al2O3 4.4 Al2O3 5.358CaO 2.95 CaO 3.154CrO 3.88 CrO 3.891FeO 7.657 FeO 6.809MgO 28.85 MgO 23.997SiO2 49.75 SiO2 56.588TiO2 0.32 TiO2 Not included in feedAlloy Recovery % Alloy Recovery %

Ir 97.867 Ir 100 %Pd 97.040 Pd 100 %Pt 69.086 Pt 100 %

Table XI

Thermodynamic analysis of stoichiometric feed of SiO2 and C to produce 1 ton of SiC

Temperature (°C) SiC product produced (kg) CO emissions (kg) ΔH – energy required (MJ)

1600 987.6 1388.19 19837.951800 979.9 1382.94 20481.992000 969.9 1376.23 21138.512200 954.8 1368.26 21843.212500 834.9 1354.35 24040.73

reacted to form SiC. Thermodynamic modelling predicts that asmall fraction of the SiO2 will be converted to SiO(g), leavingsome unreacted C(s) in the system. Overall more than 1.3 t ofCO (g) is emitted to produce 1 t of SiC. Moreover, the energyrequired to produce the given SiC quantities will contribute tooverall energy requirements for recovery of PGMs from aniron alloy. This needs to be taken into consideration from anenvironmental and economic point of view when consideringa reductive smelting route. It was, however, determined thatonly approximately 35 kg SiC per ton of LG concentrate isrequired to recover most of the PGMs. It must also be takeninto consideration that these PGMs need to be extracted froma more enriched LG concentrate feed, and reductive smeltingis the only feasible option at the present time.

ConclusionsThe feasibility of using SiC as reductant for Rowland andEasterns LG concentrates was investigated with respect tometal fall, PGM grade in the alloy, slag composition, and PGMrecovery, using small-scale experiments in combination withFactSage modelling. The findings can be summarized asfollows.

➤ SiC reduction of Easterns LG concentrate resulted insignificantly better metal fall and PGM recoveriescompared with Rowland concentrates. At a reductant toconcentrate ratio of 3.5 kg SiC per 100 kg Easterns LGconcentrate, more than 85% of Ir and Pd wererecovered, and more than 60% of Pt. The higherFeO/SiO2 ratio of Easterns LG concentrate andconsequent lower slag viscosity is most likely to havecontributed to the improvement of recoveries. SEMimages from a slag from SiC reduction of Easterns LGconcentrate showed no entrained metal prills,indicating that most metal prills had coalesced andsettled. Cr and Si concentrations were below 2% in totalin all alloys

➤ In order to improve the PGM recoveries, the FeOcontent of the initial charge had to be increased. Peirce-Smith converter slag from Lonmin was used as anaddition to increase FeO/SiO2 ratio. The experimentsresulted in more than 95% recoveries for Ir and Pd,together with about 70% for Pt. FactSage modellingpredicted that Easterns LG concentrate should make up70–80% of the LG concentrate charge and 20–30 kgconverter slag be added per 100 kg LG concentrate toattain relatively lower viscosities for phase separation.It is expected that PGM recoveries of more than 90%should be obtained from the LG concentrate, inconjunction with a good PGM grade in the alloy

➤ Using the optimum LG concentrate charge, theeffectiveness of SiC as a reductant was also comparedto that of C through thermodynamic modelling. Creduction of a LG concentrate charge led to amarginally higher metal fall at the same reductant toconcentrate ratio compared with SiC reduction. Thealloy composition was very similar and only smalldifferences in Fe, Cr, and Si concentrations were noted.Gas emissions and energy requirements were higherfor C reduction of a LG concentrate charge.

➤ From the evidence, it is clear that SiC reduction seems a

reasonably attractive alternative to carbon-basedreductants. Therefore, integrating this process into thepresent matte-based collection flow sheet could beconsidered as a future alternative for smelting LGconcentrates. However, it must be borne in mind thatSiC is not a natural compound and it should bemanufactured by again carbothermic reduction of silicaat high temperatures with its own level of emissions.This obviously will require further investigation.

AcknowledgementsThe authors wish to thank Lonmin Plc for the financing ofthis work.

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AMMANN, P.R., KIM J.J., and LOOSE, T.A. 1979. The Kennecott Process for nickelslag cleaning. Journal of Metals, vol. 1, no. 2. pp. 20–25.

PERRY, K.P.D., FINN, C.W.P., and KING, R.P. 1988. An ionic diffusion mechanismof chromite reduction. Metallurgical Transactions B, vol. 19B. pp.677–684.

QAYYAM, M.A. and REEVE, D.A.A. 1976. Reduction of chromites to spongeferrochromium in methane-hydrogen mixtures. Canadian MetallurgicalQuarterly, vol. 15, no. 3. pp. 193–200. ◆

The recovery of platinum group metals from low-grade concentrates to an iron alloy

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 383 ▲

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IntroductionApproximately 90% of all TiO2 extracted fromtitanium-bearing minerals is used to producewhite pigment (TZMI, 2012). A significantportion of this pigment is produced throughthe chloride process. This involves chlorinationof TiO2 feedstocks such as natural andsynthetic rutile, ilmenite, and high-titania slagin a fluidized-bed reactor to produce TiCl4,which is subsequently purified and oxidized toproduce TiO2. The chloride process hasstringent feedstock quality specifications toensure that it can be operated in a stable andeconomical manner.

As a major TiO2 feedstock producer,Exxaro Heavy Minerals (now part of Tronox)needed to gain a thorough understanding ofhow their products would behave in theircustomers’ chlorination reactors. Thisunderstanding would firstly assist in ensuringthat an acceptable product is produced.Secondly, it would make it possible to gaugethe value of the products in the hands ofcustomers. To improve this understanding, itwas decided to develop a techno-economicmodel of the chloride process that could beused to study the influence of different

feedstock characteristics on the performance ofthe pigment production process.

Process analysisThis section presents details of the processanalysis done with the purpose of collectinginformation that could be used as the basis ofthe modelling work.

Process descriptionThe purpose of the chloride TiO2 pigmentproduction process is to extract the maximumamount of titanium from the TiO2-containingfeed material in the form of titanium dioxide,while rejecting as much of the impurities (e.g.Ca, Mg, Si, Al, etc.) as possible to the wastestreams, based on differences in the phasetransition temperatures of different metalchlorides (Table I). An overview of the processis shown in Figure 1. The process consists ofthe following five stages (Lee 1991).

➤ Chlorination. This first stage is the focusof this paper. Chlorination converts feedmaterials to a solid waste stream and acrude liquid stream containing most ofthe titanium as TiCl4

➤ Purification. The crude TiCl4 produced inchlorination contains a wide range ofimpurities, which include solid iron andmanganese chlorides, and unreacted ore,coke, and silicates, as well as solublevanadium. Vanadium is converted to aninsoluble chloride, and all the solidimpurities are removed by vaporizingthe TiCl4 and condensing it again

➤ Oxidation. Liquid TiCl4 from purificationis vaporized and reacted with pre-heatedoxygen to produce TiO2 and chlorine(Equation [1]). Chemicals are added for

Value-in-use model for chlorination oftitania feedstocksby S. Maharajh*, J. Muller*, and J.H. Zietsman†

SynopsisIn the chlorination process for TiO2 pigment production, blends of titaniafeedstocks such as ilmenite, synthetic rutile (SR), natural rutile, upgradedslag, and chloride-grade slag are reacted with coke and chlorine at atemperature of around 1000°C to form TiCl4 (the main product) and otherwaste metal chlorides. The TiCl4 is the main feed material for the TiO2pigment-making process. Feeding different titania materials to thechlorinator affects the amount of coke and chlorine required for theprocess, the amount of waste generated, waste disposal costs, the amountof TiCl4 produced, and bed build-up rates. These factors influence thevalue of the feedstock. Generally, a higher TiO2 feedstock is more valuedsince less waste is generated and less reagents are consumed. To quantifythe impact of different feedstocks on the chlorinator, a techno-economicmodel was developed to describe the chlorination process and estimateprocess variables at steady state. This paper describes the development ofthe model and studies in which the model has been used to quantifyeffects of using different feedstocks.

Keywordstitania feedstock, chlorination, process modelling TiCl4, value-in-use.

* Exxaro Resources, Pretoria, South Africa.† University of Pretoria, Pretoria, South Africa.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. This paperwas first presented at the, PyrometallurgicalModelling Principles and Practices, 4–5 August2014, Emperors Palace Hotel Casino ConventionResort, Johannesburg.

385The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

ISSN:2411-9717/2015/v115/n5/a6http://dx.doi.org/10.17159/2411-9717/2015/v115n5a6

Value-in-use model for chlorination of titania feedstocks

crystallization control, and the mixture of solids andgases is cooled before separation. The gas isrecirculated to chlorination

[1]

➤ Surface Treatment. The TiO2 particles are treated toimprove their properties for specific pigmentapplications. This stage involves surface coating,washing, and de-watering to arrive at a final pigmentproduct

➤ Dispersion and packaging. The final stage ensures thatthe product is in a suitable form and packaging for theintended application.

Details of the chlorination stage of the process are shownin Figure 2. The equipment consists of a continuous fluid bed

chlorinator, a solids removal cyclone, a condensation unit,and a gas scrubber.

The chlorinator receives ore, coke, and chlorine as itsmain inputs. Some crude liquid TiCl4 is recycled to thechlorinator as a coolant for temperature control. Some of thesolids blown over in the chlorinator are recovered by thecyclone and recirculated.

The combination of the cooled off-gas duct from thechlorinator and the solids removal cyclone is referred to asthe ‘cross-over and cyclone’. Recycled crude liquid TiCl4 isused as a coolant in the cross-over section. Cooling results insome of the chlorides precipitating as solids. The cycloneseparates the coarsest solids from the vapour and fine solids.The coarse particles, which are a result of blow-over, arerecycled back to the chlorinator.

The condensation unit splits the inlet gas stream intocrude liquid TiCl4 and a gas stream. The gas is cleaned in thescrubber, and the crude TiCl4 is partially recycled and theremainder fed to the purification section.

The main chemical reactions occurring in the chlorinatorare shown in Equations [2] to [5] (Lee, 1991).

[2]

[3]

[4]

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386 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 1 – Schematic representation of the chloride TiO2 pigment production process. Adapted from Lee (1991)

Table I

Phase transition temperatures of metal chlorides(Bale et al., 2009)

Compound Melting point Boiling point Sublimation °C °C point °C

TiCl4 -24.3 136.0FeCl2 676.9 1042.4FeCl3 303.9 645.3MnCl2 649.9 1230.9AlCl3 192.6 432.4MgCl2 713.9 1358.3CaCl2 771.9 1934.8CrCl3 953.1CrCl2 814.9 1293.6VOCl3 126.85VCl2 1060.9VCl4 151.9

Figure 2 – Schematic representation of the TiO2 chlorination process. Adapted from Lee (1991)

[5]

The chlorination reactor is operated at temperatures ofaround 1000°C. During chlorination most of the metal oxidesare converted to chlorides and evaporated into the gas phase.High boiling-point chlorides such as CaCl2 and MgCl2 tend toremain in the bed as liquids, causing operational problems.SiO2 and ZrO2 tend not to chlorinate, and accumulate in thebed as solids. These problematic solids and liquids are bledfrom the bed to avoid build-up. Carbon binds to the oxygenin the metal oxides and leaves the reactor as a mixture of COand CO2. Moisture tends to react with chlorine and carbon toform hydrogen chloride gas. The gaseous chlorides and othergas species leave the reactor and are cooled, causing most ofthe impurity metal chlorides to revert to the solid state, butleaving TiCl4 in the vapour phase (Lee, 1991).

The reactions occurring in the chlorinator are mostlyexothermic. For example, when the reagents in Equation [2]are combined at 25°C adiabatically, the system would reach atemperature in excess of 1300°C. For this reason crude liquidTiCl4 is charged into the chlorinator as a coolant fortemperature control. This input stream was not includedexplicitly in the model, but due to the assumption ofisothermal equilibrium and the fact that separation efficiencyis specified and not modelled, this has a negligible effect onthe model results.

Material descriptionsTo operate the chlorinator efficiently, the TiO2 feed materialshave to comply with stringent quality specifications(Stanaway, 1994b).

➤ The bulk density and particle size distribution mustsupport fluidization of the bed, while minimizing blow-over of unreacted particles into the gas stream. TheTiO2 feedstock particle sizes must typically be smallerthan 850 μm and larger than 100 μm. The bulk densityof the material must be more than 2 kg/l

➤ The alkali oxide content (MgO and CaO) must be verylow since the alkali chlorides are liquid at chlorinatoroperating temperatures, and can cause clogging of thebed

➤ Although most of the feed materials contain some iron,the iron content must be limited to minimize ironchloride waste generation and reagent consumption,and maximize plant capacity

➤ Chromium and vanadium can cause toxicity of the ironchloride waste. The levels of these elements in the feedmust be limited

➤ Feed silica content must be limited to prevent build-upin the chlorinator

➤ Tin and arsenic tend to remain in the TiCl4 stream afterpurification, contaminating the final product. Feedmaterials must therefore be low in these elements

➤ Uranium and thorium are radioactive and concentratein waste and product streams, causing health andsafety risks. Very stringent specifications on theseelements are enforced.

A typical chemical composition specification for high-TiO2slag is shown in Table II.

Coke is ground to a particle size suitable for fluidization

before use. This material also has to be of high purity to limitthe introduction of impurity oxides into the process.

Industrial-grade chlorine is used in the main chloride feedstream. The recycled chlorine is less pure than the freshchlorine.

Key phenomenaGiven the purpose of the model, the most important processphenomena that had to be included were the following:

➤ Chemical reactions. The chemical reactions convertingfeed to product are the essence of the chlorinationreactor. These are affected by both feedstock propertiesand operating conditions

➤ Blow-over. The entrainment of particles in the gasproduct stream from the chlorinator has an influenceon the value of a particular feed material in the chlori-nation reactor

➤ Cyclone separation. The recovery of coarse particles inthe cyclone and recycling to the chlorinator is of similarimportance to blow-over.

Model developmentThe purpose of the model was to conduct techno-economiccalculations to evaluate the influence of different feedmaterials and operating conditions on the behaviour andperformance of the chlorination process. For this reason itwas decided to employ a steady-state model based on theprocess mass and energy balance.

The equipment incorporated into the model included thechlorinator, the off-gas cooling duct and cyclone (cross-overand cyclone), and the condensation unit that separates theproducts into gas, liquid, and waste streams.

Model overviewThe model flow sheet is presented in Figure 3. All solidmaterial inputs to the chlorinator are split into two streams. Aportion of the material is blown over based on particle sizeand density. The remaining solids are combined with thegaseous inputs and taken to isothermal thermochemicalequilibrium to model the chemical reactions. The products aresplit into solid waste bled from the chlorinator and a gaseousproduct stream destined for the cross-over and cyclone.

The blow-over solids and product gas from thechlorinator are charged into the cross-over and cyclonesection. A portion of the blow-over solids is recovered and

Value-in-use model for chlorination of titania feedstocks

387The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

Table II

Typical impurity specifications for titaniafeedstocks used in the chloride process(Stanaway, 1994b; Pistorius, 2007)

Compound Specification

SiO2 < 2.00%Al2O3 < 1.50%CaO < 0.13%MgO < 1.20%MnO < 2.00%Cr2O3 < 0.25%V2O5 < 0.60%U+Th < 100 ppm

Value-in-use model for chlorination of titania feedstocks

recycled to the chlorinator. The remaining solids and gas arecombined with cooling TiCl4 and equilibrated to generate thefinal product stream from this section. The product from thecross-over and cyclone is split between waste solids, a gasproduct, and a crude liquid TiCl4 product.

Model variables and parametersThe material input variables supplied to the model by theuser are listed in Table III, and the remaining input variablesin Table IV. The model parameters are presented in Table V.

The material stream values calculated by the model areshown in Table VI. In addition to these variables, those listedin Table VII are also calculated.

AssumptionsThe following assumptions were made in the model.

➤ The content of the fluid bed reactor is homogeneous interms of temperature, particle size distribution, andchemical composition. This assumption is implicit inthe lumped parameter modelling approach applied. Theassumption is not true, but it does not have adetrimental influence on the modelling results, giventhe purpose of the model

➤ Chlorinator reactions run to equilibrium. Thisassumption and the next one were required to make itpossible to solve the model. Very little detail wasavailable on the actual chemical reaction behaviour ofthe process, since access to a plant was not possible.The model results based on this assumption weredeemed to be acceptable for the purpose of the work

➤ Material reaches equilibrium in the cross-over➤ Coke is assumed to be pure graphite. This assumption

was made to simplify the model, and to focus it on theinfluence of the TiO2 feedstock rather than the coke.This means that sulphur is ignored by the model.Sulphur leaves the system through the waste gassystem as sulphur species such as H2S, COS, and

SOCl2. It has a limited influence on the chlorinationphenomena of interest here

➤ Liquid phases are immiscible. For simplicity, all liquidcompounds were handled separately as puresubstances, and not as a mixture. This reduced thedriving force for liquid formation because the activitiesof all the liquid compounds are unity. This assumptionwould have introduced inaccuracies into theequilibrium calculations

➤ Particles are compositionally homogeneous➤ Chlorine slip is usually low and therefore was assumed

to be zero in the model.Chlorine slip occurs when chlorine passes through thechlorinator bed unreacted. This phenomenon reduceschlorine efficiency and indicates a chlorinator bed issue(e.g. high SiO2 content, low carbon content).

Material definitionsThe materials in the model were described with thermo-chemical data from the FACT pure substance database inFactSage 5.5. The compounds included in the model are listedin Table VIII.

Examples of TiO2 feedstock assays are shown in Table IX.Coke, chlorine, oxygen, and nitrogen were all treated as purestreams. Air was entered as a 79% N2, 21% O2 mixture onvolume basis.

Some examples of particulate material properties forsynthetic rutile and slag are shown in Tables X and XI.

Physical phenomenaChemical reactions were modelled very simply throughequilibrium calculations using the ChemApp thermochemistrylibrary (Petersen and Hack, 2007), and data from FactSage(Bale et al., 2009).

Blow-over of solids in the chlorinator and subsequentrecovery of coarse solids in the cyclone were the only physicalphenomena that were modelled in detail, and are describedhere.

In the chlorinator, solids are entrained in the fluidizinggas and elutriated out of the chlorinator unit. This mass issignificant and therefore had to be included in the model. Dueto the complexities and uncertainties surrounding elutriation,a simplified methodology was used based on the followingmodel input parameters for each feedstock material:

➤ Density – the solid density of the feedstock material➤ Particle size distribution – the particles sizes and mass

fraction of each size class for each feedstock➤ Particle size elutriation constants (Ki*) – the elutriation

constants for each particle size class for each feedstock➤ Particle size cyclone separation efficiency – the fraction

of material of each particle size class recovered in thecyclone

➤ Composition – the composition of the feedstockmaterial, which is assumed to be constant throughouteach particle and to remain the same until it is blownout of the reactor.

In test work, the particulate solids elutriation constantsare determined in units of kg/(s.m2). These constants are afunction of particle shape, density, and size. This implies thatfor a reactor of a given area, a certain flux of solids of aspecific particle size is expected in the outlet. Data on the

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388 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 3 – The process model flow sheet

fluidization of TiO2 feedstocks has been previouslyinvestigated (Moodley et al., 2012).

At steady-state operation the rate of particles blown overis limited to the incoming rate of particles of that size. Forthis system, the following are the sources of particles of aspecific size:

➤ Feed material. Material feed is classified, and theparticles of a certain average particle size enter at aspecified total feed rate multiplied by the fraction ofmaterial with that average particle size

➤ Recycled blow-over feed. Using the elutriation constantfor a particular particle size class, the rate at whichparticles are blown over is calculated, with only someof these being removed in the cross-over and cyclonesection. The blow-over rate is therefore multiplied withthe separation efficiency in the cyclone for a particlesize class to calculate the rate at which particles of asize class are recycled

➤ Larger size particles reacting. Chemical reactions in thechlorinator cause particles to shrink and hence fall intosmaller size classes. This feed rate of particles into alower size class is determined from the average particle

sizes of the current size class and the larger size class,and the total rate of particles falling into the larger sizeclass minus the rate blow-over rate.

Therefore, the blow-over rate of size class i (Equation[6]) is calculated as the minimum of the input rate ofparticles and the value calculated from the elutriationconstant. This approach ensures that the blow-over rate isconstrained by a mass balance over the chlorinator. Thevariable used in the blow-over and cyclone recoverycalculations are defined in Table XII.

[6]

Value-in-use model for chlorination of titania feedstocks

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 389 ▲

Table III

Model material input variables. ‘X’ indicates user-specified input variables

Stream Feed rate Composition Temperature Compounds

TiO2 feed X X X TiO2, Ti2O3, FeO, Fe2O3, ZrO2, SiO2, Cr2O3, Al2O3, P2O5, MnO, CaO, MgO, V2O5, Nb2O5,

H2O, SnO2

Coke feeds X X CCl2(g) feed X X Cl2N2(g) X X X N2

O2(g) X X X O2

Air X X X N2, O2

Cl2(g) recycle XRecycle blow-over XChlorinator cooling TiCl4 X X TiCl4Cross-over cooling TiCl4 X X TiCl4

Table IVModel input variables

Variable Description Typical value Units

Feedstock basis

Operating temperatureCross-over temperatureProduct final temperatureOff-gas CO:CO2 molar ratioBed inerts cut-off fractionMass Ti oxides unreacted set-pointChlorinator TiCl4 coolingpercentage of product flowCross-over TiCl4 cooling ratio ofproduct flowPercentage Cl2 recoveredBlow-over reductant solidsremoval efficiencyReductant blow-overChlorine feed initial valueReductant feed initial valueChlorine recycle initial value

Total feedstock rate used as basis for the calculations. Each feedstock’s rate iscalculated using this value and the fraction of the feedstock in the mixTemperature at which the chlorinator is operated and the products exit from chlorinatorTemperature at which the material exits from the cross-over unitFinal temperature of the solid, liquid, and gas product out of the systemMolar ratio of CO and CO2 in the gas product out of the chlorinatorMaximum percentage of inerts in the bedAmount of unreacted Ti oxides in the chlorinator reaction mixture to solve the amountof chlorine required. Should be a small value larger than 0Fraction of the chlorinator product flow rate used to calculate the rate of TiCl4 cooling

Ratio of TiCl4 added to the cross-over for cooling relative to the TiCl4 production rate inthe chlorinatorPercentage of chlorine recovered from the circuitFraction of the reductant solid blow-over removed from the chlorinator output stream,and recycledFraction of reductant solids lost as blow-overInitial value of chlorine feed for the first iteration of the modelInitial value of reductant feed for the first iteration of the modelInitial value of recycled chlorine feed for the first iteration of the model

8100 kg/h

1050 °C200 °C70 °C140 %0.2 kg/h

0.025

1

99 %90 %

10 %13,660 kg/h1,439 kg/h

0 kg/h

Table V

Model parameters

Parameter Description Typical value Units

Bed volume Volume of the solids bed 20 m3

Bed area Area of the fluidized bed 7 m2

Value-in-use model for chlorination of titania feedstocks

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390 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table VI

Model material output streams. Values for feed rate, composition, and enthalpy are calculated for all the streams

Stream Description

Chlorinator output Gas product from the chlorinator equipment, containing solid feed material carried overBleed solids Solids and liquids forming in the chlorinator, which build up over time and are removed periodically as a bleed streamCross-over output Output from the cross-over section containing cooled gas and carried-over feed solids not recovered and recycledGas product Gas product from the chlorination circuit that includes CO, CO2, O2, N2, etc. This stream excludes any gas species forming due

to impurities in the feed materialsTiCl4(liq) Liquid TiCl4 product from the chlorination circuitSolid/liquid/gas All the solid, liquid, or gas species forming due to impurities in the feed materials that are not removed in any other stream from

the chlorination circuit

Table VII

Model output variables

Variable Description Units

Chlorinator energy requirement Energy balance over the chlorinator unit. This value is calculated as the chlorinator output enthalpy, minus kWthe cooling TiCl4 enthalpy and enthalpies of all feed material streams

Cross-over energy requirement Energy balance over the cross-over section. This value is the sum of the enthalpies of the cross-over output kWand the recycled blow-over, minus the chlorinator output enthalpy and the TiCl4 cooling enthalpy

Actual off-gas CO/CO2 ratio Molar ratio of CO to CO2 in the chlorinator output stream

Table VIII

Compounds used to describe the materials in the model, obtained from the FactSage 5.5 FACT pure substancedatabase (Bale et al., 2009)

Gas Liquid Solid

(FeCl2)2 FeCl3 AlCl3_liquid(liq) Al2O3_corundum(alpha(s4) NbO2Cl_solid(s)(FeCl3)2 H2O C2Cl4_liquid(liq) Al2O3_delta(s2) NbOCl2_solid(s)(MgCl2)2 HCl CaCl2_liquid(liq) Al2O3_gamma(s) NbOCl3_solid(s)Al2Cl6 MgCl CCl4_liquid(liq) Al2O3_kappa(s3) OAlCl_solid(s)AlCl MgCl2 CrCl2_liquid(liq) AlCl3_solid(s) (P2O5)2_solid(s)AlCl2 MnCl2 CrCl3_liquid(liq) C_graphite(s) S_orthorhombic(s)AlCl3 N2 CrO2Cl2_liquid(liq) CaCl2_hydrophilite(s) SiO2_coesite(s7)C2Cl2 O2 Fe3C_liquid(liq) CaCO3_aragonite(s) SiO2_cristobalite(h)(s6)C2Cl4 OAlCl FeCl2_liquid(liq) CaCO3_calcite(s2) SiO2_cristobalite(l)(s5)C2Cl6 OPCl3 FeCl3_liquid(liq) CaO_lime(s) SiO2_quartz(h)(s2)C6Cl6 OTiCl H2O_liquid(liq) CaOCl2_solid(s) SiO2_quartz(l)(s)CaCl PCl MgCl2_liquid(liq) Cr2O3_solid(s) SiO2_stishovite(s8)CaCl2 PCl3 MnCl2_liquid(liq) CrCl2_solid(s) SiO2_tridymite(h)(s4)CCl PCl5 NbCl5_liquid(liq) CrCl3_solid(s) SiO2_tridymite(l)(s3)CCl2 SCl2 OPCl3_liquid(liq) Fe_bcc(s) SnCl2_solid(s)CCl3 SiCl PCl3_liquid(liq) Fe2O3_hematite(s) SnO_solid(s)CCl4 SiCl2 SiCl4_liquid(liq) Fe3O4_magnetite(s) SnO2_cussit.ite(s)Cl SiCl3 SnCl2_liquid(liq) Fe3O4_magnetite(s2) Ti2O3_solid-a(s)Cl2 SiCl4 SnCl4_liquid(liq) FeCl2_solid(s) Ti2O3_solid-b(s2)CO SiO TiCl4_liquid(liq) FeCl3_molysite(s) Ti3O5_solid-a(s)CO2 SnCl2 VCl4_liquid(liq) FeO_wustite(s) Ti3O5_solid-b(s2)COCl SnCl4 VOCl3_liquid(liq) FeOCl_solid(s) TiC_solid(s)COCl2 SO ZrCl2_liquid(liq) (FeO)(TiO2)_ilmenite(s) TiCl2_solid(s)COS SO2 FeTi2O5_pseudobrookit(s) TiCl3_solid(s)CrCl SO2Cl2 MgAl2Cl8_solid(s) TiCl4_solid(s)CrCl2 SO3 MgCl2_chloromagnesite(s) TiO_solid_alpha(s)CrCl3 SOCl2 MgCO3_magnesite(s) TiO_solid_beta(s2)CrCl4 Ti2Cl6 MgO_periclase(s) TiO2_anatase(s2)CrCl5 TiCl MgTi2O5_karrooite(s) TiO2_rutile(s)CrCl6 TiCl2 MnCl2_scacchite(s) V2O5_solid(s)CrO2Cl TiCl3 MnO_solid(s) VCl2_solid(s)CrO2Cl2 TiCl4 Nb2O5_solid(s) VCl3_solid(s)CrOCl TiOCl2 Nb3Cl7_solid(s) ZrCl2_solid(s)CrOCl2 VCl4 Nb3Cl8_solid(s) ZrCl3_solid(s)CrOCl3 VOCl3 NbCl2_solid(s) ZrCl4_solid(s)CrOCl4 NbCl3_solid(s) ZrO2_cubic(s3)FeCl NbCl4_solid(s) ZrO2_monoclinic(s)FeCl2 NbCl5_solid(s) ZrO2_tetragonal(s2)

The first term in Equation [6], which is the maximumpossible blow-over rate according to the mass balance, isderived as follows:

The rate at which particles enter size class i (Fin,i) iscalculated as follows:

[7]

The rate at which particles leave the larger size class i+1(Fout,i+1) due to chemical reaction is calculated as follows:

[8]

The total blow-over rate of a feedstock is calculated as thesum of blow-over for all the particle size classes.

[9]The composition of the blow-over is derived from the

composition of the feed materials. The above methodology isbased on the following assumptions and simplifications:

➤ The composition of particles is that of the specified feedmaterial, and is constant throughout each particle

➤ particles are spheres and are evenly distributedthroughout each size class

➤ Particles do not break, and the only cause of particlesbecoming smaller is chemical reaction.

ImplementationThe model was implemented mostly in the Microsoft .NETenvironment with the C# programming language. MicrosoftExcel® was used as the front end because it is widelyavailable and used in industry. All thermochemicalcalculations were done using the ChemApp thermochemicallibrary (Petersen and Hack, 2007) and data exported fromFactSage (Bale et al., 2009).

ValidationThe model was validated qualitatively through interviewswith persons that have first-hand knowledge and experienceof the chlorination process. Quantitative validation wasplanned, but was ultimately cancelled for corporate reasons.

Modelling Studies

Value-in-use conceptsValue-in-use (VIU) models combine technical and financialinformation to provide a decision-making tool for productassessment and customer interaction. The concept aims toextract maximum sustainable value through knowledge andunderstanding of the value chain for the customer andproducer. One of the main uses of VIU models is to evaluateproduct changes against a base case and determine thefinancial impact of the change. VIU models can also be usedto compare and assess the value of different products in acustomer’s process. Examples where these two concepts havebeen practically used are provided in the following sections.

Example 1: production cost versus customer benefitThe first example evaluated the cost of producing a higherTiO2 grade slag versus the savings incurred by chlorideproducers.

Ilmenite (FeTiO3) is the most abundant titanium-bearingmineral and contains between 45 and 60% TiO2 (Moodley,2011). Ilmenite cannot be used directly in most pigmentproduction processes and has to be treated in order to

Value-in-use model for chlorination of titania feedstocks

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 391 ▲

Table IX

Example TiO2 feedstock assays

Compound Slag Synthetic rutile

Al2O3 1.1% 2.5%CaO 0.2% 0.2%Cr2O3 0.0% 1.0%Fe2O3 0.0% 0.0%FeO 10.2% 0.0%H2O 0.1% 0.1%MgO 1.0% 1.8%MnO 2.0% 1.4%Nb2O5 0.1% 0.1%P2O5 0.0% 0.0%SiO2 4.0% 1.8%SnO2 0.0% 0.0%Ti2O3 46.5% 0.0%TiO2 34.4% 91.4%V2O5 0.3% 0.3%ZrO2 0.3% 0.2%Total 100.2% 100.8%

Table X

Example synthetic rutile properties

Density: 4260 kg/m3

Size class Ki* (kg/m2/s) Mass % Cyclone removal

+850 0 0.0% 90%-850+600 0 0.0% 90%-600+425 0 0.9% 90%-425+300 0 3.3% 90%-300+212 0 16.5% 90%-200+150 0.00005 50.9% 90%-150+100 0.00037 26.0% 90%-100+75 0.0017 2.4% 90%-75 0.0829 0.2% 0%

Table XI

Example titania slag properties

Density: 4260 kg/m3

Size class Ki* (kg/m2/s) Mass % Cyclone removal

850 0 3.35% 90%-250 0 16.65% 90%-175 0 19.70% 90%-125 0 20.20% 90%-88 0 16.15% 90%-50 0.000285 11.30% 90%-50 0.0023 7.00% 90%-25 0.00575 2.85% 90%-75 0.0202 1.85% 0%

Value-in-use model for chlorination of titania feedstocks

upgrade the TiO2 content. In the smelting process, which cantake place in either an AC or a DC furnace, ilmenite is reducedusing anthracite to produce pig iron and titania-rich slag.

Titania slag competes with natural rutile (NR), syntheticrutile (SR), and upgraded slag (UGS) as feedstock to thechloride pigment process. NR, SR, and UGS contain more TiO2units than the titania slag, which has a typical titania contentof 85-87% TiO2. Production of a higher TiO2 slag using aconventional smelting process, although beneficial to thechloride pigment producers, incurs increased reductant andenergy requirements, increased refractory wear, possibletapping issues, and foaming in the smelting furnace. There isan increasing demand for slag producers to produce higherTiO2 content slag, since waste generation and chlorine costsare reduced.

Burger et al. (2009) utilized the chlorinator modeltogether with a smelter VIU model to evaluate the viability ofproducing a 90% TiO2 slag instead of the more conventional85–87% TiO2 slag. The chlorinator model has been describedin detail in this paper. The smelter VIU model was developedto model the production of TiO2 slag in a DC arc furnace. It isan Excel-based model that combines thermodynamic dataand plant data to calculate the energy requirements, reductantrequirements, and slag and metal yield for the smelter. Thisis combined with financial information to quantify the coststhat the slag producer will incur to produce different qualityslags.

Utilizing a higher TiO2 feedstock in the chlorinator resultsin less waste generation, lower treatment and disposal costs,and lower chlorine consumption, hence it results in a savingfor chloride pigment producers. However, producing a higherTiO2 slag results in increased reductant and energyrequirements, higher metal yield, and a lower slag yield. Thechlorinator VIU model and a smelter VIU model were used toquantify the savings realized by the chloride producers andthe extra costs incurred by the slag producer.

Smelter model assumptionsThe following assumptions were made in the smelter model(Burger et al., 2009):

➤ All feed materials enter the process at 25°C➤ Ilmenite feed composition from Tronox’s Hillendale

mine was used➤ Reductant from Zululand Anthracite Collieries was used

➤ Recovery factors, dust loss rates, dust analyses ,andcarbon contribution are derived from operatingexperience

➤ The carbon content of tapped iron is 2%➤ The carbon efficiency factor is 94.5%.

Smelter resultsThe results from the smelter VIU model are summarized inTable XIII. The base case was for the production of an 85%TiO2 slag. The results are expressed as the percentage changein various parameters when a 90% TiO2 slag is produced.Figure 4 is a waterfall graph that shows the increase in cost,expressed as a percentage of the slag price for a 85% TiO2slag, due to the increase in operational costs (i.e. electrodeconsumption, energy consumption, and reductant usage).The decrease in income due to the changes in slag and metalyield is shown as ‘pig iron income’ in Figure 4. The VIUcalculations indicate that slag producers will incur 13.5% costincrease in order to produce a 90% TiO2 slag using a conven-tional smelting process.

Chlorinator model assumptionsThe following assumptions were made in the chlorinatormodel:

➤ All feed materials enter the reactor at 25°C➤ Blow-over is determined by the elutriation constant,

which was determined experimentally for different sizefractions and different feedstocks

➤ Waste treatment includes neutralization of the wastewith lime

➤ Chlorinator operating temperature is 1000°C➤ The exit gas stream from the chlorinator is cooled to

200°C by liquid TiCl4 sprays (below the sublimationpoint of ferric chloride)

➤ The molar ratio of CO/CO2 in the gas product from thechlorination reactor is 1

➤ Price assumptions for the model are based on figuresobtained from a TZMI (2007).

Chlorinator model resultsIn the base case, only an 85% TiO2 slag is fed to thechlorinator. The percentage change in reagent consumptionand waste generation is reported in Table XIV for thealternative case, i.e. when only a 90% TiO2 slag is used in the

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392 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table XII

Variables used in blow-over and cyclone recovery calculations

Symbol Description Units

Fbor,i Blow-over recycle rate of solids from particle size class i kg/hFbo,i Blow-over rate of solids from particle size class i kg/hFin,i Total rate of particles fed into particle size class i kg/hKi* Elutriation constant for particles in size class i kg/s/m2

A Area of the chlorinator fluidized bed m2

i Index of the current particle size class for which calculations are madeFfeed,i Rate of particles from size class i in the feedstock stream kg/hXcr,i Fraction of particles from size class i removed in the cyclone and recycled to the chlorinatorFret,i Rate of particles in size class i retained in the fluid bed. This is the difference between the total input rate into a size class and the kg/h

rate of particles blown over from that size classri Average particle radius for particles in size class i μm

chlorinator. A negative value indicates that consumption islower with the 90% TiO2 feedstock.

Figure 5 is a waterfall graph that shows the monetarycontributions of coke, chlorine, logistics, and waste cost tothe increase/decrease in value of the 90% TiO2 feedstock. Thecontribution is expressed as a percentage of the 85% TiO2slag price (i.e. the base case). Chloride producers will utilizemore coke to chlorinate a higher TiO2 feedstock, thereforepetroleum coke will have the opposite effect of the chlorineand waste costs and reduce the value of the higher TiO2feedstock as indicated in Figure 5. The impact of increasedpetroleum coke consumption is, however, low compared tothe waste and chlorine savings. The overall VIU calculationsindicate that the pigment producer’s costs will be 9.2% lowerif a 90% TiO2 slag is utilized instead of an 85% TiO2 slag.

The study indicates that the cost of producing a high-grade slag outweighs the savings realized at the pigmentplant. However, although production of a higher TiO2 slag isnot viable based on the assumptions and prices used in themodel, this could alter as prices change, and the use of VIUmodels will enable the changes to be quickly assessed.

Example 2: relative feedstock valuesIn the second example, the relative value of an 86% TiO2feedstock is compared with a 95% TiO2 feedstock. Utilizing ahigher TiO2 feedstock in the chlorinator results in less wastegeneration, lower treatment costs, lower disposal costs, and

lower chlorine consumption, and hence results in a saving forchloride producers. The value of TiO2 feedstock containingmore TiO2 units should therefore be higher. The chlorinatormodel was used to determine the relative value of using atitania slag (86% TiO2) compared to a natural rutile productcontaining 95% TiO2 in the chlorination process, consideringthe major cost elements (i.e. the costs of petroleum coke,chlorine, waste disposal and waste treatment).

Model assumptions➤ Same as in Example 1➤ Price assumptions are based on figures obtained from a

TZMI (2012) and escalated to present-day prices.

Model resultsThe major results from the chlorinator VIU model areprovided in Table XV. In the base case, only an 86% TiO2slag is fed to the chlorinator. The percentage change inreagent consumption and waste generation is reported inTable XV for the alternative case, i.e. when only natural rutileis used.

With natural rutile, less waste is generated, and less limeand make-up chlorine is required, but coke consumptionincreases.

The waterfall graph (Figure 6) shows the monetarycontribution of coke, chlorine, and waste cost to theincrease/decrease in value of the natural rutile. The contri-

Value-in-use model for chlorination of titania feedstocks

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 393 ▲

Table XIII

Summary of smelter VIU results (Burger et al.,2009)

Parameter (per ton ilmenite) Change from 85% to 90% TiO2 slag

Slag yield -5.5%Metal yield +7.2%Electrode consumption +3.1%Energy consumption +3.8%Reductant consumption +7.1%

Table XIV

Summary of chlorinator VIU results

Parameter Change from 85% to 90% TiO2 slag

Petroleum coke consumption +3%Chlorine gas consumption -7%Lime consumption for neutralization -24%Waste generated -24%

Figure 4 – Breakdown of smelting costs and income to produce high-grade slags (Burger et al., 2009)

Figure 5 – Breakdown of cost elements for the chlorination of a high-grade slag, corrected (Burger et al., 2009)

Value-in-use model for chlorination of titania feedstocks

bution is expressed as a percentage of the 86% TiO2 slagprice (i.e. the base case).

VIU calculations show that for the given set ofassumptions and prices, the value of natural rutile in thechlorinator is 6.7% higher than that of the slag. This islargely due to the chlorine costs, which account for 4.80% ofthe 6.7% change. In the Burger et al. (2009) study, thecontribution from waste treatment costs (Figure 5) was thehighest, but due to the decrease in waste treatment costs(TZMI, 2012), the chlorine costs are now the major costdriver.

Knowledge of the relative value of the feedstocks isimportant for suppliers to position themselves in the marketand understand the value of their feedstock.

ConclusionsModelling of the chlorination process facilitates the quickassessment of different feedstocks and provides valuableinsight into a process that feedstock producers normally donot have access to.

Although VIU models are a powerful-decision makingtool, care must be taken to ensure that the assumptions arevalid and are regularly updated.

References

BALE C. W., BÉLISLE, E., CHARTRAND, P., DECTEROV, S.A., ERIKSSON, G., HACK, K.,

JUNG, I. H., KANG, Y.B., MELANÇON, J., PELTON, A.D., ROBELIN, C., and

PETERSEN, S. 2009. FactSage thermochemical software and databases -

recent developments. Calphad, vol. 33. pp. 295–311. www.factsage.com

BURGER, H., BESSINGER, D., and MOODLEY, S. 2009. Technical considerations and

viability of higher titania slag feedstock for the chloride process. 7thInternational Heavy Minerals Conference, ‘What Next’. Champagne Sports

Resort, Drakensberg, South Africa, 20–23 September 2009. Southern

African Institute of Mining and Metallurgy, Johannesburg. pp. 187–194.

HAMOR, L. 1986. Titanium dioxide manufacture. Australia: A World Source ofIlmenite, Rutile, Monazite and Zircon Conference, Perth, Australia.

Australasian Institute of Mining and Metallurgy. pp. 143–146.

KOTZÉ, H., BESSINGER, D., and BEUKES, J. 2006. Ilmenite smelting at Ticor SA.

Southern African Pyrometallurgy Conference, Cradle of Humankind, South

Africa, 5–9 March 2006. Southern African Institute of Mining and

Metallurgy, Johannesburg. pp. 203–214.

LEE, R.F. 1991. Chloride route titanium dioxide pigments. Process and

properties. Fifth AusIMM Extractive Metallurgy Conference. Perth,

Australia, October 1991. Australasian Institute of Mining and Metallurgy.

pp. 35–38.

MOODLEY, S. 2011. A study of the chlorination behaviour of various titania

feedstocks. MSc thesis, University of the Witwatersrand, Johannesburg.

MOODLEY, S., KALE, A., BESSINGER, D., KUCUKKARAGOZ, C., and ERIC, R.H. 2012.

Fluidization behaviour of various titania feedstocks. Journal of theSouthern African Institute of Mining and Metallurgy, vol. 112, no. 6. pp.

467–471.

PETERSEN S. and HACK, K. 2007. The thermochemistry library ChemApp and its

applications. International Journal of Materials Research, vol. 98, no. 10.

pp. 935–945.

PISTORIUS, P.C. 2007. Ilmenite smelting: the basics. 6th International HeavyMinerals Conference ‘Back to Basics’, Hluhluwe, South Africa, 9–14

September 2007. Southern African Institute of Mining and Metallurgy,

Johannesburg. pp. 35–43.

STANAWAY, K.J. 1994a. A titanium pigment feedstock overview. SME AnnualMeeting, Albuquerque, New Mexico, 14–17 February 1994. pp. 1–6.

STANAWAY, K.J. 1994b. Overview of titanium dioxide feedstocks. MiningEngineering, vol. 46. pp. 1367–1370.

TZMI. 2007. Global TiO2 Pigment Producers - Comparative Cost and

Profitability Study. TZ Minerals International Pty Ltd, Victoria Park, WA,

Australia.

TZMI. 2012. Global TiO2 Pigment Producers - Comparative Cost and

Profitability Study. TZ Minerals International Pty Ltd, Victoria Park, WA,

Australia. ◆

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Table XV

Results from the chlorinator VIU model

Parameter Change from 86% to95% TiO2

slag

Petroleum coke consumption +12%Chlorine gas consumption -73%Lime consumption for neutralization -67%Waste generated -67%

Figure 6 – Breakdown of cost elements in the chlorinator for 86% TiO2

slag and natural rutile

IntroductionThe direct current (DC) arc furnace isemployed as a metallurgical unit operation in asignificant portion of the pyrometallurgyindustry worldwide. While DC furnaces aretraditionally used in the re-melting of steelscrap, they are becoming increasinglyattractive for the smelting of raw materials toproduce commodities such as ferrochromium,ilmenite, ferronickel, platinum group metals,magnesium, and others (Jones and Curr,2006).

The layout of a typical DC furnace isshown in Figure 1. A cylindrical shell toppedwith a conical roof and lined with refractorymaterials forms the furnace vessel, andcontains the molten process material. Thismolten bath typically consists of multipleliquid phases, with oxide (slag), alloy, and/orsulphide (matte) phases possible. Rawmaterials consisting of ores, reductants, andchemical modifiers are continuously fed into

the vessel via feed ports at various locations inthe roof. Single or multiple pre-baked graphiteelectrodes enter through the top of the roof. Apower supply consisting of a transformerconnected to a DC rectifier feeds electricalpower to the furnace via the graphite electrodeand an anode connection in the hearth.

The electrical energy is converted intothermal energy in the plasma arc, which is theprimary heating and stirring element inside theDC furnace. The arc consists of ionizedfreeboard gas containing a mixture of ions andfree electrons, and is able to conduct electricitywell. The electromagnetic forces interact withthe plasma gas to accelerate and heat it,resulting in the formation of the arc – a high-temperature, high-velocity jet directed at thesurface of the molten bath (Bowman, 1994).The arc may be thought of as the ‘engineroom’ of the furnace, and improving theunderstanding of the physical behaviour ofthis region is invaluable for the improvementof current and future furnace designs.

One of the cited advantages of using DCfurnaces for a particular process is that it isable to treat very fine materials directly,without the need for an agglomeration orbriquetting stage prior to their introductioninto the furnace (Jones and Curr, 2006). Whileit simplifies the furnace plant and operationconsiderably, this practice often raises theissue of dust carry-over – that is, how much ofthe dust from the feed material bypassestreatment in the furnace and is carried out ofthe off-gas ducts by entrainment in the gasflow. This is exacerbated in DC furnaces by theopen-bath, open-arc nature of the operation,which provides no physical obstruction to theflow and circulation of gas in the freeboard.

Computational study of the dust behaviourin DC furnaces may be broken down into two

Interaction of dust with the DC plasmaarc – a computational modellinginvestigationby Q.G. Reynolds*

SynopsisThe presence of dust and fume suspended in the freeboard region is acommon feature of the operation of direct current (DC) plasma smeltingfurnaces. This occurs primarily as a result of the use of fine feed materialstogether with the open-arc, open-bath operation of such smelters, and isexacerbated by the high velocities and turbulent mixing of the gas in thevicinity of the arc jet. Dust and fume losses into the furnace off-gas systemcan be significant in some cases and may have economic, operational, andenvironmental impacts on the process. A computational modelling study ispresented in which the concentration of dust material was considered as acontinuous field subject to a governing partial differential equation.Settling behaviour was calculated as a function of particle size, localgas/plasma temperature, and other physical properties. Development ofthe coupling between the concentration field and a magnetohydrodynamicdescription of the arc is shown, and the resulting models were used tocompute various aspects of the behaviour of the concentration field in thearc region for a variety of furnace conditions. Time-averaged as well astransient models of the arc were used to generate the results presented.Qualitative case studies produced several practical suggestions for furnaceoperation, including increased dust capture by the bath when feed portsare located closer to the electrode, and the possible effects of feedsegregation in the furnace freeboard based on dust particle size anddensity.

KeywordsDC furnace, arc plasma, dust entrainment, continuumcomputationalmodel, freeboard flow, arc flow.

* Pyrometallurgy division, Mintek.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. This paperwas first presented at the, PyrometallurgicalModelling Principles and Practices, 4–5 August2014, Emperors Palace Hotel Casino ConventionResort, Johannesburg.

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ISSN:2411-9717/2015/v115/n5/a7http://dx.doi.org/10.17159/2411-9717/2015/v115n5a7

Interaction of dust with the DC plasma arc – a computational modelling investigation

broad areas – the freeboard flow problem and the arc flowproblem. In the freeboard flow problem, one models thelarge-scale flow structures inside the entire furnace freeboardspace and uses these models to predict dust bypassing fromthe feed port inlets to the off-gas outlets. In order to maintaincomputational economy, these models generally either ignorethe presence of the arc or treat it as a simple jet, and usesimplified fluid descriptions for the gas. A number ofresearchers have done valuable work in this area, predomi-nantly focusing on engineering applications inside andaround furnaces (de Jong and Mitchell, 2010; Ravary andGradahl, 2010). In the arc flow problem, the influence of theplasma arc on the entrained dust in its immediate vicinity ismodelled in detail. This generally requires a more sophis-ticated mathematical description of the arc in terms ofmagnetohydrodynamics (e.g. Szekely et al., 1983). Thecoupling between the entrained dust and the plasma gasmust also be accounted for, and may be added to such arcmodels by modelling the dust particles’ presence in severaldifferent ways, including continuum methods (Ranz et al.,1960) and particle methods such as discrete elementmodelling (Hager et al., 2013). Owing to the large drivingforces and highly dynamic nature of the plasma arc, whichcan evolve on time scales of the order of milliseconds or less(Reynolds et al., 2010), study of the arc flow problemgenerally requires transient solution methods on high-resolution numerical meshes, and is considerably morecomputationally demanding even when considering only asmall region of the freeboard space around the arc. As aresult, it appears that little work has been done in the area todate.

In the present study a continuum computational model ofthe arc and dust concentration field is proposed, togetherwith a discussion of various levels of coupling between thedust field and the arc plasma gas. The model is then used toexamine the effect of various parameters on the dust distri-bution in and around the arc on a simplified, approximatetwo-dimensional geometry.

Model developmentEquations describing the coupled momentum, energy, andelectromagnetic fields are required in order to produce

mathematical and computational models of plasma arcs.These have been described in some detail elsewhere(Reynolds et al., 2010), and are reproduced below.

[1]

[2]

[3]

The Navier-Stokes and continuity equations [1] representthe conservation of momentum and mass respectively. InEquation [1], ρ is the plasma density, U is the velocity vector,P is the pressure field, τij is the viscous stress tensor(Newtonian fluids with viscosity μ are used in the presentwork), j is the electric current density vector, and B is themagnetic field vector.

The conservation of energy is governed by Equation [2].In this relationship, h is the enthalpy of the plasma, κ is thethermal conductivity, CP is the heat capacity, σ is the electricalconductivity, kb is the Boltzmann constant, e is the electroncharge, and QR is the radiation emission coefficient. The lastthree terms on the right-hand side of Equation [2] representenergy generation by ohmic heating, electron enthalpytransport, and energy loss from the plasma by radiation.

Maxwell’s equations for electrostatics and magnetostatics(Equation [3]) govern the electromagnetic fields in theplasma. Here, φ is the scalar electric potential field, and μ0 isthe magnetic permeability of free space.

Under the assumptions of local thermodynamicequilibrium (Boulos et al., 1994), it is possible to describe theplasma fluid using a single temperature T. Enthalpy and allphysical properties of the plasma are strongly temperature-

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396 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 1 – Diagram showing layout of a typical DC arc furnace

dependent, and are therefore treated as variables inEquations [1]–[3]. This temperature dependence, togetherwith the additional coupling via convection and source terms,results in a fully-coupled system in which all field variablesdepend to some degree on the others.

Modelling the dust fieldIn order to evaluate the distribution of dust in the arc region,a continuum approach is taken. If the local mass concen-tration of dust, c (mass of dust per unit mass of plasma gas),is taken as a field variable, then an additional transportequation for the mass conservation of dust may be written asshown in Equation [4].

[4]

Here, US is the terminal settling velocity of the dustparticles in stagnant plasma gas, and DD is a diffusioncoefficient for the dust field. An assumption has been madein order to derive Equation [4], namely that the dust particlesare fully entrained at the same velocity as the plasma gas atall times apart from the additional settling velocity. Thisimplies that the dust particles are accelerated or deceleratedto the local plasma gas velocity instantly, without any timelag due to the action of drag forces.

US may be calculated from the Stokes settling law (Lamb,1932) as shown in Equation [5], assuming that the dustparticles are sufficiently small that the Reynolds numberremains below 1; this is generally the case for particles lessthan 1 mm in size falling through plasma gases.

[5]

Here, ρD and dD are the dust particle density and averagediameter respectively, and g is the vector of acceleration dueto gravity.

DD is somewhat harder to calculate accurately as it isgenerally a function of both molecular diffusion due toBrownian motion and collisions with particles in the plasmagas, and turbulent diffusion due to the eddy viscosity of thesurrounding fluid. The molecular component may beestimated using the Stokes-Einstein relationship (Einstein1905), shown in Equation [6].

[6]

Evaluation of this equation for a range of typical plasmaconditions gives values for DD,M of between 10-10 and 10-12

m2/s. These are so low as to be negligible, and the moleculardiffusivity may be safely ignored.

In the case of the fully-transient and coupled dustmodels, in which Equation [4] is solved in lock-step withEquations [1]–[3], no turbulence closure model is used, andthe solution is obtained on high-resolution meshes whichattempt to resolve the majority of the turbulent flow scalesdirectly. For these cases, the eddy diffusion component iscalculated explicitly as part of the flow field and DD may betaken as equal to DD,M. However, in the case of time-averaged models with decoupled solution of the dust field (asare used for many of the results presented later), the

turbulent eddy viscosity is not calculated directly and must beestimated from the time-averaged velocity field. For thispurpose, the strain rate relationship familiar from large eddysimulation methods (Smagorinsky, 1963) is used, as shownin Equation [7].

[7]

Here, Sij is the strain rate tensor, which may bedetermined from the velocity field. δl is a length parameterrepresenting the size of the numerical mesh elements at anygiven location, and CS is a dimensionless empirical constantwith value 0.16 – 0.2. Evaluation of Equation [7] gives aturbulent diffusivity field, and the total diffusivity to be usedin Equation [4] may be calculated from DD = DD,M + DD,T.

Coupling between dust, energy, and momentumfieldsIt is important to note that Equations [1]–[4] represent asystem with only one-way coupling between the dust concen-tration field and the remaining variables. The arc affects thedust concentration distribution, but not vice versa. Suchdecoupling is valid for low dust concentrations, and hascertain advantages; for example, in cases in which thesettling velocity of the dust is very low, the time scales of themotion and evolution of the dust field may differ from thoseof the arc by an order of magnitude or more. In such cases, itis convenient to calculate time-averaged representative flowand temperature fields from Equations [1]–[3], and thensolve Equation [4] separately using the time-averaged fieldsto study the evolution of c.

In general, however, the presence of a dust concentrationfield distributed through the plasma gas will affect both themomentum and heat transfer behaviour via two-waycoupling. In order to study this effect, a simplified method ofcoupling is adopted based on the assumption of thermal andmomentum equilibrium – that is, that the local temperatureand velocity (apart from settling) of the dust particles areidentical to those of the plasma gas. In reality, the dustparticles have their own velocity and temperature distrib-utions, with momentum and energy exchanged between themand the plasma gas resulting in acceleration and heating ofthe dust. However, this would not only introduce twoadditional fields requiring numerical solution (dust velocityand temperature) but also raise additional questions of howthe transport between dust and plasma gas is to be modelled.As the purpose of the present study is to examine the grossqualitative effects of coupling between dust and arc, thesimpler equilibrium approach was deemed justified, but thisremains an open area for future work.

Under the equilibrium assumption for momentum,neglecting any additional momentum transfer to the plasmadue to settling of the dust, and using the approximation thatDD = DD,M ≈ 0 in the fully-coupled model, additional sourceterms representing the momentum of acceleration anddeceleration of the dust field are included in the Navier-Stokes Equation [1] to give:

[8]

Interaction of dust with the DC plasma arc – a computational modelling investigation

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Interaction of dust with the DC plasma arc – a computational modelling investigation

Similarly, under the equilibrium assumption for energy,source terms are added to Equation [2] to account for thetransport of thermal energy contained in the dust field. Somere-arrangement and simplification using Equation [4] thenleads to:

[9]

Here, CPD is the heat capacity of the dust particles,assumed to be constant.

Two-way coupling between the dust concentration fieldand the arc’s momentum and enthalpy fields may then berealized by solving Equation [8] in place of Equation [1],Equation [9] in place of Equation [2], or both.

Model geometry and boundary conditionsWhile the model as developed extends naturally to both two-and three-dimensional cases, only 2D cases were consideredin the present study. Time and resource limitations were themain reason for this, as the solution of high-resolutiontransient models of the plasma arc in three dimensions isextremely computationally intensive and time-consuming.

In order to study representative behaviour of the arc andassociated dust fields, a 2D planar model region is generatedby taking a slice through the centre line of the arc.Axisymmetry is not enforced in the model, and while thisallows asymmetric structures and phenomena to evolvenaturally, it is important to note that the planar nature of themodel may be expected to result in some discrepanciesbetween real three-dimensional furnaces and the modelresults. Such models should therefore best be viewed as ‘arc-like systems’ which are capable of generating physicallyrealistic and qualitatively similar behaviour, rather thanquantitatively accurate engineering tools (Reynolds et al.,2010). The purpose of the present study is primarilyqualitative, and the 2D approach was deemed to be anacceptable approximation.

The geometry used for the arc flow models is shown inFigure 2.

ABCD describes the surface of the molten bath, whichacts as the anode in the system. In general, the thrust force ofthe arc deforms the bath surface, creating a cavity BC belowthe electrode tip. The graphite electrode surface is describedby FGHIJKLM, with the cathode attachment spot IJ acting asthe cathode and the root of the plasma arc jet. All other

boundaries are open to the freeboard atmosphere, permittinginflow and outflow of plasma gases and dust. NO is the dustinlet, through which a fixed concentration of dust isintroduced into the arc region.

Boundary conditions for the various fields in the modelare required in order to complete the solution. These areshown in Table I.

Here, P0 is the gas pressure in the furnace freeboard,typically atmospheric. hA, hI, and hE are the plasma gasenthalpies at the anode surface temperature (taken as3000K), inlet freeboard gas temperature (taken as 3000K),and electrode surface temperature (taken as the sublimationtemperature of graphite, 4100K) respectively. c0 is the inletdust concentration, fixed for each simulation. jk is the currentdensity at the cathode spot, for which a value of 3.5 × 107

A/m2 was used (Bowman, 1994). Specifying the total current carried by the arc permits the

calculation of the diameter of the cathode spot xIJ, using thegiven value of jk. Knowing the current and the distance fromthe cathode tip to the surface of the anode also permits thecalculation of the thrust force generated by the arc (Bowman,1994) and by analogy with turbulent gas jets, the shape anddimensions of the cavity surface BC (Cheslak et al., 1969).BC is treated as static in the present model. However, ingeneral this zone will be characterized by a large amount ofturbulent splashing and mixing. This would be expected tosignificantly enhance dust capture by the bath in this area,but a detailed examination of this phenomenon is left forfuture studies.

Numerical and computational implementationIn order to obtain numerical solutions of Equations [1]–[3] aswell as the fully-coupled systems using Equations [8] and[9], a custom solver application was written in C++ codeusing the open source OpenFOAM v2.3.0 framework for fieldsolutions of differential equations (OpenFOAM, 2014).OpenFOAM implements a generalized unstructured-meshfinite-volume method for defining conservation equations,and provides a number of accelerated and parallelized matrixsolution techniques suitable for such problems.

Following the general approach of Sass-Tisovskaya(2009), an existing OpenFOAM CFD solver for incompressibleflow using the Pressure Implicit with Splitting of Operators(PISO) predictor-corrector algorithm was heavily extendedand modified to allow for general temperature-dependentplasma physical properties specified via lookup tables.Additional solvers for enthalpy and the electromagnetic fieldswere then constructed from scratch and incorporated into thestandard PISO algorithm used for the velocity and pressurefield solutions. Finally, a solver for the dust equation [4] wasconstructed and added to the algorithm, together with run-time switches to enable user activation or deactivation of thecoupling behaviour.

Computational meshes for the problems were constructedand parsed into OpenFOAM format via Perl scripts usingGmsh v2.5.1 (Gmsh 2014), an open source geometry andmesh generation application.

The solver code was compiled and executed on a smallcomputational cluster running Ubuntu Linux 12.04 LTS. GCC4.6.3 (GCC, 2014) was used as the compiler, along withstandard OpenFOAM wmake scripts. OpenMPI was used as

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398 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 2 – Diagram showing region geometry for computational model

the message-passing middleware. Post-processing andvisualization was performed using ParaView v4.1.0(ParaView 2014) together with the OpenFOAM wrapperscript, paraFoam.

Results

Effect of the arc on the dust – one-way couplingIn order to study the effect that the flow field of the plasmaarc has on the dust concentration field in the model region, adecoupled approach using time-averaged fields was taken.The procedure was as follows:

➤ Set up the model geometry and parameters➤ Run the plasma arc model (Equations [1]–[3]) with no

dust present for a short time period, until the initialconditions have decayed

➤ Generate time-averaged velocity and temperature fieldsusing the results of the initial run

➤ Run the dust transport model (Equation [4]) for anappropriate length of time to study the evolution and

development of the dust concentration field to steadystate.

Base case modelAs a starting point for the one-way coupling study, a basecase was defined. From this initial starting point, thesensitivity of the model to various design and materialparameters could be studied. The parameters used for thebase case model are shown in Table II. Dimension subscriptsrefer to Figure 2.

The dimensions of the region were chosen to match atypical small-scale pilot plant DC arc furnace facility. For thebase case model, the dust inlet was located equidistantbetween the electrode and the edge of the model region. Dustconcentrations in smelting furnace freeboards typically rangebetween 0.1 and 1 kg/kg (Geldenhuys and Jones, 2009), andthe diameter of suspended and bypassed dusts has beenmeasured between 20 and 1000 µm (Guezennec et al., 2005;Rughubir and Bessinger, 2007). Tabulated physicalproperties as functions of temperature for air thermal

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Table I

Boundary conditions used in DC plasma arc dust transport model

Table II

Base case model parameters, one-way coupling

Parameter Value Parameter Value

Region dimensions (xAD × zDE) 0.5 × 0.25 m Inlet dust concentration (c0) 0.5 kg/kgElectrode diameter (xFM) 0.1 m Dust diameter (dD) 750 μmArc length (zH) 0.1 m Dust density (ρD) 3500 kg/m3

Dust inlet length (xNO) 0.05 m Run time (arc) 0.1 sDust inlet location Midpoint Run time (dust) 0.1 sArc current 1000 A Plasma gas Air

Interaction of dust with the DC plasma arc – a computational modelling investigation

plasmas were used (Boulos et al., 1994). The plasma arcmodel with no dust present was used to compute the first 100ms of arc motion, with the final 25 ms being used for timeaveraging. The time-averaged fields were then used as inputfor the dust model, which was run for an additional 100 ms.An unstructured mesh of approximately 20 000 quadrangularelements was used for the simulations, in which highresolution was applied in regions of high shear and energyflux.

Time-averaged velocity and temperature fields of the arcare shown in Figure 3. It can be seen that time averaging ofthe rapid turbulent and chaotic dynamics of the arc columnacts to produce a wide, conical temperature and jet velocityfield which spreads out from the electrode tip into the spacebelow. Figure 3b also shows the path of streamlines (inbrown, including the settling velocity US) from the dust inletat top left through the region and eventually out at the leftboundary. It can be seen that the recirculation vorticesgenerated around the arc result in substantial deformation ofthe streamlines, and much of the dust introduced at the inletboundary is therefore likely to be entrained into the body ofthe arc jet.

Development of the dust concentration field c for the basecase model is shown in Figure 4. It can be seen that the dust

is very rapidly drawn into the high-velocity arc jet within 10ms of entering the region. The fully developed steady-statedust concentration profile is shown in Figure 4d, with themajority of the dust passing through the arc jet before beingcarried out of the region by the strong flow adjacent to thebath surface. A smaller portion of the dust is captured by thebath surface.

The rate of dust convection or settling through thevarious boundaries can be studied by calculating the dustmass flux at the boundary surface (per linear metre in the y-direction, since the model domain is planar). Figure 5 showsthe evolution of the rate of dust ingress or removal throughthe inlet (surface NO in Figure 2), bath (surface ABCD inFigure 2), sides (surfaces DE and AP in Figure 2), and top(surfaces EF, MN, and OP in Figure 2). The inlet flux is seento be constant, as expected, while the dust fluxes through thevarious boundaries rise from zero to their steady-state valuesbetween 8 and 20 ms.

Once steady state is reached in the base case model,approximately 17% of the dust entering the region throughthe inlet is captured by the bath in the vicinity of the arc,77.4% is bypassed out the sides of the region (mostly veryclose to the bath surface), and 5.6% recirculates back into thefreeboard space above the arc region.

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400 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 4 – Dust concentration field at various times, base case model. Scale 0 (grey) to 0.5 (black) kg/kg

Figure 3 – Plot of time-averaged (a) temperature and (b) velocity unit vector fields, base case parameters

Effect of arc lengthIn order to study the effect of arc length (distance from tip ofelectrode to bath surface) on the dust transport model,several cases were set up and run keeping all parameters inTable II constant, with the exception of zH which was variedbetween 0.025 m and 0.2 m. Arc models were run for 100 ms(with 25 ms time-averaging) in order to generate averagedtemperature and velocity fields. The dust transport model wasthen run for 100 ms for each case.

The resulting steady-state dust concentration fields forthe two extreme cases are shown in Figure 6.

It is immediately obvious that running at shorter arclengths results in far less entrainment of dust. This isprimarily due to the reduction in size of the high-velocityrecirculation zones around the arc – these zones scale inproportion to the length of the arc jet, and shorter arcsproduce more compact velocity fields. Short arcs also producea narrower, deeper cavity in the bath surface, which redirectsmuch of the impinging gas flow back into the arc jet. As aresult, dust is able to settle most of the way to the bathsurface before interacting with the velocity field to anynoticeable degree. The opposite is true for long arcs – thesecreate very large vortex cells on either side of the arc jet, andthe dust is almost immediately drawn into the body of the arcas it enters the region.

The dispersion of dust from the inlet to the boundarysurfaces at steady state for the various arc length cases is

shown in Figure 7. Increasing the arc length shows a generaltrend of increasing the dust bypassed out of the arc region,either via the side or top boundaries. Shorter arc lengthsproduce less bypassing and permit more capture of the dustby the bath, assuming all other geometry and materialparameters remain constant.

Effect of arc currentThe electric current carried by the arc is a key design andoperation variable for DC furnaces. The effect of current onthe dust behaviour was examined by varying the specified DCcurrent while keeping all other parameters of the model aslisted in Table II constant. Currents from 250 to 2000 A wereused. Run-times for the arc model and time-averaging stepvaried from 400 ms (100 ms averaging) in the 250 A case to50 ms (12.5 ms averaging) in the 2000 A case. The dusttransport models were run for an additional 100 ms,sufficient for the fields to evolve to steady state in all cases.

Steady-state dust concentration fields for the 250 A and2000 A cases are shown in Figure 8. While both high andlow currents show some degree of entrainment of dust intothe arc jet and significant bypassing of dust material out ofthe region boundary at left, it can be seen that the lowervelocities resulting from lower currents result in morepronounced settling of the dust cloud as it is convected. Thehigher currents, while entraining more of the dust andcarrying it closer to the bath surface, result in such highvelocities that the dust is rapidly carried out of the side of themodel region before it gets a chance to settle onto the bathsurface.

The steady-state dispersion of the inlet dust to variousboundaries for the different arc current cases is shown in

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Figure 6 – Steady-state dust concentration fields at various arc lengths. Scale 0 (grey) to 0.5 (black) kg/kg

Figure 5 – Graph showing time evolution of dust mass fluxes at variousboundary surfaces, base case model

Figure 7 – Distribution of dust flux as a function of arc length

Interaction of dust with the DC plasma arc – a computational modelling investigation

Figure 9. These results are somewhat noisy and it is difficultto discern an overall trend in the deportment of the dust flux,although comparing the result at 500 A to that at 1000 Asuggests the possibility that higher currents may cause moredust to be bypassed out of the arc region. This may beexpected given the fact that increasing current typicallyincreases the velocities of the plasma in the arc (Reynolds etal., 2010), significantly reducing the residence time of thedust in the arc region and therefore reducing the timeavailable for it to settle as it passes through.

Effect of dust inlet locationThe location of the dust inlet, although a somewhat artificialconcept in the present model, may be related back to thedesign of feed systems for DC furnaces and in particular thelocation of the feed ports in the furnace roof. With this inmind, the location of the inlet was varied while keeping allother parameters constant. Cases with the dust inlet adjacentto the electrode, and at the edge of the region, wereexamined. As before, arc models with no dust present wererun for 100 ms, with the last 25 ms being used to generatetime-averaged fields for input into the dust transport model,which was then run for an additional 100 ms.

Steady-state dust concentration fields resulting fromchanging the location of the dust inlet are shown in Figure10. It is interesting to note that moving the dust inlet doesnot affect the gross dust entrainment behaviour, namely thatthe falling dust column is drawn into the arc jet along theelectrode surface at the centre of the furnace, acceleratedtoward the bath surface, and then convected outward.However, it can be seen that in the case of the adjacent inletconsiderably more of the dust is captured by the bath surfacebefore it leaves the model region.

This effect is quantified in Figure 11, showing dispersion

of the dust to various boundaries in the model at steadystate.

Location of the dust inlet close to the electrode results infar more efficient dust capture by the bath in the vicinity ofthe arc, with more than 95% settling to the bath surface inthis case. As the inlet is moved further away, the dust hasgreater opportunity for interaction with recirculationstructures in the plasma gas around the arc column, and as aresult a much larger fraction of the incoming dust is capturedinto these recirculation structures and convected out of theside and top boundaries.

Effect of dust densityIn general a dust stream entering a DC furnace as part of theraw material feed will be polydisperse, that is, it will consistof a distribution of particle types and sizes. It is therefore ofsome value to examine the effect of changing the dustproperties, which affect the settling velocity US, on the dustbehaviour in the model. Dust density may be taken as acrude proxy for the composition of the particles – lowdensities of the order of 1500 kg/m3 are typical forcarbonaceous reductants, while higher densities of 7000kg/m3 or more are common for metallic components in thefurnace feed. Different densities were tested in the modelwhile keeping all other parameters constant as per the basecase. The standard 100 ms arc time-averaging calculationwith 25 ms of averaging was used, together with anadditional 100 ms run time for the dust transport model.

Figure 12 shows the steady-state dust concentrationprofiles in the arc region for a low-density and a high-densitydust.

It can be seen that the low-density dust with its lowersettling velocity remains largely contained in the recirculationcell near to the electrode surface – very little dust actuallyenters the arc column. In contrast, the high-density dustsettles through the recirculation layer but is then stronglyconvected toward the side of the model region.

Distribution of the dust flux over the various boundarysurfaces in the models at steady state is shown in Figure 13.

Lower density components in the dust stream were seento remain entrained in the velocity field of the arc as theypass through the model region. In extreme cases, the low-density material may be circulated out of the top of the regionand back into the freeboard space above. Generally, higherdensity components will still experience significantentrainment and bypassing, but they will tend to be carriedout of the sides of the arc region close to the bath surface and

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402 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 8 – Steady-state dust concentration fields at various currents. Scale 0 (grey) to 0.5 (black) kg/kg

Figure 9 – Distribution of dust flux as a function of current

are likely to settle out rapidly as the arc’s velocity field decaysaway from the electrode.

Effect of dust particle diameterThe polydisperse nature of the dust stream entering the arcregion extends to particles of different diameter. This candrastically affect the dust behaviour in the region, as settlingvelocities scale with the square of the diameter as perEquation [5]. The dust transport model was used to examinethe behaviour of a variety of dust particles with diametersranging from 50 µm up to 1250 µm. As before, all otherparameters as per Table II were held constant, with only thedust diameter dD varying. 100 ms time-averaging runs tocalculate the arc flow and temperature fields were used asbefore, together with additional 100 ms runs for eachdiameter model case.

Figure 14 shows the dust concentration fields at steadystate for particles at the two extremes of size. Substantialdifferences are obvious; the smaller particles, unable to settlefast enough to overcome the recirculation flow that the arc

creates in the surrounding plasma gas, are immediatelyentrained and convected out of the arc region by the medium-velocity gas flow near to the electrode. The larger particles,with much higher settling velocities, pass easily through therecirculation regions before being drawn toward the arc bythe high-velocity flow in the vicinity of the main arc jet.

The distribution of the dust fluxes on the model boundarysurfaces at steady state are shown as a function of the dustparticle diameter in Figure 15.

A distinct trend may be observed in the data, with smallerparticles being so easily entrained by the flow patterns in andaround the arc that they are completely bypassed out of theupper boundary of the region by convection. As the particlesize increases past 500 μm, an increasing amount of dust isable to reach and settle on the bath surface, and the quantityconvected through the top boundary drops away rapidly.

For a furnace feed stream containing dust across a widerange of size fractions, these differences in behaviour mayresult in substantially different fates for the different sizes.

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Figure 10 – Steady-state dust concentration fields for various inlet locations. Scale 0 (grey) to 0.5 (black) kg/kg

Figure 11 – Distribution of dust flux as a function of inlet location

Figure 12 – Steady-state dust concentration fields for various dust densities. Scale 0 (grey) to 0.5 (black) kg/kg

Figure 13 – Distribution of dust flux as a function of dust density

Interaction of dust with the DC plasma arc – a computational modelling investigation

Smaller particles will tend to become suspended in thefreeboard gas and lost via the furnace off-gas system, whilelarger particles may be entrained into the arc regiontemporarily but will ultimately be captured by the bathsurface or furnace sidewalls, possibly some distance from thearc itself.

Effect of the dust on the arc – two-way couplingIn order to study the feedback effects of the dust interactingwith the plasma arc, the fully-coupled dust-arc model is used.Since for these cases the dust transport equation [4] cannotbe decoupled from the plasma arc equations [3], [8], and [9],a single unified model algorithm is used to solve themomentum, energy, electromagnetic, and dust fields simulta-neously. This improves the level of detail and temporalaccuracy of the model results, but at the cost of significantlyincreased computational complexity and longer run-times.

Effect of coupling termsFor the initial study, base case parameters were kept identicalto those used for the one-way coupling cases (Table II). Theonly additional parameter required for the two-way couplingcase was an indicative heat capacity for the dust particles,which was taken as 1100 J/kgK for the typical oxidematerials found in metallurgical ores. This value is approx-imated as constant across the range of temperatures typicallyencountered in plasma arcs, although in reality the dustparticles would be in a molten or gaseous state at the highertemperatures.

The effect of introducing different degrees of coupling wasexamined by starting with the model base case with nocoupling, i.e. Equations [1]–[4], followed by cases with energycoupling only (Equations [1], [3], [4], and [9]), momentumcoupling only (Equations [2], [3], [4], and [8]), and finally the

fully coupled system. Each case was run for 100 ms of modeltime, using as initial conditions the established velocity,temperature, and electromagnetic fields from an earlier run ofthe arc model without any dust present.

Some example images showing the instantaneoustemperature and dust concentration fields at the same timefor each case are shown in Figure 16. It can be seen thatalthough identical model parameters and initial conditionswere used, activating various coupling terms results in thefields in the model producing very different patterns later inthe simulation. Inspection of the general shape of the dustconcentration field suggests that energy coupling has arelatively small effect, whereas momentum coupling changesthe dust distribution patterns to a greater degree. Activatingboth coupling terms results in a visibly more turbulent andchaotically mixed dust concentration field, suggesting thatthere is increased transient behaviour of the flow and energyfields. This indicates that the presence of dust in the arc isable to affect its stability detrimentally in the model, bycausing larger and more rapid fluctuations in the velocity andtemperature fields.

This observation is borne out more quantitatively byexamining the time dependence of the peak temperature, peakvelocity, arc voltage, and dust flux at the bath surface for thevarious model cases. These results are shown in Figure 17.

By comparing the solid grey line (one-way coupling only)on the graphs to the others, particularly the solid black line(full coupling), it can be seen that while the coupling termsare not large enough to appreciably change the averagevalues of the fields over long time periods, they do signifi-cantly affect the variability of the fields over shorter periods.The arc model with full coupling is noticeably less stable thanthe model with one-way coupling only.

The entrainment of dust into a plasma arc may thereforebe expected to produce an increase in high-frequencyelectrical noise in the furnace circuit, as well as an increasedprobability of arc extinction, due to the interaction betweenthe dust and the arc.

Effect of dust inlet concentration in fully coupled modelIn contrast to the one-way coupling case, the dust transportmodel in the fully coupled case is not linear due to theinteraction terms in the momentum and energy equations.Therefore, the inlet dust concentration is able to affect thesystem behaviour in different ways – low concentrationswould be expected to result in minimal effects, high concen-trations in exaggerated effects. This was studied in the modelwith full coupling by varying the dust inlet concentration

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404 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 14 – Steady-state dust concentration fields for various dust particle diameters. Scale 0 (grey) to 0.5 (black) kg/kg

Figure 15 – Distribution of dust mass flux as a function of dust particlediameter

while keeping all other parameters constant, and examiningthe evolution of the various fields over time.

Visualizations of the dust concentration and temperaturefields at different times in the models are shown in Figure 18.

The visualizations demonstrate that while the variousfields in the fully coupled model start out with very similardistributions in space, different behaviour evolves as soon as

the dust cloud begins to interact with the arc between 5 and10 ms into the simulation.

There is considerable turbulent mixing by the high-velocity flow in and around the arc jet in both cases, but it isinteresting to note that in the high-concentration case the arcjet is occasionally deflected away from the dust inlet at anangle of 45° or more (e.g. Figure 18d, c.f. Figure 18b). This

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Figure 16 – Effect of activating different coupling terms on fields calculated by plasma arc model. Dust concentration at left, scale 0 (grey) to 0.5 (black)kg/kg. Temperature at right, scale 3000 (blue) to 15000 (red) K

Figure 17 – Graphs of field variable values as functions of time for different coupling model cases, last 20 ms of model evolution

Interaction of dust with the DC plasma arc – a computational modelling investigation

deflection is a transient phenomenon, but is observed tooccur more frequently during the high-concentration case. Apossible explanation for this behaviour is that as the dustconcentration increases, it forms a physical and thermalbarrier for the arc plasma due to the two-way coupling in themodel. This results in the arc seeking the path of leastresistance, deflecting away from the ‘cold wall’ that thefalling column of dust represents.

The time dependence of the peak temperature, peakvelocity, arc voltage, and dust flux at the bath surface for thedifferent dust inlet concentration cases is shown in Figure 19.

It can be seen – especially in the arc voltage and dust fluxgraphs – that increasing the dust inlet concentration producesgreater fluctuations relative to the mean value in the fullycoupled model over the course of the 100 ms simulationperiod. This indicates that more extreme and violent changes

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406 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 18 – Effect of dust inlet concentration on fully coupled model at various times. Dust concentration field at left, scale 0 (grey) to c0 (black).Temperature field at right, scale 3000 (blue) to 15000 (red) K.

Figure 19 – Graphs of field variable values as functions of time for different dust inlet concentrations

are occurring in the morphology of the fields that define theplasma arc. Together with the deflection behaviour observed,this would suggest that furnaces operating with high dustconcentrations in the freeboard gas are likely to exhibitsomewhat more instability and possibly even difficulties witharc extinction.

It is interesting to note that while both the average andpeak arc voltages predicted by the high-concentration caseare somewhat higher than those of the low-concentrationcase, it is a weakness of the present model that the effect issmaller than might be expected. From observations of realfurnaces, the introduction of feed (together with dust) intothe freeboard generally results in a significant increase in theresistance of the arc by both cooling of the plasma andincreased turbulent flow.

ConclusionsDevelopment of a continuum model for dust transport in thearc region of DC plasma arc furnaces was largely effective.The coupling between the governing equations of the plasmaarc and the dust concentration field was developed in detailfor cases of one-way and two-way coupling. The solvers weresuccessfully implemented using the OpenFOAM finite volumemethod framework, and parallelized for distributedcalculation on high-performance computing facilities.

The partially decoupled model was used to study theimpact of a wide range of operational and materialparameters on quantitative and qualitative aspects of thebehaviour of the dust field. Dust particle properties inparticular were seen to have a significant effect on the distri-bution of the dust through the arc region, as well as thequantity of dust bypassed relative to that captured by thebath surface. Location of the feed inlet was also seen to havea substantial impact on the dust circulation, with inletslocated close to the electrode resulting in substantiallyimproved capture of dust by the molten bath surface.

A brief examination of the effect of the dust on the arcvia full coupling between the dust concentration field and theenergy and momentum equations demonstrated that thepresence of dust in the plasma gas could cause increased arcinstability. Higher dust concentrations were seen toexacerbate this effect in the model results.

Much work remains to be conducted in this area.Extension of the coupling models to include separatetemperature and velocity fields for the dust would improvethe accuracy of the calculations. Inclusion of some form ofempirical turbulence modelling (either Reynolds-averaged orlarge eddy simulation variants) would be valuable for scalingthe simulations to larger, industrial-scale furnaces.Combining the DC plasma arc and dust models with a free-surface multiphase flow model to more accurately calculatethe shape of the bath surface below the arc would also be ofinterest, in order to examine the role that the dynamics of thearc cavity plays in dust dispersal and capture. Experimentalstudy by injecting dust into the vicinity of an operating arcwould also be of great value in proving or disproving thehypotheses arising from the model results.

AcknowledgementsThis work is published by permission of Mintek. Interactionswith personnel and facilities at the CSIR/Meraka Institute

Center for High Performance Computing and the CSIRAeronautic Systems Group during the development phase ofthis work were useful and are greatly appreciated.

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IntroductionBlast furnaces are predominantly used forironmaking, with more than 90% of theworld’s clean iron units produced in theseprocesses (Kogel et al., 2006). High-gradelump iron ore, high-grade ore fines, andconcentrates from lower grade ores are utilizedas feed to blast furnaces. These ore fines areagglomerated typically through sintering orpelletization to improve the blast furnaceburden permeability, reducing the cokeconsumption rate and improving reductionrate. Sinter is used as blast furnace feed atpercentages of up to 90%, and contributes toreduced operating costs (Kogel et al., 2006).

Research and development for thesintering process on the laboratory or pilotscale is typically conducted by means of asinter pot test. The VIU (Value In Use) team atKumba Iron Ore routinely uses sinter pot tests

to evaluate new sinter raw materials, primarilyin terms of production rate, coke consumption,and product sinter physical properties.Optimum sinter blends can be investigated aswell as the impact of changes in sinter blendson the sintering performance and sinterquality, providing information for marketing ofthe iron ore. As such, the VIU sinter pot testequipment is in high demand.

Sinter pot tests are time-consuming, thusthe turnaround time can be slow. The tests arealso very costly. For these reasons, it is clearthat a modelling tool that could predict the keyoutcomes of sinter tests with a reasonablelevel of accuracy would be a valuable tool forassessing the performance of sinter blends inthe initial phases of a project. It is not expectedthat the model could replace the pot test, butthat it would complement the pot test workand possibly reduce the number of actual testsrequired. This paper discusses the work doneto date towards this goal.

The sinter pot testThe continuous industrial sintering beltprocess is approximated by using a batch ’pot’test in which the material contained in thestationary sinter pot passes through the sameprocess stages as it would on a sinter belt.Sinter pot test measurements are used as basisfor the validation of one-dimensional (1D)models (Patisson et al., 1991; Zhou et al.,2012a). The sinter pot test is illustrated inFigure 1.

The procedure for the sinter pot test is asfollows. After the equipment is prepared, theraw material blend is manually weighed andfed into the test pot via a high-intensity mixerand a granulation drum. Once the pot has been

A finite difference model of the iron oresinter processby J. Muller*, T.L. de Vries*, B.A. Dippenaar*, and J.C. Vreugdenburg*

SynopsisIron ore fines are agglomerated to produce sinter, which is an importantfeed material for blast furnaces worldwide. A model of the iron oresintering process has been developed with the objective of being represen-tative of the sinter pot test, the standard laboratory process in which thebehaviour of specific sinter feed mixtures is evaluated. The model aims topredict sinter quality, including chemical quality and physical strength, aswell as key sinter process performance parameters such as production rateand fuel consumption rate. The model uses the finite difference method(FDM) to solve heat and mass distributions within the sinter pot over theheight and time dimensions. This model can further be used forestablishing empirical relationships between modelled parameters andmeasured sinter properties. Inputs into the model include the feed materialphysical properties, chemical compositions, and boundary conditions. Sub-models describe relationships between applied pressure differential andgas flow rate through the bed of granulated fine ore particles, combustionof carbonaceous material, calcination of fluxes, evaporation and conden-sation of water, and melting and solidification. The model was applied totypical sinter test conditions to illustrate the results predicted, and to testsensitivities to parameters such as feed void fraction, feed cokepercentage, and the fraction of combustion heat transferred to the gasphase. A model validation and improvement study should follow, ensuringsinter test results are free from experimental errors by conducting repeatedtests.

Keywordsiron ore sintering, finite difference method, coke combustion, calcination,evaporation and condensation, melting and solidification, sinter strength.

* Kumba Iron Ore, Anglo American plc.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. This paperwas first presented at the, PyrometallurgicalModelling Principles and Practices, 4–5 August2014, Emperors Palace Hotel Casino ConventionResort, Johannesburg.

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ISSN:2411-9717/2015/v115/n5/a8http://dx.doi.org/10.17159/2411-9717/2015/v115n5a8

A finite difference model of the iron ore sinter process

filled, key settings, including the ignition time andtemperature and the pressure drop set-points of the variousstages, are entered on the sinter control system via theSCADA interface. Typically, the applied pressure differentialbelow the pot is pressure-controlled rather than flow ratecontrolled, and the material in the pot is ignited using LP gas.

After ignition, the temperature is manually controlled bythe operators by regulating the gas flow rate. The ignitionstage ends automatically after the prescribed time has elapsedand the process proceeds to the sintering stage. The end ofsintering is defined as the time at which the peak exhaust gastemperature is measured, indicating that the flame front hastravelled all the way through the sinter cake. An optionalcooling cycle may be included after this, where cold aircontinues to be drawn through the sinter cake. However, thisis used only occasionally.

After sintering is completed, the sinter cake is removedfrom the pot using an overhead crane. The cake thenproceeds to physical and metallurgical testing. This startswith a set of drop tests followed by a drum breakdownprocedure; after which the sample is screened to determinethe size distribution of the final sinter. The FeO content of thesample is measured by titration. Additional tests areconducted: a tumble index (TI ) test as per ISO 3271, areducibility index (RI ) test as per ISO 4695 and ISO 7215,and a reduction disintegration (RDI ) test as per ISO 4696-1and 4696-2. If required, REAS (Reduktion, Erweichung, undAbschmelzen – meaning reduction, softening, and melting)tests are also carried out to determine the softening andmelting characteristics of the sinter.

The performance of a sinter in the pot test is defined byseveral measures, including productivity, fuel rate, and thestrength and reactivity of the sinter as defined by the physicaland metallurgical tests listed above. In order for a sintermodel to be useful to VIU, it ultimately needs to be able topredict all of these parameters with a reasonable degree ofaccuracy. To validate the sinter model, sinter pot tests wouldtypically be conducted in which additional thermocouples aremounted in the pot at various depths so that the measuredtemperature profiles in the sinter cake can be recorded andcompared with those predicted by the model. Thermocouples

are typically inserted at depths of 100 mm, 230 mm, and 420mm, measured from the top of the sinter pot.

Sinter modelling

General principlesThe sinter pot test can be simulated by modelling the massand heat transfer phenomena occurring within the sinter bedover time as the boundary conditions change. Chemicalreactions, heat transfer, and physical transformation can bemodelled, and the temperature profiles, chemicalcompositions, gas flow velocities, etc. predicted. Physicalstrength parameters of the final sinter can be estimatedthrough correlations with some of these predicted variables.Previous researchers have developed several such modelsdescribing iron ore sintering with various degrees ofcomplexity (Dash et al., 1974; Thurlby, 1988; Patisson et al.,1991; Clixby and Young, 1992; Yang et al., 2004; Majumderet al., 2009). More recently, Zhou et al. (2012a) also reportedon the development of such a model, and evaluated such workcurrently available in the literature (Zhou et al., 2012b).

The sinter pot test itself is a transient problem with heatand mass transfer occurring in all three dimension, butpredominantly in the height dimension as the flame fronttravels downwards through the sinter bed. It is thereforetypical to assume variations over the area of the sinter pot tobe negligible, and to model the sintering process using atransient 1D approach as illustrated in Figure 2 (Zhou et al.,2012b).

The transformation of a 1D section of solid material(control volume) from the sinter bed (Figure 2a), whichincludes a returned (recycled) sinter fines layer at the bottom(grit), is modelled over time. Gas flow in the model is fromthe top to bottom of this ‘control volume’. The solidstemperature and chemical composition profiles are modelledover time, as well as the change in temperature and chemicalcomposition of the gas flowing from top to bottom. Thevariable profiles along the sinter bed height (e.g. solidstemperature, shown in Figure 2a) are combined for all of thetime steps simulated to produce an illustration such as Figure2b, showing solids temperature. This illustrates the complete

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410 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 1 – Illustration of the sinter pot test

sinter process from start to finish, representative of the‘control volume’ of material, which moves at the speed of thesinter strand. Variables may also be illustrated over time andat a specific depth, for example, the solids temperature asshown in Figure 2c.

Model inputsInitial and boundary conditions that have to be specified asinputs to the model include the following:

➤ Individual feed materials specification (e.g. each ore,flux, fuel, etc.): amount of each constituent in feedmixture (recipe), chemical composition, particle sizedistribution, physical properties (density, porosity)

➤ Granulated feed material (mixed feed to the sinter pot)and bottom grit layer specification: particle size, voidfraction, and sphericity

➤ Sinter product requirement: basicity (CaO/SiO2), andpercentages of FeO, MgO, and SiO2 (optional)

➤ Sinter process/boundary conditions: bed and grit layerheights, ignition duration, ignition gas temperature,chemical composition, pressure at top of bed, andapplied pressure differential below bed (for first andsecond ignition stages, and then normal sintering)

➤ Simulation controls: number of height elements,duration of time step, simulation maximum time,iterative calculation controls, sub-model controlparameters, and results derivation parameters (e.g.sintering time defining temperature).

The amounts of ores, fuels, return fines, burnt lime, andmoisture are specified in terms of the ratios making up thegranulated feed. The amounts of other fluxes (dolomite,silica, lime) are determined for the feed blend prior to modelcalculation, in order to achieve the required sinter product

basicity (CaO/SiO2) and percentages of MgO and SiO2.The model allows for the void fraction to be estimated

from the JPU (Japanese Permeability Unit) that is oftenmeasured for the granulated sinter feed material in separatetest work. The actual pressure drop over the standardized JPUtest sample is calculated using Equation [1] (Lwamba andGarbers-Craig, 2008). The material void fraction is iterativelydetermined using the Ergun equation (Equation [2]) with theother physical material properties of the sinter feed.

[1]

where ΔP: pressure drop (mm H2O)H: height (mm)V: air flow rate (Nm3.min-1)A: area (m2).

Model solutionThe finite difference method (FDM) has been used to solvetransient mass and energy conservation equations. Themodelling approach followed and software algorithm are inprinciple the same as those described in more detailelsewhere (Majumder et al., 2009). An overview of themethod is provided here, but for brevity the conservation andother model equations are not been repeated, unless theywere different to those used in previous studies (Majumder etal., 2009) or of specific importance as stated later.

The control volume (illustrated in Figure 2a) is dividedinto collections of elements representing gas and solidseparately. Variables defined for each element includetemperature, chemical composition, reaction rates, etc. For

A finite difference model of the iron ore sinter process

411The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

Figure 2 – Illustration of the 1D transient sintering process model

A finite difference model of the iron ore sinter process

solid elements, the liquid fraction is also defined as avariable, whereas for the gaseous elements, variables are alsodefined for velocity and pressure.

The combustion of carbonaceous material with oxygenfrom the air drawn in at the top of the sinter bed primarilydetermines the amount of heat generated, causing melting ofsolid phases and therefore forming the basis of the sinteringprocess. The air flow velocity is controlled by the appliedpressure differential at the bottom of the sinter bed, and isinfluenced by the physical properties of the solids, and byheat transfer and the chemical reactions occurring. The modeliteratively determines the mass flow rate of gas drawn intothe sinter at each simulated time step (Figure 2a). The gas-solid heat and mass transfer is calculated in each element bymoving from the top to bottom and using sub-models todescribe physical phenomena. After calculation of all the sub-models and the resulting heat and mass transfer between thesolids and gas, the pressure drop is calculated over each solidmaterial element, and the air flow rate into the bed adjusteduntil the total calculated pressure drop converges to thatspecified as the model input. The pressure drop is calculatedas follows using the Ergun equation (Majumder et al., 2009):

[2]

where ΔP: pressure drop (Pa)L: height (mm)μg: gas viscosity (Pa.s)ε: bed void fractionø: particle sphericitydp: mean particle diameter (m)U: gas velocity (m.s-1)ρ: gas density (kg.m-3).Another important aspect of the energy balance calculated

in each element is the assumptions made with respect to theheat of reaction from chemical reactions. Solid and/or gasphase species react with each other to yield products that arealso in either the solid and/or gas phase (e.g. Equation [3]).In the energy balance of the model, the heat of reaction issplit between the enthalpies of the solid and gas products(Yang et al., 2004). For each of the chemical reactionsidentified, a fraction yi is specified as the fraction of the heatdistributed to the gas, with the balance to the solids. For thechemical reactions the model parameters are defined asimportant model inputs that greatly affect the predictedtemperature profiles – ydol: dolomite calcination; ycomb: cokecombustion; yevap: evaporation; ycond: condensation.

[3]

Sub-modelsSub-models are formulated to model the gas-solids heattransfer and mass transfer due predominantly to chemicalreactions as the gas enters at the top and moves through thesinter bed. Convective heat transfer between gas and solids ismodelled only, with conduction and radiation excluded forsimplification. Depending on the nature of the chemicalreactions, heat is generated or consumed by the chemicalreactions and distributed between the resulting gas and solidphases. Chemical reactions are modelled for water

evaporation and condensation, dolomite calcination, cokecombustion, and melting and solidification of all materials,including Fe raw materials. The physical transformation ofthe solids (e.g. bed shrinkage) and its effect on the bedpermeability is not modelled here due to the unavailability ofexperimental data. The details on each these sub-model asimplemented in the sinter model are provided below.

Convective heat transferHeat transfer between the downward-moving gas and solidsparticles in the sinter bed occurs mostly by means of forcedconvection. Heat transfer rate is calculated in each finiteelement from the relevant solids and gas temperatures, and amodelled heat transfer coefficient, hgs, using the modelreported by Majumder et al. (2009) and Pattison et al.(1991):

[4]

where hgs: convective solid-gas heat transfer coefficient

(W.m-2.K-1)ø: particle sphericityvg: gas viscosity (Pa.s)ρg: gas density (kg.m-3)Cpg: gas specific heat (J.kg.-1.K-1)εb: bed void fractionξ: channelling length factor.

Water evaporation and condensationWater evaporates from the wet granulated feed in the warmerareas of the sinter bed around where the flame frontprogresses downwards through the sinter bed. As the gasflows through the unsintered bed, it carries this water vapourdownwards into cooler areas of the bed below the flame front,where condensation becomes possible.

The rates at which evaporation (drying) or condensationoccur in each solids element are estimated using the modelreported by Patisson et al. (1990). Firstly, the mass transferrate in the gas boundary layer is modelled as a function ofthe gas temperature and pressure, the gas-solid heat transfercoefficient, and the logarithmic mean of the actual andsaturated molar fractions of moisture in the gas phase(Patisson et al., 1990):

[5]

where k: boundary gas layer mass transfer rate (m.s-1)h: heat transfer coefficient (W.m-2.K-1)Tg: gas temperature (K)P: absolute gas pressure (Pa)Xm: logarithmic mean of actual and saturated molar

fraction moisture in vapour.The rate of moisture transfer is then calculated,

depending on the actual moisture partial pressure (Pattison etal., 1990):

[6]

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412 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

where rR: rate of moisture transfer (mol.m-3.s-1)a: bed specific area (m2.m-3)k: boundary gas layer mass transfer rate (m.s-1)R: ideal gas law constant (J.mol-1.K-1)Tg: gas temperature (K)ph2O

sat: H2O saturation partial pressure in gas (kPa)ph2O: actual H2O partial pressure in gas (kPa).This is then used to model the rate of moisture transfer

between the gas and solids phases, and is also dependent onthe specific area of the solids, and the actual and saturationH2O partial pressures in the gas phase. Condensation ismodelled to occur at a constant rate, independent of theamount of moisture in the solids. Evaporation is modelledalso to occur at a constant rate up to a critical moisturecontent, after which the rate decreases in the ratio, P(Wr)(Patisson et al., 1990):

[7]

where Wr is the ratio of the actual moisture content to thecritical moisture content. A critical moisture content of 5%was used here (as per Majumder et al., 2009).

Limestone and dolomite calcinationCaCO3 and MgCO3 are present in some of the sinter feedmaterials, typically as dolomite and lime. These species startto decompose at a certain temperature in the sinteringprocess, enriching the gas with CO2. The decomposition ratesare modelled for CaCO3 and MgCO3 in each solids finiteelement using simple rate equations as functions of the solidstemperature at that point (as per Thurlby, 1988):

[8]

[9]

where kls: calcination rate constant of CaCO3 (1.s-1)kms: calcination rate constant of MgCO3 (1.s-1)Ts: solids temperature (K).

Coke combustionCoke (and possibly other carbonaceous materials) is added tothe sinter feed mixture as the fuel source. The carbon reactswith oxygen to produce CO and CO2, generating the heatrequired to initiate melting of the solids and assimilation ofthe remaining particles. Sinter models reported in theliterature apply different combustion models, which alsoconsider the generation and further combustion of CO, aswell as the Boudouard reaction whereby CO2 is convertedback to CO. Here the combustion model by Gibb and Field hasbeen applied (as per Mei et al., 2010). In this approach theabove combustion reactions are combined as follows, wherethe degree of oxidation is represented by the parameter ø:

[10]

The parameter ø is modelled as a function of temperature(Equation [5]). At lower temperatures CO2 is predicted as the

main product, while at higher temperatures (from around1000K) mostly CO is predicted as the Boudouard reaction ispredominant (Zhou, 2012a).

[11]

where Tp is the particulate (solids) temperature (K), and Asand Ts are model parameters with values of 2500K and6240K respectively, as suggested by Mei et al. (2010).

The overall coke combustion rate is determined by thediffusion rate of oxygen to the reaction surface and by thecarbon reaction rate. The oxygen diffusion rate equation issolved analytically to yield the coke combustion reaction rateequation in terms of the external oxygen diffusion rate, thesurface reaction rate, and the rate of internal diffusion andsurface reaction (Mei et al., 2010). The coke combustion rateis calculated with the Gibb model as a function of theparameter ø and other parameters, including the coke particlephysical properties, far field oxygen concentration, externaland internal gas diffusion coefficients, and the particle(solids) temperature. The following set of equations, asreported by Mei et al. (2010), describes the model:

[12]

[13]

[14]

[15]

[16]

[17]

where

: rate of carbon mass, mc, decrease (kg.s-1)

ø: oxidation mechanism parameter, molar ratio of C to O2 atoms reacting

ε: coke particle void fractionMc: carbon molar mass (kg.kmol-1)Mo2: oxygen molar mass (kg.kmol-1)C∞: far field oxygen (O2) concentration in the bulk gas

(kg.m-3)ρc: coke solid density (kg.m-3)k1: rate of external diffusion (1.s-1)k2: rate of surface reaction (1.s-1)k3: rate of internal diffusion and surface reaction

(1.s-1)DO2/N2: external diffusion coefficient of O2 through N2

(m2.s-1)DO2–C: internal diffusion coefficient of oxygen through

coke particles (m2.s-1)Rp: average coke particle radius (m)

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A finite difference model of the iron ore sinter process

kc: carbon oxygen rate (1.s-1)Ac: carbon oxidation rate equation pre-exponential

factor, 14 m.s-1

Tc: carbon oxidation rate equation constant, 21 580KTp: coke particle temperature (solid temperature) (K)β: reaction parametera: coke particle volume to internal surface ratio

(m3.m-2).The diffusivities used above are calculated as follows as

functions of the gas temperature, Tg. The external diffusioncoefficient of O2 through N2 (Majumder et al., 2009):

[18]

The internal diffusion coefficient of O2 through cokeparticles (Dash et al., 1974):

[19]

Melting and solidificationMelting and solidification are two of the most importantphenomena in sintering since they enable particle assimi-lation and directly affect the physical properties of the sinterproduct. When the fines reach a certain temperature theprimary melt forms, with its properties being a function of thetemperature achieved and its chemical composition(Debrincat et al., 2004). The primary melt chemicalcomposition is in turn influenced by the gradual assimilationof different individual particles into the melt, andsubsequently also the bonding phases formed during solidifi-cation and the properties of the sinter product, e.g. sinterstrength and reducibility (Debrincat et al., 2004).

This complex process of melting and solidification issimplified firstly by not specifically modelling the rate atwhich it occurs, based on the implicit assumption that heattransfer is rate limiting (modelled separately). A method byZhou et al. (2012a) is used here to estimate the fractionmelted as a function of the solids temperature and chemicalcomposition of the fines fraction:

[20]

where β is the mass fraction of melted material, Ts is thesolids temperature, Tsol is the solidus temperature of around1100°C, and Tliq is the liquidus temperature of the -1 mmfines fraction of around 1400°C (Zhou et al., 2012a). Thevalue for the phase change factor, α, depends on factors suchthe type of iron ore, particle size, ore porosity, and solidschemical composition (Zhou et al., 2012a). Due to thesecomplexities, a value of 1 has been assumed for α, similar tothat used by Pattison et al. (1991) for estimating the fractionmelted during solidification.

For each solids element, the fraction melting orsolidifying is derived from the above relationship for thatmelted in the current and previous time steps. The heat ofmelting or solidification is derived and included in the energybalance of each element. An average of 0.254 MJ.kg-1 is usedas the latent heat of melting, and 0.117 MJ.kg-1 for the latent

heat of solidification. These values are the averages derivedfrom other sources, as reported by Zhou et al. (2012a).

Model outputsThe model produces several outputs, which can be used toevaluate overall performance of the sinter mix and predictedsinter product properties with regard to the inputs specified,or to analyse the interrelated phenomena occurring in thesinter bed in order to make process improvements. In future,it may be possible to correlate some calculated modelvariables with some measured sinter properties, and theserelationships may then be included in the model.

The model variables available for each element include:➤ Solids variables: temperature, and chemical

composition (wt.%) ➤ Gas variables: temperature, pressure, chemical

composition (vol.%), velocity, and inlet mass flow rate➤ Reaction rates: evaporation/condensation, limestone

and dolomite calcination, and coke combustion reactionrates

➤ Other parameters: heat transfer coefficient, fractionmelted and solidified, enclosed area above 200°C and1100°C (as described in Zhou et al., 2012a).

Some of the more important model results calculated(before or after the simulation) are:

➤ Bed void fraction: calculated from the granulated feedmaterial JPU prior to the simulation, to be used then asa model input

➤ Coke consumption rate: coke usage rate calculated asthe specified mass of coke feed divided by thecalculated mass of sinter produced, with units of kg.t-1sinter

➤ Sinter product mass and chemical composition:calculated from the feed material mixture specification,subtracting ignition loss components (e.g. carbon andcarbonates)

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Table I

Model input parameter values

Parameter Units Value

Feed burnt lime wt.% (wet) 3.0Feed total return fines wt.% (wet) 26Feed total coke wt.% (wet) 4.2Feed moisture wt.% (wet) 6.0Sinter basicity target 2.0Sinter FeO target wt.% 6.0Sinter MgO target wt.% 1.8Sinter SiO2 target wt.% 5.5Sinter bed height m 0.5Grit height m 0.05Sinter bed diameter m 0.3Granulated feed bulk density kg.m-3 2000Feed void fraction 0.3Feed particle diameter m 0.004Feed particle sphericity, Ø 0.8Sinter bed JPU 31.3Combustion heat fraction to gas, ycomb 0Calcination heat fraction to gas, ydol 0Evaporation heat fraction to gas, yevap 0Condensation heat fraction to gas, ycond 0Sinter bed suction pressure mm H2O 1010Ignition temperature °C 1098

➤ Sintering time: indicative of the speed with which theflame front travels through the bed and the sinteringrate. Calculated as the time until the bottom of the bedcools to a specified temperature (e.g. 150°C)

➤ Burn-through times: similar to the sintering time, butcalculated as the time to reach the maximumtemperature below the sinter bed

➤ Production rate: rate of sinter produced calculatedusing the predicted mass of sinter produced andsintering time, typically units of t.m-2 per 24 h

➤ Variable averages: variables (from the above list)averaged over the height of the sinter bed at the end ofthe simulation.

Results

Typical sinter pot testThe sinter model was configured to represent typical sintertest conditions to illustrate the results that could begenerated. The most important model input parameters withtypical values are summarized in Table I. In Table II thechemical composition of the iron-bearing feed material isprovided. The sinter feed void fraction has been specified as0.3 (Table I), similar to what was estimated for the specifiedfeed JPU of 31.3, particle size of 0.004 m, and sphericity of0.8 using the method described earlier and shown as a resultparameter in Table III.

The model, predicted results are summarized in Table III.Together with the typical sinter performance parameters, theenclosed areas calculated above 1100°C are reported atdifferent depths and on average over the sinter bed, whichcould be correlated to physical strengths measured of sintersproduced in the test work.

Figure 3 illustrates the solids temperatures predicted bythe model at depths of 100, 230, and 420 mm. This showsthat the sinter bed temperatures increase sharply to over1200°C, when melting starts to consume energy from thecombustion reactions. As the coke is consumed, temperaturedecreases and the melted material solidifies.

Model sensitivitiesPrior to conducting test work for model validation, the model(as defined in the previous section) was used to evaluate thesensitivities of certain model parameters. This evaluation wasperformed to provide guidance for subsequent experimentalplanning, model validation, and model improvements.

The void fraction of the sinter feed material could bederived from the solid and bulk densities, correlations withparticle size, or from the actual JPUs as described earlier.These methods are likely to result in different values, and thebed void fraction is expected to change during sintering. Themodel was therefore used to estimate the sensitivity to voidfraction by changing the baseline value of 0.3 by 10% in bothdirections. The temperature profiles predicted at a bed depthof 230 mm in Figure 4 show significant variations, with thetimes at which peak temperature is reached differing by

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Figure 3 – Sinter bed temperatures predicted at 100, 230, and 420 mm depths

Table II

Iron-bearing feed material composition

Species wt.%

FeO(s) 2.12Fe2O3(s) 86.96P(s) 0.044S(s) 0.014K2O(s) 0.23Na2O(s) 0.046CaO(s) 0.46MgO(s) 0.22MnO(s) 1.16Al2O3(s) 1.48SiO2(s) 5.03

Table III

Model result parameter values

Parameter Units Value

Specific mass sinter produced kg.t-1 feed 895.3Feed void fraction – estimated from JPU 0.30Sintering time min 25.7Burn-through time min 23.2Burn-through rate mm.min-1 21.6Production rate t.m-2 per 24 h 37.5Coke consumption rate kg.t-1 sinter 69.1Sinter yield wt.% 87.3Enclosed area over 1100°C – 100 mm depth min.°C 33.9Enclosed area over 1100°C – 230 mm depth min.°C 109.9Enclosed area over 1100°C – 420 mm depth min.°C 250.5Enclosed area over 1100°C – av. min.°C 130.5

A finite difference model of the iron ore sinter process

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416 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 6 – Predicted temperature at 230 mm bed depth for fractions ofcombustion heat to gas of 0, 0.25, 0.5, 0.75, and 1

Figure 7 – Predicted productivity as a function of the combustion heatfraction to gas

Figure 8 – Predicted temperature at 230 mm bed depth for feed totalcoke percentages of 3.8, 4.2, and 4.6%

Figure 9 – Predicted productivity as a function of the feed total cokepercentage

Figure 4 – Predicted temperature at 230 mm bed depth for feed voidfractions of 0.27, 0.3, and 0.33

Figure 5 – Predicted productivity as a function of the feed void fraction

around 100 seconds. Figure 5 shows that the production ratedecreases significantly for a lower void fraction, andincreases for a higher void fraction.

Another model parameter with uncertainty is the fractionof combustion heat that is transferred to the gas. Figure 6illustrates the change in temperature profiles predicted at abed depth of 230 mm when this parameter is varied between0 and 1. This shows that values larger than around 0.25 areless likely since the predicted peak temperatures are lowerthan what would normally be obtained in sinter test work. Infurther model development work the value of this parametercould be found for the best fit between predicted andmeasured temperatures. Figure 7 illustrates that theproduction rate increases slightly with higher fractions ofcombustion heat transferred to the gas.

The percentage of coke in the feed was also varied within±10% of the baseline value of 4.2%. Figure 8 shows somevariation in the predicted temperature profiles at a bed depthof 230 mm, with a generally wider profile and higher peaktemperature predicted for higher percentages of coke as moreheat is generated for a longer duration. This higherpercentage coke results in slightly lower production rates asburn-through takes longer (Figure 9). This illustrates howthe model could be used to find the optimum amount of cokein the feed, as well as the sensitivity to inaccuracies in thefeed recipe.

Conclusions

A model of the iron ore sintering process was developed andsolved using the finite difference method. The model wasapplied for typical sinter test conditions to illustrate resultspredicted, and sensitivities of certain parameters evaluated inpreparation for test work to validate and further develop themodel. Results were highly sensitive to the feed void fraction,which would therefore have to be determined with a highaccuracy when performing test work for model validation.Further results indicated that the fraction of combustion heattransferred to the gas could possibly be varied between zeroand around 0.25 to obtain an improved fit between predictedtemperatures and those measured experimentally duringmodel validation. The results also showed that widertemperature profiles with higher peak values are predicted forincreased amounts of coke in the feed, resulting in lowerproduction rates, which would be useful in applying themodel to determine optimum coke feed rates. The outcomesof this study could be used to design a sinter test programmeto validate and improve the sinter model. It is recommendedthat tests be repeated to confirm that the measurements arefree from experimental errors prior to comparison with modelresults.

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MEI, C., ZHOU, J., PENG, X., ZHOU, N., and ZHOU, P. 2010. Simulation and

Optimization of Furnaces and Kilns for Nonferrous Metallurgical

Engineering. Springer, New York. pp. 50–52.

PATISSON, F., BELLOT, J.P., and ABLITZER, D. 1990. Study of moisture transfer

during the strand sintering process. Metallurgical Transactions B, vol.

21B. pp. 37–47.

PATISSON, F., BELLOT, J.P., ABLITZER, D., MARLIERE, E., DULCY, C., and STEILER, J.M.

1991. Mathematical modelling of iron ore sintering process. Ironmakingand Steelmaking, vol. 18, no. 2. pp. 89–95.

THURLBY, J.A. 1988. A dynamic mathematical model of the complete grate/kiln

iron-ore pellet induration process. Metallurgical Transactions B, vol. 19B.

pp. 103–102.

YANG, W., RYU, C., CHOI, S., CHOI, E., LEE, D., and HUH, W. 2004. Modeling of

combustion and heat transfer in an iron ore sintering bed with consider-

ations of multiple solid phases. ISIJ International, vol. 44, no. 3. pp.

492–499.

ZHOU, H., ZHAO, J.P., LOO, C.E., ELLIS, B.G., and CEN, K.F. 2012a. Numerical

modeling of the iron ore sintering process, ISIJ International, vol. 52, no.

9. pp. 1550–1558.

ZHOU, H., ZHAO, J.P., LOO, C.E., ELLIS, B.G., and CEN, K.F. 2012b. Model

predictions of important bed and gas properties during iron ore sintering.

ISIJ International, vol. 52, no. 12. pp. 2168–2176. ◆

A finite difference model of the iron ore sinter process

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www.verrefshaped.co.zaEmail: rietfontein@verrefshaped.co.za

IntroductionRotary kilns are used for a range of mineralprocessing operations. Kilns range in size from2–6 m in diameter and can be 50–225 m longwith an operating mass of up to 3000 t. Two ofthe most common applications are cementproduction and sponge iron production. Hatchuses a proprietary one-dimensional kiln modelto evaluate designs for clients (Haywood et al.,2009). This tool has been utilized for a varietyof pyrometallurgical applications includingferrovanadium, nickel carbonate, nickellaterite, iron ore reduction, and spodumene(lithium) production. In this paper the directreduction of iron ore to sponge iron is used asan example.

The model incorporates a large number ofvariables covering, amongst others: feedproperties and rates, combustion options, kilnoperating information (speed and fill level),blower locations and flow rates, and theenvironmental conditions. Owing to the highlevel of detail included in the model, a largenumber of variables may influence the resultsand it becomes challenging to find optimaldesigns through trial and error. Numericaloptimization techniques are ideally suited toautomate the calculations and allow theanalyst to investigate a large number ofscenarios and goal functions.

The first part of the paper presents anoverview of the kiln modelling method. Thesecond part shows how the process isautomated and combined with a numericaloptimization scheme. The result is a powerfulapproach to kiln operational optimization.Illustrative examples are included for the caseof iron ore reduction in a generic rotary kiln.

Rotary kiln modelKiln modelling consists of two steps. Firstly,an Excel®-based calculation is used todetermine the kiln bed profile and residencetime based on specific operating conditionsand the kiln configuration. This is followed bya calculation of the kiln operating character-istics with a FORTRAN program based on aone-dimensional model of the kiln’s mass andenergy transfer.

The FORTRAN model provides informationon the solids and gas temperature evolutionalong the kiln, as well as a prediction of thereduction profile and off-gas composition. Thebed profile and residence time from the Excelcalculation form part of the input required bythe FORTRAN program. The program allowscomparison of results for a variety of geometricand operating conditions, including bedprofiles, mass flow through the kiln, residencetime, air profiles, ore and coal composition,and others parameters (a total of 108 inputvariables were defined for the example in thispaper).

In the case of a generic iron ore reductionkiln (Figure 1), Table I provides an overviewof the parameters (and ranges) that areconsidered.

Modelling and optimization of a rotarykiln direct reduction processby H.P. Kritzinger* and T.C. Kingsley*

SynopsisRotary kilns are used for a variety of mineral processing operations. Hatchmakes use of a kiln model developed from first principles to evaluatedesigns for its clients. This tool has been applied to a variety of pyrometal-lurgical applications, including ferrovanadium, nickel carbonate, nickellaterite, iron ore reduction, and spodumene (lithium) production.

This paper illustrates the application of numerical optimizationtechniques in combination with the kiln model in the interrogation of ageneric iron ore reduction process. The fundamental modelling conceptsare explained, followed by a description of the optimization approach. Theresults show how the combination of the two methods, with moderncomputing power, can generate a large number of viable design andoperating candidates.

Keywordsdirect reduction, DR process, rotary kiln, modelling, numericaloptimization.

* Hatch Goba, South Africa.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. This paperwas first presented at the, PyrometallurgicalModelling Principles and Practices, 4–5 August2014, Emperors Palace Hotel Casino ConventionResort, Johannesburg.

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Modelling and optimization of a rotary kiln direct reduction process

Kiln residence time and fill levelKiln residence time and fill level affect the progress ofchemical reactions, as well as the maximum throughput for arotary kiln. This is determined by (i) the maximum flow rateof the proposed burden through a kiln, and (ii) the residencetime at temperatures that would allow sufficient reduction totake place.

A calculation method was developed that allows for theprediction of the bed profile and residence time in a rotarykiln. The method is based on the principle of granularmovement in a kiln as described by Saeman (1951) and Scottet al. (2008). It assumes the kiln is operating in the rollingregime, the active layer has zero thickness, and the bed islocally flat. The experimental results provided by Scott et al.(2008) were used to validate the calculation method fordifferent kiln and dam configurations. Figure 2 shows anexample of the correlation between the model results andexperimental data.

The method allows for the treatment of a number ofgeometrical parameters related to the kiln, including:

➤ Variations in kiln length, diameter, inclination, androtation speed

➤ Straight or conical inlet and outlet configurations➤ Stepped sections in the shell➤ Conical or cylindrical-shaped dams inside the shell.

The limitations of the model include:➤ No provision for density changes or material loss along

the kiln length➤ Lifters or chain sections cannot be considered➤ The effects of gas velocity are neglected➤ Transient effects in the bed are neglected.

The kiln under investigation does not have lifters or achain section. Based on the good correspondence with experi-mental data, the other limitations in the model were not

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Figure 1 – Schematic of a rotary kiln

Table I

Model inputs used in the evaluation of the kiln performanceDesign parameters Values

Kiln internal diameter 4.34 mKiln length 80 mKiln inclination 1.5°Solids angle of repose 31°

Operating parameters RangeKiln rotational speed 0.25–0.39 r/minBed residence time 6–10 hC/Fe ratio (based on reducible Fe) 0.5 –0.73Combined feed flow rate: cold feed 48.8–54.0 t/hCombined feed flow rate: hot feed 60.6 t/hCombined feed density range 1895–2100 kg/m3

Exit dam height 0.7–1.0 mOff-gas temperatures 640–1210°C

Figure 2 – Comparison of fill level calculation to experimental data

deemed to be critical for calculating bed profiles in the currentinvestigation. Should greater accuracy be required, a discreteelement method simulation of the bed flow would berecommended.

One-dimensional kiln modelThe kiln model applied in this work was developed by Hatchover a number of years and has been used for several rotarykiln projects. It has its roots in a FORTRAN program that wasdeveloped by Venkateswaran (1978) to study the reductionof iron ore. The original software was used to model theoperation of a 35 m pilot kiln at the Stellco company inCanada. A number of enhancements to the originalVenkateswaran code have been added to increase thereliability and fidelity of the model. Examples of theseenhancements include, but are not limited to:

➤ Incorporation of a bed level profile calculation along thelength of the kiln

➤ Improved radiation modelling to account for viewfactors and gas participation at each axial node

➤ Extensive development of a comprehensive materialdatabase

➤ Inclusion of several additional chemical reactionschemes and kinetics (including ferrovanadium, nickelcarbonate, nickel laterite, and spodumene)

➤ Code optimization to improve solution time by takingadvantage of advances in modern compilers.

The kiln model is a one-dimensional finite-volumerepresentation of the governing equations for mass andenergy conservation, which are approximated by integrationover discrete control volumes along the kiln length (i.e. axialslices). The solution of these discrete equations provides thelocal gas and solids temperatures and mass fluxes at eachaxial location along the kiln length.

In the bed phase the reactions may include the removal ofmoisture (free and bound) from the feed material, calcinationof dolomite, as well as the Boudouard and Fe reductionreactions. The moisture removal from the bed is modelledwith Arrhenius rate equations which describe the progress ofthe free and fixed moisture removal. The rate constants formoisture removal have been validated in earlier projects andwere not modified for the presented calculations.

Where dolomite is included in the feed, it is assumed toconsist of CaCO3 only. Calcination occurs according to thereaction:

CaCO3(s) → CaO(s) + CO2(g)The reaction rate is effected through the use of an

Arrhenius rate equation with constants as per previousapplications of the model.

The Boudouard reaction is included as:C(s) + CO2(g) →2CO(g)The reduction reaction is modelled with a single step from

haematite to metallic iron:Fe2O3 + 3CO → 2Fe + 3CO2The rates of the Boudouard and reduction reactions are

prescribed by appropriate rate terms and determine theevolution of species concentrations and energy along the kiln.

The energy required for material heating, endothermicreactions, and heat losses is provided by the combustion ofcoal volatiles and char, combustion of CO gas from theBoudouard reaction, as well as a portion of the lance coal

injected with ambient air at the discharge end of the kiln. Thekiln model solves a comprehensive set of reaction equationsusing a Gibbs free energy minimization technique at theprevailing gas-phase temperature that yields realisticspeciation of the reaction products. The effect of localconditions within the kiln, where thermal effects andchemical reactions in the bed may produce different speciesin the gas phase, is included in the model.

Application of optimizationThe multivariable nature of a kiln operation can make itsomewhat difficult for the analyst to find optimalcombinations of operational or design variables through trialand error. Solution trends can be nonlinear and variablescoupled in their influence on the solution. This calls for amore rigorous exploration of the design space through designof experiments and associated numerical techniques. Due tothe need for the evaluation of a large number of designpoints, these techniques are well suited for use incombination with the one-dimensional kiln code, due to itsrelatively short solution time. LS-Opt software (LSTC) is usedto coordinate the numerical optimization techniques and theautomation of the solution evaluation.

One simple example of the use of this technique is thedetermination of the optimal distribution of secondary airintroduced along the length of the kiln. The objective is toachieve a high level of metallization while respecting upperlimits on the solids bed temperature. The aim of the designexploration is to find a secondary air distribution thatachieves these objectives while keeping the off-gas volumesand temperatures low.

The optimization function formulated here is described asfollows:The objective:

Minimize {f(x)}, the combined blow flow rateConsidering the variables:

Blower rate on each of the 8 blowers, [x1;x8]Coal feed rate, [x9]

Subject to inequality constraints:Max. bed temperature < 1250ºC, {g1(x)}Max. outlet gas temperature < 850ºC, {g2(x)}Metallization > 95%, {g3(x)}Outlet char > 10%w, {g4(x)}

and side constraints:Blower rate on each of the 8 blowers: 1000 < x1;8 <7500 Nm3/h, {g5(x)}Coal feed rate: 5 < x9 < 13 t/h, {g6 (x)}

Results

Kiln model simulationThe base case operation of the kiln was established byrunning the model with the design values for all operatingparameters. The temperatures distributions in the bed,freeboard (gas), inner wall, and outer shell are shown inFigure 3 along the length of the kiln.

The feed material (bed) enters the kiln at ambienttemperature and rapidly heats up due to the hot gas in thefreeboard. At about 10 m into the kiln the volatiles arereleased from the coal, resulting in a local temperature spike

Modelling and optimization of a rotary kiln direct reduction process

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Modelling and optimization of a rotary kiln direct reduction process

due to combustion of the volatiles in the freeboard. At thedischarge end (80 m) the air injected into the kiln is rapidlyheated by the burner. Despite the high freeboard temper-atures, the shell temperature remains at about 200°C due tothe refractory lining on the inside of the kiln.

The progress of mass transfer along the kiln length ispresented in Figure 4. The metallization curve shows the startof metallization at around 20 m into the kiln, where the ore isreaching the required reduction temperatures. The exit valueis 94% of the available Fe in the model (the model considersonly 67% of the total Fe in the feed to take part in thereduction). In terms of the total Fe content in the feed, thisrepresents a conversion of 63% Fe.

The curve representing the percentage bed carbon tracksthe mass of the carbon in the bed in relation to the carbonadded to the kiln feed inlet. The consumption of carbon bythe Boudouard reaction follows the reduction reactionprogress. It is clear that in the present case the bed is notdepleted of carbon when the material reaches the kilndischarge.

Volatiles release occurs rapidly from about 8 m into thekiln and is substantially completed at around the 25 m mark.

The gas composition in the freeboard is shown with gaspartial pressure in Figure 5. The nitrogen partial pressure isplotted on the secondary y-axis and the rest of thecomponents use the primary y-axis. The N2 and CO2 levelsthrough the kiln are appreciably higher than othercomponents.

OptimizationConsidering the costs for a single experiment on a rotary kiln,minimizing the number of physical experiments is always anaim. Through formal design of experiments, this number iskept as low as possible and the most informative combinationof the factors is chosen. In this example, five sequentialdesign domains are considered, with sequential domainreduction. The space-filling point selection requires 83 designpoints per successive design space evaluation. The fullyautomated execution and evaluation of the 416 (83*5 + 1)design points is performed on a 12-core workstation within70 minutes. Quadratic surrogate models are created for eachset of results and provide a means to evaluate sensitivities

and identify good candidate designs. A surrogate model, ormetamodel (Gasevic et al., 2007), is a mathematical modelthat describes the behaviour of a system with respect to itsvariables. In the context of this paper, a quadratic metamodelis trained from the results of the individual analysesperformed. This can be considered in much the same way asa quadratic trendline is fitted through data-points in aspreadsheet. Intuitively, the greater the number of analysesperformed, the greater the confidence one has in the qualityof the fit of the metamodel.

An illustration of this successive domain reduction(Stander et al., 2002) is shown in Figure 6, providing theresults for the relationship between combined blower flowrate, outlet gas temperature, and maximum bed temperature.In the context of the confidence in the area of interest, thedomain reduction ensures that sets of subsequent analysesare concentrated closer to the good candidate designs.

Results can be combined and good candidate designsshown within the complete cloud of results. Figure 7 providespoint cloud plots for metallization with respect to combinedblower flow, outlet gas temperature, and maximum bedtemperature. The best candidate designs are highlighted toshow their position in the cloud of results.

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Figure 3 – Kiln temperature distribution for the base case

Figure 5 – Gas partial pressure (N2 on secondary axis) along the kiln

Figure 4 – Volatiles content, bed carbon and metallization along the kilnfor the base case

Modelling and optimization of a rotary kiln direct reduction process

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 423 ▲

Figure 7 – 2D results sets for metallization showing good candidate designs

Figure 6 – 3D results plot showing successive domain reduction around the area of the good design candidates

Modelling and optimization of a rotary kiln direct reduction process

Figure 8 shows key performance indicators of the bestcandidate designs relative to the base case design. Thepercentage improvement is shown in parentheses. Based onthe spread of results, a preferred design can be selected as atrade-off between the best candidates. The base case designwas based on a blower flow distribution applied from goodkiln rules-of-thumb practice. Without the use of numericaloptimization, this design is likely to have been the chosenoperating condition imposed on the kiln. From the results,case 5.27 provides improvement of all key indicators with asignificant drop in overall gas volume, exit gas temperature,and maximum bed temperature.

ConclusionsThe direct reduction one-dimensional kiln model can bemodified to accommodate any direct reduction process (notjust iron ore), but will require physical test work and baselinecalibration. This is usually part of the process for largeprojects, but was omitted here for brevity. The model allowsevaluation of a comprehensive set of factors that mayinfluence the kiln performance.

A certain class of problem lends itself to formal design ofexperiments and subsequent numerical optimization as anelegant means of design optimization. This paper illustrateshow the one-dimensional kiln model can be coupled to theappropriate numerical tools to yield a powerful tool for kilndesign and optimization. A significant number of designvariables and design objectives (not an intuitive combinationof input variables) can be evaluated in an automated fashion.

Key components of the success of the optimization are theaccuracy of the kiln model and sensible interpretation of themodel outputs. At Hatch the first component is addressed by

working in close collaboration with clients during the modelcalibration phases to ensure model outputs are consistentwith real experience. The second component is covered byensuring that people with actual operating experience on therelevant equipment are included in the design team for theduration of the project.

References

Gasevic, D., Kaviani, N., and Hatala, M. 2007. On metamodelling inmegamodels. MoDELS’07. Proceedings of the 10th InternationalConference on Model Driven Engineering Languages and Systems.Springer-Verlag, Berlin. pp. 91–105.

Haywood, R., Taylor, W., Plikas, N., and Warnica, D. 2009. Enhanced problemsolving; the integration of CFD and other engineering applications. 7thInternational Conference on CFD in the Minerals and Process Industries,CSIRO, Melbourne, Australia, 9-11 December 2009.

Livermore Software Technology Corporation (LSTC). LS-OPT user manual.http://ftp.lstc.com/anonymous/outgoing/trent001/manuals/lsopt/lsopt_50_manual.pdf

Saeman, W.C. 1951. Passage of solids through rotary kilns. ChemicalEngineering Progress, vol. 47. pp. 508-514.

Scott, D.M., Davidson, J.F., Lim, S.-Y., and Spurling, R.J. 2008. Flow ofgranular material through an inclined, rotating cylinder fitted with a dam.Powder Technology, vol. 182. pp. 466-473.

Stander, N. and Craig, K. 2002. On the robustness of a simple domainreduction scheme for simulation-based optimization. EngineeringComputations, vol. 19, no. 4. pp. 431-50.

Venkateswaran, V. 1976. Mathematical model of the SL/RN direct reductionprocess. MSc thesis, Department of Metallurgy, University of BritishColumbia. ◆

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Figure 8 – Results for best candidate designs

IntroductionMultiple-criteria decision-making (MCDM) isone of the most considered branches ofoperation research. MCDM refers to makingdecisions that involve multiple, usuallyconflicting, criteria. The problems in MCDM areclassified into two categories: multiple-attribute decision-making (MADM) andmultiple-objective decision-making (MODM).However, the terms MADM and MCDM areoften used to indicate the identical class ofmodels and are confused in practice. Usually,MADM is used when the model cannot bestated in mathematical equations, otherwiseMODM is used (Hwang and Yoon, 1980).

Decision-making is an important task inmining engineering projects, as in otherengineering branches. Every mining engineermakes several decisions in daily miningapplications. Most of the decisions made bymining engineers are completely instinctive,drawing from their past experience; therefore

no decision-making method is applied. Equipment selection is one of the most

prominent problems in mining engineering.Mining engineers have to make difficultdecisions during the equipment selectionstage. As the decisions have a strong influenceon the economic life of any mining scenario,they are considered as complex MADMproblems. Therefore, the decision-maker canevaluate the subjective criteria concerning theproblem of equipment selection. The decision-maker wishes to consider more than oneobjective criterion for the equipment selectionstage. Among the number of alternatives, themost suitable equipment must be selectedaccording to the objectives and alternatives.MADM applications can assist the decision-maker in achieving the optimal solution. In themining industry, MADM methods can beapplied for equipment selection because thesemethods include subjective and objectivecriteria that affect the selection amongalternatives.

A review of the literature reveals thatdecision-making techniques have been used indifferent types of mining applications(Acaroglu, Feridunoglu, and Tumac; Alpay andYavuz, 2007, 2009; Ataei, 2005; Ataei et al.,2008; Bitarafan and Ataei, 2004; Elevli andDemirci, 2004; Kazakidis, Mayer, and Scoble,2004;, Kesimal and Bascetin, 2002; Kluge andMalan, 2011; Namin et al., 2008; Yavuz,2008; Yavuz and Alpay, 2008; Yavuz, Iphar,and Once, 2008).

In this paper, two different MADMmethods – the analytic hierarchy process(AHP) and fuzzy multiple-attribute decision-making (FMADM) – are applied to select theloader for an open pit mine, and the solutionsare compared.

Equipment selection based on the AHPand Yager’s methodby M. Yavuz*

SynopsisOne of the challenging problems for optimization in mining operations is tochoose the best equipment among the alternatives. Equipment selection isan important task for mine management due to its operational cost, and isalso an integral part of mine planning and design. Equipment selection isnot a well-defined process because it involves the interaction of severalsubjective factors or criteria. Besides, decisions are often complicated andmay even embody contradictions. Therefore, equipment selection isconsidered as a multi-criteria decision-making process, and suitabledecision-making methods should be employed in this process. In thisstudy, the loader selection for Aegean Lignite Colliery was made by usingboth the analytic hierarchy process and Yager’s method. Owing to themisusage of Yager’s method in the past, a new procedure is proposed inthis paper for making proper decisions. It is highly recommended in thispurposed method that criteria or alternatives should be grouped so as notto exceed the limitations of human performance (ninecriteria/alternatives). The most appropriate solution for the loaderselection was investigated by obeying this limitation for each method, andthe results were compared. At the end of the decision-making process, asensitivity analysis was applied for each method in order to see how acriterion affects the final decision. The advantages and disadvantagesencountered during the application of each decision-making method arepresented.

KeywordsAHP, Yager’s method, decision making, loader selection.

* Eskisehir Osmangazi University, MiningEngineering Department, Turkey.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. Paper receivedJan. 2013 and revised paper received Apr. 2014.

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Equipment selection based on the AHP and Yager’s method

Method Equipment selection is important and difficult task to performbecause of the need for handling many criteria simulta-neously in the decision-making process. The number ofcriteria is important for arriving at the right decisions. Amaximum of nine criteria can be handled in decision-makingproblems because of the general limitations of humanperformance (Saaty and Ozdemir 2003). These limits arewidely reported in the literature as the ‘memory span’,‘attention span’, ‘central computing space’, and ‘channelcapacity’ (Ozdemir, 2005). If the pairwise comparisonmatrixes are formed without considering these limits, theninconsistencies will likely occur. Even when the matrix isconsistent, the matrix will likely not be valid. Therefore, thecorrect usage of the selected decision-making method isimportant to ensure the proper decisions. Because of thelimitations mentioned in the AHP method, the total numberof criteria for the Yager’s method should also be less thannine (Yavuz and Alpay, 2007).

AHP methodThe AHP method developed by Saaty represents theinteraction of multiple factors in complex unstructuredsituations (Triantaphyllou, 2000). This method is based onthe pairwise comparison of components with respect toattributes and alternatives. A pairwise comparison matrix,n×n, is constructed, where n is the number of elements to becompared. This method is applied for hierarchical problemstructuring. The problem is divided into three levels: theproblem statement, the object identification to solve theproblem, and the selection of the evaluation criteria for eachobject. After structuring the hierarchy, the pairwisecomparison matrix is constructed for each level in which anominal discrete scale from 1 to 9 is used for the evaluation(Table I) (Saaty, 1980, 2000).

The next step is to determine the relative priorities of thecriteria or the alternatives implied by this comparison. The

relative priorities are determined using eigenvectors. Forexample, if the pair comparison matrix is A, then thefollowing can be written:

[1]

To calculate the eigenvalue λmax and eigenvector w=(w1,w2,..., wn), the weights can be estimated as relative prioritiesof the criteria or alternatives.

Because the comparison is based on a subjectiveevaluation, a consistency ratio is required to ensure theaccuracy of the selection. The consistency index (CI) of thecomparison matrix is computed as follows:

[2]

where λmax is the maximal or principal eigenvalue, and n isthe matrix size. The consistency ratio (CR) is calculated as:

[3]

where RI is the random consistency index. Randomconsistency indices are given in Table II (Saaty, 2000).

In general, a consistency ratio of 0.10 or less isconsidered acceptable. In practice, however, consistencyratios exceeding 0.10 occur frequently.

FMADM methodFMADM methods have been developed because of the lack ofprecision in assessing the relative importance of attributesand the performance ratings of alternatives with respect to anattribute. This imprecision may arise from a variety ofsources, such as unquantifiable information, incompleteinformation, unobtainable information, and partial ignorance(Chen and Klein, 1997).

The main problem of a fuzzy MADM is toselect/prioritize/rank a finite number of courses of action (or

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Table I

Scale for pairwise comparisons

Relative Definition Explanationintensity

1 Of equal value Two requirements are of equal value3 Slightly more value Experience slightly favours one requirement over another5 Essential or strong value Experience strongly favours one requirement over another7 Very strong value A requirement is strongly favoured and its dominance is demonstrated in

practice9 Extreme value The evidence favouring one over another is of the highest possible order of

affirmation2,4,6,8 Intermediate values between two adjacent judgments When compromise is needed

Table II

The consistency indices of randomly generated reciprocal matrices

Order of the matrix

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15RI value 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48 1.56 1.57 1.59

alternatives) by evaluating a group of predetermined criteria.Thus, to solve this problem, an evaluation procedure ratingand ranking (in order of preference) of the set of alternativesmust be constructed. The fuzzy MADM problem is describedbelow:

1. A set of m possible courses of action (referred to asalternatives): A = {A1, A2, . . . . . Ai, . . . . ., Am}

2. A set of n attributes (or criteria): C = {C1, C2, . . . . ., Cj, . . . . ., Cn} with whichalternative performances are measured

3. A performance rating of alternative Ai with respect toattribute (or criterion) Cj, which is given by the n×mfuzzy decision matrix: R= {Rij | i = 1, 2, . . . . . m; j = 1, 2, . . . ., n}, where Rij is a fuzzy set (or fuzzy number)

4. A set of n fuzzy weights: W = {Wj |Wj =1, 2, . . . . . n} where Wj is also a fuzzy set (or fuzzy number) anddenotes the importance of criterion j, Cj, in theevaluation of the alternatives (Chen and Klein, 1997).

Although a large number of FMADM methods have beenaddressed in the literature, the focus of this paper is mainlyon Yager’s method (Yager, 1978). This method is sufficientlygeneral to address both multiple objectives and multipleattribute problems. Yager’s method follows the max-minmethod of Bellman and Zadeh (1970) with the improvementof the Saaty’s method, which considers the use of a reciprocalmatrix to express the pairwise comparison criteria and theresulting eigenvector as subjective weights. The weightingprocedure employs exponentials based on the definition oflinguistic hedges as proposed by Zadeh (1973).

When describing MADM problems, only a single objectiveis considered, namely the selection of the best alternativefrom a set of alternatives. Yager’s method assumes the max-min principle approach. The fuzzy set decision is theintersection of all criteria: μD (A) = Min {μC1(Ai), μC2(Ai), . .., μCn (Ai)}. For all (Ai) ∈ A, the optimal decision is yieldedby μD (A*) = Max (μD,(Ai)), where A* is the optimal decision.

The main difference in this approach compared with otherapproaches is that the importance of the criteria isrepresented as exponential scalars. This representation isbased on the idea of linguistic hedges. The rationale behindusing weights (or importance levels) as exponents is that asthe importance of a criterion increases, the exponent shouldincrease, providing the minimum rule. Conversely, the lessimportant a criterion, the smaller the weight. This processseems intuitive. Formally, this method can be written for α>0(Bascetin and Kesimal, 1999):

[4]

Modified Yager’s method Because of the limitations mentioned in the ‘Methods’section, the total number of criteria for Yager’s methodshould be less than nine (Yavuz and Alpay, 2007).Therefore, a new procedure for Yager’s method is proposed inthis paper for making proper decisions by taking into consid-eration the fact that the criteria or alternatives should be

grouped so as not to exceed the limitation of humanperformance (nine criteria/alternatives). In this method, thefollowing steps should be applied to solve the problem inquestion with Yager’s method:

➤ Group the criteria into clusters with less than ninecriteria

➤ Perform Saaty’s method for the main groups andcalculate the weights for each group

➤ Perform Saaty’s method for all criteria in each groupand calculate the weight of each criterion in the group

➤ Calculate the final weights of the criteria by multiplyingthe criteria weights with their own group weights.

An application for loader selection

Description of the study siteIn Turkey, coal production units controlled by the EtibankCompany were transferred into Turkish Coal Enterprises(TKI) in 1957. In accordance with the Government’s generalenergy policy, TKI was assigned to produce lignite and othertypes of coal to satisfy the country’s coal demand, contributeto the economy, prepare and execute plans and programmes,determine application strategies, and to realize the strategies.The lignite reserves in Turkey total 13 billion tons, 4.5 billiontons of which are controlled by TKI. In total, 46 per cent oflignite production in Turkey is by TKI. TKI’s productiondepends on the requirements of the power generationcompanies and the demands from heating and industry. TKIproduced 26.2 Mt of marketable coal in 2007 and 3.6 Mtthrough license. In 2007, TKI produced a pickling of 268million m3. In total, 79 per cent of the coal sold in 2007 (31.6Mt) was supplied to the thermal plants operated by theelectricity generation company.

Mining in the Aegean Lignite region has been ongoingsince 1913. This region depended on the Western LigniteColliery (GLI) from 1939 until 1978, and then operated underGLI until 1995. After 1995, the mines were operated by theAegean Lignite Colliery (ELI). TKI received a regionaldirectorate, operation directorate and was re-established as alegal entity in April 2004. After 2004, TKI establishedactivities at the centre of the largest entity, Soma, and 90miles away in Manisa (Figure 1).

In the township of Manisa, Soma has 610 Mt of lignitereserves with lower heating values between 2080–3150kcal/kg. TKI holds these reserves, which are spread over 24.4thousand hectares, and 71 per cent of these reserves arelicensed to be exploited with underground operations by TKI.

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427The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

˘ ˘˘

˘ ˘˘

Figure 1 – The location of Soma and ELI

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Equipment selection based on the AHP and Yager’s method

Approximately half of the total sales of heating and industrialcoal sold by TKI originate from this region. In addition, coalpreparation and coal extraction plants are located in Soma.Opencast mining uses large-capacity excavators and heavytrucks. Underground mining is performed for the tender andthe period of royalty; the annual production is 5.2 Mt. Thetotal production of this establishment was 9.6 Mt in 2011.

Defining the equipment selection processThe AHP technique was used for wheel loader selectionaccording to the criteria established by ELI. Selection criteriaaffecting the decision-making process should be consideredin the AHP technique. The criteria used in this work to selectthe best alternative for a loader are the required technicalfeatures:

➤ Operating weight between 80 and 90 t➤ Diesel engine with a net power exceeding 650

horsepower and suited for heavy working conditions➤ Rated bucket capacity exceeding 12 cubic yards➤ 45º discharge height exceeding 4 m➤ Breakout force exceeding 60 000 kg➤ Lifting capacity exceeding 17 500 kg➤ Static tipping load exceeding 45 000 kg➤ Articulating angle exceeding 30º➤ Tyre protection chain should be available➤ Rops-type operator cabin suited for all types of climate

conditions➤ L-5 class tubeless tyres resistant to wearing➤ The machine should be equipped with a torque

converter, full power shift, and four-wheel drive.In addition to these technical features, the wheel loader

should be able to operate in local conditions such as 1000 mof altitude, ambient temperatures of -25 to +40°C, and soildensities of 1.1 to 1.8 t/m3.

The criteria and sub-criteria were assessed by an expertteam consisting of one mechanical engineer (the manager ofthe firm, with 10 years of experience in the mining industry)and two mining engineers with 20 years of experience in the

mining industry. All decisions have a common hierarchicalstructure whereby options are evaluated against the variouscriteria to promote the ultimate decision objective. Theproblem of the loader selection was structured in a hierarchyof different levels constituting goals, criteria, sub-criteria, andalternatives (Figure 2).

The AHP solutionAfter structuring a hierarchy, the pairwise comparison matrixfor each level is constructed. During the pairwise consid-eration, a nominal scale is used for the evaluation (Table I).As shown in Table III, each main criterion affecting the loaderselection was compared, and the pairwise comparison matrixwas constructed. The expert team performed thesecomparisons. The Economic Main criterion is the mostimportant factor (priority 0.4714).

After constructing the pairwise comparison matrix for themain criteria, all subgroups of each main criterion should becompared (Tables IV, V, VI, and VII).

The pairwise comparison matrices are constructed bycomparing each loader alternative with each sub-criterion inall of the main criteria. The comparison matrix for the CapitalCost sub-criterion in the Economic Main criterion is given inTable VIII as an example, and the general evaluation of the

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428 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 2 – Hierarchy structure for the loader selection

Table III

Pairwise comparison matrix for the maincriteria

C1 C2 C3 C4 Geo mean Weights

C1 1 3 2 4 2.2134 0.4714C2 1/3 1 1/2 3 0.8409 0.1791C3 1/2 2 1 2 1.1892 0.2533C4 1/4 1/3 1/2 1 0.4518 0.0962

Total 4.6953 1.0000λmax=4.1233, CI=0.0411 and CR=0.0457≤0.1, OK.

Economic Main criterion is given in Table IX. An identicalprocedure was applied for the other main criteria andassociated sub-criteria.

The total priorities of the Economic Main criterion arecalculated by summing the product of the relative priority ofeach sub-criterion and the eigenvalues considering thecorresponding Economic Main criterion. The total prioritiesare calculated as the following: (0.2329×0.3882) +(0.4747×0.2580) + (0.4668×0.1612) + (0.2717×0.1612) +(0.0861×0.0314) = 0.3346 (Table IX).

The overall rating for each alternative is calculated bysumming the product of the relative priority of each criterionand the alternatives considering the corresponding maincriteria. For example, the overall rating of ‘Alternative A’ iscalculated as the following: (0.3346×0.4714) +(0.1007×0.1791) + (0.3723×0.2533) + (0.3402×0.0962) =0.3028. Similarly, the constructed final matrix is shown inTable X.

Because the comparison is based on a subjectiveevaluation, a consistency ratio (CR) should be calculatedfrom Equation [3] to ensure the accuracy of the selection.From TableS III to VIII, the maximum eigenvalues (λmax) arenearly the size of the matrix (in Table III, the size of thematrix is 4×4, and the mean value of λmax is 4.1233), and

Equipment selection based on the AHP and Yager’s method

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 429 ▲

Table VI

Pairwise comparison matrix for the Technical criterion with the associated sub-criteria

C31 C32 C33 C34 C35 C36 C37 C38 Weights

C31 1 1/3 1 3 2 2 1 3 0.1401C32 3 1 3 5 4 4 3 5 0.3277C33 1 1/3 1 3 2 2 1 3 0.1401C34 1/3 1/5 1/3 1 1/2 1/2 1/3 1 0.0468C35 1/2 1/4 1/2 2 1 1 1/2 2 0.0792C36 1/2 1/4 1/2 2 1 1 1/2 2 0.0792C37 1 1/3 1 3 2 2 1 3 0.1401C38 1/3 1/5 1/3 1 1/2 1/2 1/3 1 0.0468λmax=8.0776, CI=0.0111 and CR=0.0079≤0.1, OK.

Table IV

Pairwise comparison matrix for the EconomyMain criterion with the associated sub-criteria

C11 C12 C13 C14 C15 Weights

C11 1 2 3 3 6 0.3882C12 1/2 1 2 2 7 0.2580C13 1/3 1/2 1 1 8 0.1612C14 1/3 1/2 1 1 8 0.1612C15 1/6 1/7 1/8 1/8 1 0.0314λmax=5.2500, CI=0.0625 and CR=0.0558≤0.1, OK.

Table V

Pairwise comparison matrix for the Operatingcriterion with the associated sub-criteria

C21 C22 C23 C24 Weights

C21 1 2 1/3 1/4 0.1158C22 1/2 1 1/6 1/8 0.0579C23 3 6 1 1/2 0.3138C24 4 8 2 1 0.5125λmax=4.0206, CI=0.0069 and CR=0.0076≤0.1, OK.

Table VII

Pairwise comparison matrix for the Warrantycriterion with the associated sub-criteria

C41 C42 C43 C44 Weights

C41 1 1/2 1/2 1/3 0.1169

C42 3 1 1 1/3 0.2175

C43 2 1 1 1/2 0.2175

C44 3 3 2 1 0.4481λmax=4.1880, CI=0.0627 and CR=0.0696≤0.1, OK.

Table VIII

Pairwise comparison matrix for the CapitalCost sub-criterion of the Economic Maincriterion

A B C D Weights

A 1 1/3 2 3 0.2329B 3 1 4 5 0.5450C 1/2 1/4 1 2 0.1385D 1/3 1/5 1/2 1 0.0836λmax=4.0511, CI=0.0170 and CR=0.0189≤0.1, OK.

Table IX

Total priorities of the Economic Main criterion

C11 C12 C13 C14 C15 Total

A 0.2329 0.4747 0.4668 0.2717 0.0861 0.3346B 0.5450 0.2551 0.1603 0.1569 0.1424 0.3330C 0.1385 0.1072 0.0953 0.4832 0.2651 0.1830D 0.0836 0.1630 0.2776 0.0882 0.5064 0.1494Main 0.3882 0.2580 0.1612 0.1612 0.0314

Equipment selection based on the AHP and Yager’s method

the CR values are less than 0.1 (in Table III, the mean CRvalue is 0.0457). These values are in the desired range.Therefore, this decision is selected without repeating theprocedure.

Considering the overall results in Table X, Alternative Amust be selected as the optimum loader selection to satisfythe goals and objectives of the TKI management because thepriority of this alternative (0.3028) is the highest among alloptions.

For the loader selection, the proposed AHP model isunique in its identification of multiple attributes, minimaldata requirement, and minimal time consumption. However,the most substantial general disadvantage of the AHPmethod is developing a large number of pairwise comparisonmatrixes. Pairwise comparisons are often a stressful processfor decision-makers.

The solution by Yager’s method The calculation procedure by Yager’s method is identical tothe AHP method both for the main criterion and sub-criteriain the problem in question.

The main criterion and sub-criteria weights are calculatedidentically as in the AHP method. The combined weights forthe 21 sub-criteria are then calculated by multiplying eachmain criterion weight and each sub-criterion weightseparately. The combined weights for each sub-criterion aregiven in Table XI.

The sum of the weights of this sub-criterion weight is 1.The calculated weight values for each sub-criterion are takenas membership functions of each sub-criteria. Theexponential weights for each sub-criterion are given asfollows:

α11=0.1831, α12=0.1216, α13=0.0760, α14=0.0760, α15=0.0148α21=0.0207, α22=0.0104, α23=0.0562, α24=0.0918α31=0.0355, α32=0.0830α33=0.0355, α34=0.0118, α35=0.0201, α36=0.0201, α37=0.0355, α38=0.0118α41=0.0112, α42=0.0209, α43=0.0209, α44=0.0431 The general weight values for each sub-criterion obtained

from expert opinions are given as the ‘decision matrix’ inTable XII. The weights of each alternative for the sub-criteriaare selected using fuzzy numbers ranging from 0 to 1.

By using membership levels of alternatives and theweight of criteria for each criterion, the following conclusionswere reached using Equation [4]:

μD(Alternative A)=min{0.40.1831, 0.20.1216, 0.20.0760,0.40.0760, 0.80.0148, 0.40.0207, 0.40.0104, 0.20.0562, 0.20.0918,0.40.0355, 0.20.0830, 0.40.0355, 0.40.0118, 0.60.0201, 0.20.0201,0.20.0355, 0.20.0118, 0.20.0112, 0.40.0209, 0.20.0209,0.40.0431}=0.822.

μD(Alternative B)=min{0.20.1831, 0.40.1216, 0.60.0760,0.60.0760, 0.60.0148, 0.20.0207, 0.60.0104, 0.60.0562, 0.40.0918,

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430 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table X

Overall result/final matrix

C1 C2 C3 C4 Main Total

A 0.3346 0.1007 0.3723 0.3402 0.4714 0.3028B 0.3330 0.2009 0.2549 0.3037 0.1791 0.2867C 0.1830 0.4478 0.2010 0.1767 0.2533 0.2344D 0.1494 0.2506 0.1718 0.1794 0.0962 0.1761

Table XI

Calculation of each sub-criterion weight

Main criteria Sub-criteria WeightsSub Main Combined

Economy (C1) Capital cost (C11) 0.3882 0.1831Fuel cost/consumption (C12) 0.2580 0.1216Spare parts cost (C13) 0.1612 0.4714 0.0760Auxiliary equipment cost (C14) 0.1612 0.0760Resale cost (C15) 0.0314 0.0148

Operating (C2) Material size (C21) 0.1158 0.0207Moisture (C22) 0.0579 0.0104Ground conditions (C23) 0.3138 0.1791 0.0562Weather conditions (C24) 0.5125 0.0918

Technical (C3) Machine weights (C31) 0.1401 0.0355Flywheel power (C32) 0.3277 0.0830Bucket capacity (C33) 0.1401 0.0355Discharge height (C34) 0.0468 0.0118Breakout capacity (C35) 0.0792 0.2533 0.0201Lifting capacity (C36) 0.0792 0.0201Static tipping load (C37) 0.1401 0.0355Articulating angle (C38) 0.0468 0.0118

Warranty (C4) Pleasure from the dealer (C41) 0.1169 0.0112Service conditions (C42) 0.2175 0.0209Spare parts providing (C43) 0.2175 0.0962 0.0209Warranty (C44) 0.4481 0.0431

TOTAL 4.0000 1.0000 1.0000

0.80.0355, 0.60.0830, 0.20.0355, 0.20.0118, 0.20.0201, 0.40.0201,0.40.0355, 0.60.0118, 0.40.0112, 0.60.0209, 0.80.0209,0.20.0431}=0.745.

μD(Alternative C)=min{0.60.1831, 0.80.1216, 0.80.0760,0.20.0760, 0.40.0148, 0.80.0207, 0.20.0104, 0.80.0562, 0.80.0918,0.60.0355, 0.40.0830, 0.60.0355, 0.60.0118, 0.40.0201, 0.80.0201,0.80.0355, 0.10.0118, 0.60.0112, 0.80.0209, 0.40.0209,0.60.0431}=0.656.

μD(Alternative D)=min{0.80.1831, 0.60.1216, 0.40.0760,0.80.0760, 0.20.0148, 0.60.0207, 0.80.0104, 0.40.0562, 0.60.0918,0.150.0355, 0.80.0830, 0.60.0355, 0.40.0118, 0.80.0201, 0.60.0201,0.60.0355, 0.40.0118, 0.80.0112, 0.20.0209, 0.60.0209,0.80.0431}=0.707.

Using the Bellman-Zadeh max-min rule, the structure ofa decision is reached as follows:

μD(A)={Alternative A/0.822, Alternative B/0.745,Alternative C/0.656, Alternative D/0.707}

The optimal solution is found as follows:μD(A*)=max{μD(Alternative A1)}=0.822. Therefore,‘Alternative A’ is found to be the most appropriate wheelloader because it displays the highest membership functionvalue.

Yager’s method consists of the identical order ofoperations as the AHP method, up to the calculation of allcriterion weights. The most substantial difference betweenYager’s method and the AHP is achieving the final solutionusing only one decision matrix. Therefore, Yager’s methodinvolves fewer processing steps and fewer pairwisecomparisons than the AHP method. Although not used inthis study, decision-makers can include linguisticexpressions in the decision-making process in the applicationof Yager’s method (Yavuz, 2008). This feature facilitatesdecision-making in a fuzzy environment.

Sensitivity analysis for each methodA sensitivity analysis must be performed to determine howthe alternatives will change with the importance of thecriteria. As the priority of one of the criteria increases, thepriorities of the remaining criteria must decrease propor-tionately, and the global priorities of the alternatives must berecalculated. A sensitivity analysis can also be used todetermine the most important or critical criterion bycomputing the absolute or percentage amount by which theweight of any criterion must be changed to cause a switch inthe ranking of the top alternative or in any pair ofalternatives (Triantaphyllou and Sánchez, 1997).

The values of the eigenvector for the main criterion in thepairwise comparison matrices simulated were increased ordecreased for several scenarios in the AHP and FMADMmethods. No change was noted in the judgment evaluationsin the final priority ranking when the eigenvector value ofeach criterion increased/decreased up to 44 per cent in theAHP method (Figure 3). Therefore, ‘Alternative A’ canalways be selected as the most convenient alternative for thedecision-making process, and ‘Alternative B’, ‘Alternative C’,and ‘Alternative D’ sequentially followed in the finalpriorities. From the sensitivity analysis, the final result of theproposed AHP model is mainly sensitive to decreases in theTechnical Main criterion.

However, the final decision found by the proposedFMADM model was compared with the simulated scenarios inFigure 4. When the priority of the all criteria increased ordecreased by 50 per cent, the present decision does notchange. Therefore, ‘Alternative A’ can always be selected asthe most convenient alternative for the decision-makingprocess, and ‘Alternative B’, ‘Alternative C’, and ‘AlternativeD’ sequentially followed in the final priorities. From the

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The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 431 ▲

Table XII

Decision matrix for Yager’s method

Expert opinions General weight valuesA B C D A B C D

C11 0.4 0.2 0.6 0.8 0.846 0.745 0.911 0.960C12 0.2 0.4 0.8 0.6 0.822 0.846 0.960 0.911C13 0.2 0.6 0.8 0.4 0.885 0.911 0.960 0.846C14 0.4 0.6 0.2 0.8 0.933 0.911 0.745 0.960C15 0.8 0.6 0.4 0.2 0.997 0.911 0.846 0.745C21 0.4 0.2 0.8 0.6 0.981 0.745 0.960 0.911C22 0.4 0.6 0.2 0.8 0.991 0.911 0.745 0.960C23 0.2 0.6 0.8 0.4 0.914 0.911 0.960 0.846C24 0.2 0.4 0.8 0.6 0.863 0.846 0.960 0.911C31 0.4 0.8 0.6 0.15 0.944 0.960 0.911 0.707C32 0.2 0.6 0.4 0.8 0.879 0.911 0.846 0.960C33 0.4 0.2 0.6 0.6 0.976 0.745 0.911 0.911C34 0.4 0.2 0.6 0.4 0.989 0.745 0.911 0.846C35 0.6 0.2 0.4 0.8 0.992 0.745 0.846 0.960C36 0.2 0.4 0.8 0.6 0.974 0.846 0.960 0.911C37 0.2 0.4 0.8 0.6 0.958 0.846 0.960 0.911C38 0.2 0.6 0.1 0.4 0.981 0.911 0.656 0.846C41 0.2 0.4 0.6 0.8 0.982 0.846 0.911 0.960C42 0.4 0.6 0.8 0.2 0.981 0.911 0.960 0.745C43 0.2 0.8 0.4 0.6 0.967 0.960 0.846 0.911C44 0.4 0.2 0.6 0.8 0.961 0.745 0.911 0.960

The Minimum Values 0.822 0.745 0.656 0.707

Equipment selection based on the AHP and Yager’s method

sensitivity analysis, the final result of the proposed FMADMmodel is mainly sensitive to increases in the Economic Maincriterion.

In the sensitivity analysis, by increasing or decreasing thevalues of the eigenvector of the main criterion, the variabilitylimit is calculated as 61 per cent. After this limit, theWarranty Main criterion takes a negative value, increasingthe Economic Main criterion. As previously mentioned, theAHP model is mainly sensitive to decreases in the TechnicalMain criterion, and the FMADM model is mainly sensitive toincreases in the Economic Main criterion. Figure 5 and Figure6 show respectively multiple runs of the sensitivity analysisfor every 5 per cent change of the AHP and FMADM modelsaccording to its most sensitive criterion. These figures alsoindicate the accuracy of the applied decision-making processin both methods.

ConclusionsMining engineers frequently have to select an optimumoption among alternatives related to the mining operation.Each mining engineer might make precise decisions in allmining operations, and the decision-maker must use asuitable technique to make proper decisions. A number oftechniques are available for solving different types of decisionproblems. In this study, the AHP and Yager’s methods, whichare two similar MADM techniques, are used to solve anequipment selection problem.

Equipment selection involves the interaction of severalsubjective and objective factors or criteria. Decisions are oftencomplicated and many even embody contradictions. The AHPand Yager’s method models, which contain four main criteriaand 21 sub-criteria, were developed. Among the fouralternatives under consideration, ‘Alternative A’ is the mostacceptable option identified for both methods whenconsidering all main and sub-criteria in the analysis.

Unlike the traditional approach for loader selection,decision-making methods are more scientific, providingintegrity and objectivity in the estimation process. Both theAHP and Yager’s method models are transparent, easy tocomprehend, and applicable by the decision-maker.

The AHP and Yager’s method-based applications can beutilized in different sectors of the mining industry. Thenumber of criteria and alternatives in the AHP and Yager’smethod applications should be determined by the decision-maker because of the consistency and validity of thedecision-making process (Saaty and Ozdemir, 2003;Ozdemir, 2005). The number of alternatives should be lessthan nine, otherwise a grouping method should be appliedfollowing the instructions presented in this study.

Finally, the result of this study shows that AHP andYager’s method applications can assist engineers ineffectively evaluating the alternatives available in miningengineering.

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432 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 3 – Sensitivity analysis results for the AHP method

Figure 4 – Sensitivity analysis results for Yager’s method

Figure 5 – Multiple runs of the sensitivity analysis for the AHP modelwhen decreasing the Technical Main criterion

Figure 6 – Multiple runs of the sensitivity analysis for the FMADM whenincreasing the Economic Main criterion

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BACKGROUNDGeostatistics constitutes a globally accepted technicalapproach to mineral resource-reserve estimation and the basictoolkit for mine evaluation practitioners. Following the call forpapers and the publication of the Danie Krige CommemorativeVolumes, the SAIMM invites submission of papers for theDanie Krige Geostatistical Conference to be held inJohannesburg, South Africa, 19–20 August 2015.

Second Announcement

THE DANIE KRIGE GEOSTATISTICAL CONFERENCE

GEOSTATISTICAL GEOVALUE REWARDS AND RETURNS FOR SPATIAL MODELLING

Crown Plaza, Johannesburg · 19–20 August 2015

EXHIBITION/SPONSORSHIPSponsorship opportunities are available. Companieswishing to sponsor or exhibit should contact theConference co-ordinator.For further information contact:

Conference Co-ordinator, Yolanda RamokgadiSAIMM, P O Box 61127, Marshalltown 2107

Tel: +27 (0) 11 834-1273/7E-mail: yolanda@saimm.co.za

Website: http://www.saimm.co.za

THEMEThe theme of the conference is ‘GeostatisticalGeovalue—Rewards and Returns for SpatialModelling’, a theme which emphasizes theimprovement in, or addition to, value that spatialmodelling can bring to the process of mineevaluation and mineral resource and reserveestimation. Spatial modelling of earth relateddata to estimate or enhance attributed value isthe principle domain of geostatistics, the broadcontent of the Danie Krige CommemorativeVolumes, and the focus of this conference.

OBJECTIVESThe conference provides authors who have recentlypublished papers in the SAIMM’s Danie KrigeCommemorative Volumes, a platform to present theirresearch. In addition an invitation to geostatisticians,resource estimation practitioners, and those with an interest ingeostatistics to present new papers for inclusion in the proceedingsis now open. The conference will explore advances in technologyand methodologies, and case studies demonstrating the applicationof geostatistics. It will cross the commodity boundaries, withapplications presented from precious to base metals, and diamonds.This is a valuable opportunity to be involved in constructive dialogueand debate, and to keep abreast with the best practice in thisspecialist field.

WHO SHOULD ATTENDThe conference provides a platform for:

• local and international geostatisticians• geologists• engineers• researchers• software vendors• mineral resource managers and practitioners, across the mining

industry• consultancy and academia, to present their work and contribute

to the advancement of this field.

CONFERENCE SUPPORTER

CONFERENCE SPONSORS

IntroductionDuring the preliminary design phases andfeasibility study of a proposed vertical shaft,very little detailed information is available onthe rock mass characteristics, in-situ stress,rock strengths, and hydrological characteristicsfor the evaluation of the project. A pre-sinkdiamond drill-hole is normally drilled on theproposed site and is steered with the use ofgyro surveys and wedges to remain within thebarrel of the shaft. i.e. not to deviate morethan 5 m in any direction from the centralposition.

Geotechnical logging of the core can nowtake place and parameters such as rockstrengths, fracture orientations and dip, in-situmagnitude and orientation of the principalhorizontal stress field, and positions of waterinflow can be determined with the use ofwireline geophysics.

Rock mass classification schemes havebeen developing for over 130 years since Ritter(1879) attempted to formalize an empiricalapproach to tunnel design and supportrequirements. Numerous multi-parameterclassification systems (Wickham et al., 1972;Bieniawski, 1973, 1976, 1989; Laubsher,1977; Barton et al., 1974, 1976, 2002;McCracken and Stacey, 1989) have beendeveloped since then. Although most of thesystems were developed for the design and

support of horizontal tunnels and excavations,McCracken and Stacey (1989) adaptedBarton’s Q-factor to the stability of raise-boring. Peck and Lee (2008) have alsoadapted the Bieniawski (1989) and McCrackenand Stacey systems to assess the stability ofraise-bored shafts.

The choice of the rock mass classificationsystem used is very often client-dependent.However the ‘stick plot’ technique used hereemploys modified versions of Barton, as wellas McCracken and Stacey, which have beenenhanced with the use of wireline techniquesand adjusted to various raise-bore diameters.

The purpose of the stick plot is to combineall geotechnical parameters applicable to thestability of a proposed shaft into an easilyreadable colour-coded format where cross-correlations can readily be made on a day-to-day basis during the shaft-sinking process. Adetailed spreadsheet of all parameters is alsoprovided for the use of the rock engineers.

The purpose of this paper is to introducethe stick plot method to the mining and shaft-sinking community as a viable alternative tothe usually more esoteric methods of reporting,thus making the information readily accessibleto all involved.

The deviated stick plotThe basis of the stick plot technique (Figure 1)is the use of Barton’s geotechnical parametersthat have been adapted to a vertical tunnelusing data derived from geophysical wirelinelogging. Each individual structure (jointing,fractures, bedding, breakout etc.) is identifiedin the core, and geologically and geotechnicallydescribed with exact depth, inclination, andazimuth. This geophysical data is used to

Pre-sink shaft safety analysis usingwireline geophysicsby N. Andersen*

SynopsisDuring the preliminary design phases and feasibility study of a proposedvertical shaft, a vertical diamond drill-hole is normally drilled on the site.This paper outlines how rock mass characteristics, in-situ rock stress, rockstrength, hydrological characteristics, and structural parameters can bedetermined using wireline logging of this borehole. In addition, a rockmass classification scheme is developed, based on published work, and inparticular the Q-factor is adapted to assess the stability of raise-boredshafts. The ‘stick plot’ is introduced, which combines all geotechnicalparameters applicable to the stability of a vertical shaft into a colour-coded format where cross-correlations can readily be made on a day-to-day basis during the shaft-sinking process.

Keywordsshaft-sinking, raise-boring, geophysical logging, geotechnical, Q-factor,stability.

* GeoScientific and Exploration Services, Irene.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. Paper receivedMay 2013 and revised paper received Jan. 2015

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Pre-sink shaft safety analysis using wireline geophysics

modify Barton’s, parameters such as joint set number ‘Jn’,joint water reduction factor ‘Jw’, and stress reduction factor‘SRF’ for conditions in a vertical tunnel.

Both Bieniawski’s and McCracken and Stacey’sparameters are also adjusted using the more precisefracture/joint spacings and orientations derived from thegeophysical data. The stick plot poster itself shows only themodified Barton (2002) data with an additional ‘sidewallstability index’. Barton’s (1974, 1976) Q-factor values forsupport/no support are plotted on the modified Barton barchart to determine what length of the shaft would requiresupport (Figures 2 and 3). A detailed spreadsheet is alsosupplied for the use of the rock engineers, showing allcalculated parameters.

The parameters shown on the poster are for the day–to-day use of the shaft-sinkers, and include the following:

➤ Colour-coded modified Barton Q-factor normalized toshaft diameter

➤ Colour-coded sidewall stability index developed byAndersen Geological Consulting

➤ Stick plots showing dips of all structures subdivided intosets with the borehole deviation. There can be two or threesets, depending on the dominant structural sets

➤ Stereograms showing the orientation of the structural setsover 100 m intervals down the shaft. These diagramsshow the orientation of sidewall bolting to achieveoptimum anchoring

➤ Calculated UCS plotted in a barchart against the lithologicallog of the borehole

➤ Barton’s (1974, 1976) tunnel support charts showing thesupport/no support required using different Q-factor values(Figures 2 and 3).

Details of modified parameters➤ RQD. Having accurately determined the positions of all the

structures from the acoustic televiewer (ATV), geotechnicalunits are determined (where all structural spacings aresimilar) and the RQD is determined over these units, ratherthan at fixed intervals. The reason for this is that the RQDzone forms the basis of all of the derived parameters

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436 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 1 – The shaft ‘stick plot’ showing the normalized Q-factor, the sidewall stability index, fracture orientations and calculated UCS superimposed onthe geology

Figure 2 – Barton’s (1974) tunnel support chart showing the the support/no support required for the shaft being evaluated

➤ Jn (joint set number). Having measured the structuralorientation, stereographic projections are plotted and Jncan then be accurately determined. The zone overwhich this calculation is done would depend on the dipof the structures and the diameter of the shaft

➤ Jr and Ja. Each structure is identified on the ATV imageso that its exact orientation and depth are known. Jrand Ja parameters are then directly linked to thisposition. Structural features such as brecciation andorientation of slickensides are also determined. Thisdata then allows for a ’structural domain analysis’ tobe carried out on the data

➤ Jw (joint water reduction factor). This factor is linked tothe amount of water flowing from the fractures andalso to the pressure. Before the water stratification inthe borehole is disturbed by inducing water flow, afluid conductivity log is run in the borehole. The size ofthe conductivity plume can then be related to therelative water ingress. This data is then interpretedalong with physical observations of the fractures in thecore, the televiewer classifications, the resistivityresponse given by the fractures, as well as the responseof the full wave-form sonic tool. Following this, awireline flow-meter can be used to determine therelative water ingress from fractures if a constant waterflow is induced in the borehole from surface. This isoften not possible as the fractures fill up and there isno flow of water

➤ SRF (stress reduction factor). Weak zones intersectingthe excavation which may cause loosening of the rockmass when the shaft is sunk. This is addressed by thesidewall stability index (SSI). Shear zones are clearlyidentified by the full-waveform sonic log, where P andS wave velocities of the sidewall are determined. Thegeotechnical log will also provide information aboutclay content and chemical alteration

➤ Rock stress. The orientation of the principal stress canbe determined if borehole breakout is visible on theteleviewer imagery. The stress ratio can be determinedfrom the geometry of the breakout. If the UCS iscalculated over this zone then the value of the principalhorizontal stress can be estimated

➤ Rock strength. The elastic moduli can be calculatedfrom the S- and -P-wave velocities derived from the fullwave sonic geophysical log. These would includeYoung’s modulus, Poisson’s ratio, bulk modulus, andUCS. The UCS values are dynamic and are usuallycalibrated against laboratory measurements. Theprocedure is to take core samples from zones wherethere is a clean geophysical response, over a range ofvalues and submit them for laboratory UCS determi-nations. A regression curve is calculated from theresulting data and the USC values are adjusted ifnecessary.

Sidewall stability index (SSI)

This is a parameter that was developed by AndersenGeological Consulting (2010) in order to quantify the stabilityof the sidewall of a raise bore. It is essentially an earlywarning system to alert the operators as to where sidewallconditions have deteriorated and sidewall collapse may occur.The index is not an absolute value, but is a probabilisticdetermination between ‘good’ and ‘very poor’ (Figure 4).

Barton (2002) takes the frictional component concept ofhis Q-factor further by referring to the fact that the ratio Jr/Jaclosely resembles the dilatant or contractile coefficient of thefriction for joints and filled discontinuities. The relativemagnitude of tan-1 (Jr/Ja) approximates the actual shearstrength that might be expected of the various combinationsof wall roughness and alteration products. Andersen (2010)combined this ratio with the true dip (from ATV) of the

Pre-sink shaft safety analysis using wireline geophysics

437The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

Figure 3 – Barton’s (1976) modified tunnel support chart showing his re-evaluation of the support/no support required

Pre-sink shaft safety analysis using wireline geophysics

discontinuities and produced the sidewall stability index. Thisindex relates to the stability of the sidewall, so dips wereranked from 60º to 90º, with a dip number of 2 beingallocated to the category 60º to 65º, and 12 for the category85º to 90º. Any dip less that 60º was given the value of unity.It was considered that dips of less than 60º were less likely tocause sidewall problems during raise-boring than steeperdips. The index has been expanded to include face stability,where fractures with low angles of dip are likely to drop outahead of the raise bore. Here the dip number is inverted, withthe flatter dips being given the higher dip numbers.

The stick plot technique has not been formally published,and was first used for the evaluation of several shafts atImpala Platinum over the period 2002 to 2004, followed byLonmin Platinum in 2002, Western Platinum in 2003, atLonmin Platinum’s Akanani Project in 2008, De Beers’Venetia diamond mine in 2011, and Lonmin Platinum in2013. The first public presentation of the technique was at asymposium of the SAIMM’s Bushveld Branch in 2004(Andersen, 2004), followed by workshops given at WitsUniversity, (Andersen, 2008, 2009). The concept of thesidewall stability index was first introduced at an SAIMMconference in 2010 (Andersen, 2010).

Summary of geotechnical results for theaccompanying stick plotThe accompanying stick plot (Figure 1) is for the upper 400 m section of a 1 100 m borehole that was drilled for theevaluation of a shaft site. The graphic has been reduced insize for the purpose of publication.

The results below summarize the geotechnical evaluationof this borehole using the stick plot technique for the purposeof sinking a shaft.

Q-factor analysisThe Q-factor was calculated over the entire depth of the shaftborehole using intervals of RQD measurement. As stability

varies with shaft diameter, an exercise was conducted tocalculate the Q-factor for different diameters of raise bore.Shaft diameters of 6 m, 5 m, 4 m, and 3 m have been used.The Q-factor changes because the joint set number (Jn)becomes smaller as the shaft diameter decreases. The barchart for the 6 m diameter shaft is shown in Figure 1.Superimposed onto this chart is the support/no support Q-factor value determined by Barton (1974, 1976). If Barton’s1974 value of <1.4 is used, then for a 6 m diameter shaft, itis estimated that approximately 2% (16.1 m) of the length ofthe shaft would have to be supported. If the 1976 value ofabout 5 is used then approximately 35% (353.12m) of theshaft would require support. The Q-factor values increase asthe shaft diameter decreases. However, there is not a markedchange as the fracture/joint dips are generally steeper than60º.

The Q-factor analysis is used to estimate support/nosupport for horizontal tunnels, so it should be interpreted inconjunction with the sidewall stability index.

Sidewall stability indexThe sidewall stability has been rated over the same intervalsas the Q-factor and UCS and is shown as a bar chart on thestick plot. The sidewall stability is rated between medium andgood down to a depth of 1006 m, after which there are somepoor zones.

It is important to note that the SSI refers to the probabilityof the sidewall of the shaft collapsing during excavation.However, the normalized Q-factor indicates the blocky natureof the rock mass, which would be caused by the jointing andfracturing in the rock. In a horizontal tunnel this would relatedirectly to support required, whereas in a vertical shaft itindicates possible rock fragmentation after blasting. The SSIis a better indicator of sidewall stability.

In general the sidewall stability is reasonable but thereare distinct zones where the dip of the fractures is greaterthan 60º and the fractures contain soft filling which could

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438 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 4 – The sidewall stability index. Matrix showing the calculation of the SSI based on angle of dip of the structure and shear strength derived fromBarton’s Jr and Ja parameters

result in sidewall collapse. These zones are classified as pooron the SSI.

Below 860 m the jointing intensity increases and the Q-factor shows zones that are rated between fair and poor withsome intervening sections being good. There is one very poorzone where there is a possibility of water inflow.

Overall trend of the structuresThe banding and foliation are the most prominent structurespresent in the borehole. These have a mean strike direction ofnorthwest to southeast, and dip dominantly to the southwestwith a small component dipping to the northeast.

The jointing has been separately classified and is alsoseen to follow the dominant foliation direction, although thedips are steeper with an average of about 75º. A shallowerdipping joint set is also present with a northwest to southeaststrike and a dip to the southeast.

It was possible to classify dip, oblique, and strike slipfaults. The orientations of the strike and oblique slip faultsare sympathetic to the jointing. These faults dip at more than70º. The dip slip faults form a conjugate set strikingnorthwest-southeast and dipping at about 45º to both thenortheast and southwest.

Sidewall boltingA series of stereograms has been calculated over 100 mintervals down the length of the shaft. These show the meandip and strike of the structures over each section. Thepurpose of these is to indicate the optimum direction forrockbolting, which is indicated by an arrow.

Groundwater intersectionsThe shaft will ‘make water’ from numerous joints fromdepths greater than 66 m, which is the current static waterlevel. These are shown in column 6 as blue intervals. Thedriller’s log did not indicate any water intersections.

Rock strength analysisThe rock strength values, including UCS, Young’s modulus,and Poisson’s ratio, were calculated using the P- and S-wavevelocities as determined from the full-waveform sonicmeasurements. The UCS values were overlaid onto thegeological log on the stick plot and the other values areavailable as text files.

ConclusionBarton’s Q-factor system (Barton et al., 1974) was initiallydeveloped to assess the design and support of horizontaltunnels and excavations. This system has been adapted foruse in a vertical shaft, with structural information beingderived from an acoustic televiewer (ATV) geophysicalwireline log and Barton’s geotechnical parameters describeddirectly from the borehole core at the depths indicated by theATV log. A colour-coded bar chart is then constructeddepicting Barton’s classification of rock mass quality betweenvery poor and extremely good, based on RQD domains.

As the stability of the sidewall is very important in shaftsinking, a sidewall stability index (SSI) is introduced. Thisindex is based on the dip of the structures (determined fromthe ATV) and the shear strength of the structure (Jr/Ja)determined from the geotechnical logging. Again, a colour-

coded bar chart is derived, ranking the sidewall stabilitybetween good and very poor.

Zones of possible water inflow into the shaft excavationare indicated based on wireline fluid conductivity, differentialtemperature, and impeller measurements.

Stereographic projections are constructed based ofstructural measurements made by the ATV. Dominant jointsets are identified that could cause structural failure if theirorientation is not taken into consideration when excavatingshaft stations or developing shaft bottom orepass raises.These joint sets are shown as graphical projections.

Rock strength parameters are calculated from the wirelinevelocity and density measurements. The orientation of theprincipal horizontal stress is determined from the orientationof borehole breakout observed on the ATV image.

McCracken and Stacey’s (1989) maximum unsupportedspan, based on the geotechnical parameters, is also calculatedbut is not shown on the stick plot.

The ‘stick plot’ is designed as an easy-to-read graphic forthe daily use of shaft sinkers to indicate rock conditions inadvance of the sinking. A detailed report discussing the rockengineering parameters is also provided for the rockengineers.

ReferencesANDERSEN, N.J.B. 2004. Structural and rock strength analysis of pre-sink shaft

boreholes using wireline geophysics. Symposium on the Bushveld IgneousComplex, Rustenburg. Bushveld Branch of the Geological Society of SouthAfrica.

ANDERSEN, N.J.B. 2008, 2009. Lectures to postgraduate mining engineeringstudents, University of the Witwatersrand.

ANDERSON, N.J.B. 2010. Pre-sink shaft safety analysis using wirelinegeophysics. South African National Committee on Tunnelling. SouthernAfrican Institute of Mining and Metallurgy, Johannesburg.

BARTON, N., LIEN, R., and LUNDE, J. 1974. Engineering classification of rockmasses for the design of tunnel support. Rock Mechanics, vol. 6. pp.189–236.

BARTON, N. 1976. Recent experiences with the Q-system of tunnel supportdesign. Proceedings of the Symposium on Exploration for Rock Engineers,Johannesburg, November 1976.

BARTON, N. 2002. Some new Q-value correlations to assist in site characteristicsand tunnel design. International Journal of Rock Mechanics and MiningSciences, vol. 39. pp. 185–216.

BIENIAWSKI, Z.T. 1973. Engineering classification of jointed rock masses.Transactions of the South African Institute of Civil Engineers, vol. 15. pp.335–344.

BIENIAWSKI, Z.T. 1976. Rock mass classification in rock engineering.Exploration for Rock Engineering. Bieniawski, Z.T. (ed.). Balkema, CapeTown. pp. 97–106.

BIENIAWSKI, Z.T. 1989. Engineering Rock Mass Classification. Wiley, New York.

LAUBSCHER, D.H. 1977. Geomechanics classification of jointed rock masses –mining applications. Transactions of the Institution of Mining andMetallurgy Section A: Mining Industry, vol. 93. pp. A70–A82.

MCCRACKEN, A. and STACEY, T.R. 1989. Geotechnical risk assessment for large-diameter raise-bored shafts. Transactions of the Institution of Mining andMetallurgy Section A: Mining Industry, vol. 98. pp. 309–316.

PECK, W.A. and LEE, M.F. 2008. Raise bored shafts in Australian Mines. 13thAustralian Tunnelling Conference, Melbourne, Vic., 4– May 2008.

RITTER, W., 1879. Die Statik der Tunnelgewölbe. Springer, Berlin.

WICKHAM, G.E., TIEDEMANN, H.R., and SKINNER, E.H. 1972. Support determinationsbased on geologic predictions. Proceedings of the North American RapidExcavation Tunneling Conference, Chicago. Lane, K.S. and Garfield, L.A.(eds). Society of Mining Engineers of AIME, Littleton, CO. pp. 43–64. ◆

Pre-sink shaft safety analysis using wireline geophysics

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 439 ▲

For further information contact:Head of Conferencing

Raymond van der Berg, SAIMMP O Box 61127, Marshalltown 2107

Tel: +27 (0) 11 834-1273/7E-mail: raymond@saimm.co.za

Website: http://www.saimm.co.za

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Copper CobaltAfrica

In association withThe 8th Southern African Base Metals Conference

6–8 July 2015Zambezi Sun Hotel, Victoria Falls

Livingstone, Zambia

Second Announcement

Join us for the inaugural Copper Cobalt Africa Conference in the heart of Africa.To be held at Victoria Falls, one of the Seven Natural Wonders of the World, this prestigious

event will provide a unique forum for discussion, sharing of experience and knowledge, andnetworking for all those interested in the processing of copper and cobalt in an African context, inone of the worldʼs most spectacular settings.

The African Copper Belt has experienced a huge resurgence of activity in recent years followingmany years of political and economic instability. Today, a significant proportion of capital spending,project development, operational expansions, and metal value production in the Southern Africanmining industry are occurring in this region. The geology and mineralogy of the ores aresignificantly different from those in other major copper-producing regions of the world, often havingvery high grades as well as the presence of cobalt. Both mining and metallurgy present someunique challenges, not only in the technical arena, but also with respect to logistics and supplychain, human capital, community engagement, and legislative issues. This conference provides aplatform for discussion of these topics, spanning the value chain from exploration, projects, throughmining and processing.

For international participants, this conference offers an idealopportunity to gain in-depth knowledge of and exposure to theSouthern African base metals industry, and to better understandthe various facets of mining and processing in this part of theworld that both excite and frustrate the industry.

A limited number of places are available for post-conferencetours to Kansanshi, the largest mine in Zambia, with 340 kt/ycopper production and its soon-to-be-completed 300 kt/ysmelter.

Jointly hosted by the mining and metallurgy technicalcommittees of the Southern African Institute of Mining andMetallurgy (SAIMM), this conference aims to:

• Promote dialogue between the mining and metallurgicaldisciplines on common challenges facing the industry,

• Encourage participation and build capacity amongst youngand emerging professionals from the Copper Belt region,

• Improve understanding of new and existing technologies,leading to safe and optimal resource utilisation.

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IntroductionIn 2013, coal production in China was 3680Mt, accounting for more than one-third of thetotal world output. Production from thickseams accounts for 40–50% of national coalproduction. China is also the country with thehighest incidence of mining disasters (Wu,Chen, and Long, 2012; Yuan, 2012). It istherefore essential to ensure mining safety inthick coal seams, especially concerning theprevention and control of hydraulic supportinstability (HSI), which poses a significantthreat to the safety of mine workers andequipment (Tu, Yuan, and Yang, 2009). Themost commonly used methods for mining thickcoal seam (over 3.5 m), are fully mechanizedmining with top coal caving (FMMTCC) andfully-mechanized mining with a large miningheight (FMMLMH). Experience shows thatrecovery rate in FMMLMH is 10–15% higher

than with FMMTCC. In recent years, with thedevelopment of mining equipment, FMMLMHtechnology has been widely used in thick coalseam mining. With its high recovery,FMMLMH has become a promising miningmethod for thick coal seams with a thicknessless than 7.0 m (Ju and Xu, 2014).

However, due to the large mining heightand intense induced ground pressure, HSIoften occurs in FMMLMH, especially in theworking faces of steeply dipping coal seams.The poor stability of hydraulic support anddifficulty of HSI control are the chief obstaclesto the widespread implementation of FMMLMHtechnology (Yuan et al., 2010). Therefore, thecontrol of HSI has become one of the mostvital techniques in FMMLMH (Yuan et al.,2010; He, Qian, and Liu, 1997). Figure 1shows hydraulic support tilting (HST) in aFMMLMH working face with a dip angle of15°. Considerble research has been done onthese issues. He et al. (1997) reportedmeasures to avoid HST when the roof wascaving or the hydraulic support was moving.Gong and Jin (2001) studied the relationshipbetween HST and its influencing factors. Theyclaimed that dip angle, mining height, theheight of centre of gravity of the hydraulicsupport, and sequence of hydraulic supportmovement can influence HST significantly.Hua and Wang (2008) studied HS stability ofthe inclined FMMLMH. Tu and Yuaninvestigated the HSI mechanism and its controlin a FMMTCC face in a steeply inclined seam(Tu et al., 2008; Yuan et al., 2008). Wanganalysed the stability and applicability ofhydraulic support with a two-prop shield(Wang, 2009; Liu, 2006).

Hydraulic support instabilitymechanism and its control in a fully-mechanized steep coal seam workingface with large mining heightby Y. Yuan*, S.H. Tu*, F.T. Wang*, X.G. Zhang* and B. Li*

SynopsisHydraulic support instability (HSI) is one of the most common causes ofdisasters in underground coal mining, posing a threat to the safety of mineworkers and normal operation of the equipment. It is prone to occur infully-mechanized mining faces with a large mining height (FMMLMH),especially when the dip angle of the coal seam is large. The key tocontrolling HSI is to deduce its mechanism and employ effective controltechniques. This paper focuses on the analysis of HSI types, the keyparameters and techniques to control HSI in FMMLMH, the establishmentof a model of HSI in FMMLMH, and a multi-parameter sensitivitymechanical model of different HSI forms in the no. 7219 longwall face inXutuan Coal Mine, Huaibei Mining Group, by using sensitivity analysis .The results show that HSI mainly presents in three forms: hydraulicsupport gliding (HSG), hydraulic support tilting (HST), and hydraulicsupport tail twisting (HSTT). The occurrence of the above three formsdepends mainly on support anti-instability capability. In the no. 7219longwall face, HSG and HST are the main two forms of HSI. The dip angleof the working face and the friction coefficient between floor andhydraulic support are the sensitive parameters for HSG, while HST isstrongly dependent on the dip angle of the working face and the frictioncoefficient between roof and hydraulic support. By the applications ofmeasures such as the oblique layout of the working face, cutting the floorinto a step pattern, moving the support under pressure, and raising thesetting load, the support stability was controlled effectively.

Keywordsfully mechanized mining, mining height, hydraulic support instability,sensitivity analysis, sensitivity parameter, control technique.

* Key Laboratory of Deep Coal Resource Mining,Ministry of Education of China, School of Mines,Mining Research Centre of China-Australia, ChinaUniversity of Mining & Technology, China.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. Paper receivedAug. 2012 and revised paper received Aug. 2014

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Most of the above studies focused on the role of factorssuch as mining height, dip angle, working resistance ofhydraulic support, the hydraulic support gravitational height,and sequence of hydraulic support movement on HSI. Thereis still a lack of relevance and effectiveness in HSI controltechnology; it is difficult to make proper adjustments tocontrol HSI effectively for a particular case. This paperestablishes a HSI mechanical model for FMMLMH throughthe critical stability conditions under three main instabilityforms – hydraulic support gliding (HSG), hydraulic supporttilting (HST), and hydraulic support tail twisting (HSTT). Thesensitive parameters were obtained via multi-parametersensitivity analysis of HSI. Based on the results, controlmeasures were carried out to tackle the HSI problem, andsatisfying results have been achieved in field applications.

Mechanical model of HSIHSI mainly takes the forms of HSG, HST and HSTT in aFMMLMH working face (Yuan et al., 2008). In order to derivethe following equations, we assume that the forces imposingon the hydraulic support from the overburden and the floorare both uniformly distributed and the hydraulic support hasa sequence number from the top to the bottom, such as 1, 2,3…, n where n is the total number of hydraulic supports inthe working face).

Hydraulic support gliding As shown in Figure 2, due to the combined influence ofhydraulic support weight W, the overburden resultant force P,the floor resultant force R, and the force between the adjacentsupport Ts and Tx, the hydraulic support tends to glide alongthe floor.

In order to avoid HSG, the anti-gliding force, which canbe calculated from Equation [1] (Gong and Jin, 2001; Yuan etal., 2008; Yuan, 2011), should be not less than the glidingforce.

[1]

where f1 is the friction coefficient between roof and hydraulicsupport, f2 is the friction coefficient between floor andhydraulic support, and α is the dip angle of the working face.

Assuming nh is the number of HSG, then the followingsituations apply.

(1) If nh=1 and Ts= Tx, i.e. the number of HSG is one,then Equation [1] can be divided into two cases:

Case 1: the hydraulic support does not contact the roof,i.e. P=0, then Equation [1] can be simplified as f2≥tanα，thatis, the condition of hydraulic support remaining stable underits own weight.

Case 2: the hydraulic support contacts the roof, i.e. P>0.The critical angle of HSG αhl can be obtained by Equation [1]as follows:

[2]

In general, f1 is affected by the structural integrity of theroof, and varies from 0.35 to 0.40; f2 is affected by floorwater, float coal, and period weighting pressure, and variesfrom 0.22 to 0.82 (Gong and Jin, 2001).

According to Equation [2], αhl is proportional to P, f1 andf2; αhl is inverse to W.

(2) If 1<nh<n and Ts≠Tx, i.e. one HSG leads to the glide ofadjacent supports, some supports in one certain area willglide. Assuming the first hydraulic support in the upper faceglides, then Tx=0, and the induced force to the secondhydraulic support can be calculated through Equation [3]:

[3]

Similarly, the gliding force induced by nh supports is:nhThs(1〜2). If there are k(k>1) supports forming a groupbeneath the nhth support, then the stability of the lowersupport group can be calculated through Equation [4]:

[4]

where Ths(1-2) is the gliding force from the first hydraulicsupport to the second.

According to Equations [3] and [4], the support grouptends to glide with increasing α, nh and decreasing k.

(3) For the case nh=n, as all of the supports glide, thesmallest anti-gliding force to maintain stability of the supportis nThs(1-2), This case occurs only rarely and is easy tocontrol.

Hydraulic support tiltingWith the existence of an inclination component of force alongthe coal seam, the point of resultant force deviates from thelower edge of the support, which is the reason for the support

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442 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Hydraulic support instability mechanism

Figure 1– HSG in a FMMLMH working face

Figure 2 – Model of HSG and HST

tilt. As shown in Figure 2, on the condition of the moment-limiting balance of torque, the point of reacting force is O. Inorder to avoid HST, the anti-tilting torque should no less thanthe tilting torque, and can be calculated from Equation [5](Gong and Jin, 2001; Hua and Wang, 2008; Yuan et al.,2008; Yuan, 2011):

[5]

where h is the mining height, B is the width of the HS base,and C is the height of the centre of gravity of the hydraulicsupport.

Assuming the number of HSTs is nd, then the followingrelationships can be derived.

(1) For the case nd=1, Ts= Tx, i.e. the number of HSTs isunity, this circumstance can be divided into three conditions.

(a) The hydraulic support does not contact the roof, i.e.P=0. Equation [5] can be abbreviated as

which is the condition for hydraulic support to keep inbalance in the free state.

(b) The hydraulic support contacts the roof, i.e. P>0. Thecritical angle of HST αdl can be derived from Equation [5] asfollows:

[6]

According to Equation [6], the possibility of HST becomessmaller when the value of αdl increases under the followingconditions: C decreases, f1 or B increases. There is anarctangent function relationship between 1/h and αdl, and thesuitable mining height decreases as αdl increases. That iswhy the hydraulic support tends to tilt when mining heightincreases, even in coal seams with a low dip angle as shownin Figure 1.

(c) The hydraulic support tends to tilt when hydraulicsupport falls or moves or when the roof is caving or broken.The loading pressure P then decreases, even to P=0. ThenEquation [6] can be simplified as

i.e. the value of αdl depends mainly on

(2) For the case 1<nd<n, Ts≠Tx, assuming the firsthydraulic support of the upper face end tilts, the pressure onthe second hydraulic support Tds(1-2) can be calculated byEquation [7]:

[7]

Similarly, the pressure transferred from the i-1th to theith hydraulic support is defined as Tds((i-1)-i). Assuming thereis a hydraulic support group formed by j supports beneaththe ith hydraulic support, the stability of the hydraulic

support group can be ensured through Equation [8]:

[8]

According to Equation [8], the hydraulic support becomesmore stable with decreasing dip angle and increasing i and j.

(3) For the case nd=n, all the supports of the working facetilt. This situation is not tolerated in field production, and willnot be discussed in this paper.

Hydraulic support tail twistingThe height of the caving zone is much larger in a FMMLMHworking face. The impulse force due to the caving rock afterhydraulic support moves will cause the tail part of thehydraulic support to twist. If the anti-twist capacity of thehydraulic support is not enough to resist the lateral forcecaused by the component force due to caving in a down-dipdirection, the tail of the hydraulic support will rotate andcause the support to tilt. An increase in the dip angle of theworking face will aggravate this phenomenon.

As shown in Figure 3, L is the distance from the workingline of the resultant force P from the roof to the end part ofthe top beam; Ld is the distance from the working line of theresultant force from the floor to the projection of the end partof top beam on the substructure; D is the length of the tailbeam; and θ is the rotation angle of the tail beam.

The weight of caving rock on the hydraulic support tailbeam G can be expressed as:

[9]

Hydraulic support instability mechanism

443The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

Figure 3 – Mechanical model of the anti-twist analysis in the tail of thehydraulic support

Hydraulic support instability mechanism

where H is the caving rock height, (metres) and γ is theaverage force per unit volume, (kN/m3);

The torque force of caving rock on the hydraulic supporttail beam F1 can be expressed as:

[10]

where f3 is the friction coefficient between caving rock andhydraulic support tail beam.

The torque M1 can be expressed as：

[11]

The anti-twist force along the roof F2 caused by P can beexpressed as:

[12]

The anti-twist force along the floor F3 caused by R can beexpressed as:

[13]

Then the anti-torque M2 can be expressed as:

[14]

In the case M2≥M1, hydraulic support remains stable, andthe critical angle of hydraulic support tail tilting αnl can beexpressed as:

[15]

It is important to note that the above analysis depends onthe axis being located in the midpoint of the top beam end.The axis position changes with the development of gliding. Itmay locate in the midpoint of the substructure end, and theanalysis procedure is similar to the above.

According to Equation [15], the possibility of HSTTbecomes smaller when the value of αnl increases under theconditions as follows: f1, f2, P, W, L, or Ld increases, and B,H, f3, D, or θ decreases.

Based on the above analysis, the instability of onesupport is the cause of the unstable support in the workingface. Whether the HSI is controlled effectively depends on therelationship between the critical instability angle and the dipangle of the working face. To evaluate the HSI quantitatively,a stability factor K is included:

[16]

where αlj is the critical instability angle and α is the dip angleof the working face. There are three possible scenarios,depending on the value of K:

K>1, the hydraulic support is stable K=1, the hydraulic support is in a critical balanced state K<1, the hydraulic support is unstable. The value of K is the least value of three parameters Kh,

Kd, and Kn, i.e.:

[17]

where Kh, Kd, and Kn are the coefficients of anti-glide, anti-tilting, and anti-tail twisting for the hydraulic support,respectively:

[18]

[19]

[20]

According to Equations [17]–[20], there are many factorsaffecting the HSI, including α, f1, f2, f3, P, W, h, B, D, H, L, Ldand θ. It is a complex systematic problem to analyse theimpact of the above factors on the HSI. Thus, it is necessaryto conduct a quantitative analysis on the problem. Sensitivityanalysis provides a new method of solving such a problem.

The sensitivity analysis method The sensitivity analysis method (SAM) is a systemic analysismethod focusing on the stability of a system (Zhang andZhu, 1993; Zhu and Zhang, 1994; Hou et al., 2005).Assuming that there is a system, the characteristic U isdetermined by factors in the number of m:β1={β1,β2...,βm},U=ϕ(β1,β2...,βm). The system characteristic is under thecriterion state β*={β1*,β2*...,βm*}. The definition of SAM is thetrend and degree of deviation of U from U*, due to changes ofeach factor in its own possible range.

The first step of SAM is to establish a system model, i.e.forming a function relationship between the systematicalcharacteristics and factors U=ϕ(β1,β2...,βm). It is much betterto define the function relationship with an analyticexpression. For the complex system, numerical anddiagrammatic methods are also appropriate to represent thefunction relationship. The establishment of the system model,conforming to the practical system to its greatest extent, isthe key to the effective analysis of the parameter sensitivity.

It is necessary to provide a criterion parameter set, whichis based on the specific questions. For example, the rockmass characteristics in a specific working face can be takenas a criterion parameter set to analyse the sensitivity ofsupport stability to the given parameters. Sensitivity analysiscan be conducted as the criterion parameter set has beendefined. When the influence of parameter βk on the charac-teristic U is analysed, the remaining parameters are taken ascriterion parameters and remain constant, βk varies in thepossible range, and then U can be expressed:

[21]

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444 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

indicates that U is very sensitive to βk, and βk can be termeda hypersensitive parameter. Otherwise, βk is a less-sensitiveparameter.

For purposes of comparison with each property-differentand unit-different parameter, a dimensionless sensitive factoris defined as follows:

[22]

where S(βk) is the sensitivity of βk; A* is the correspondingcharacteristic value of criterion parameter set; and Aβk maxand Aβk min are the maximum and minimum characteristicvalues varying in the range of βk, respectively.

Multi-parameter SAM of HSI in FMMLMH Tthe no.7219 working face in Xutuan Mine is used as anexample. The coal seam thickness ranges from 4.0 to 6.5 m,with an average of 6.0 m, and the dip of the seam varies from5° to 25°, with an average of 12°. The hydraulic supportsystem used is ZY11000/28/63, and the main technicalparameters are listed in Table I.

Eleven parameters are selected to analysee their influenceon HSI, i.e. α, f1, f2, f3, P, h, B, H, L, Ld, and θ. Using thegeological conditions, the ground pressure observations atthe working face, and the parameters in Table I, the averagevalue and range of variation for each parameter are listed inTable II. The the average value and variation range of the 11parameters are derived as follows. The parameters of α and hare obtained from the geological conditions of the workingface; P and H are derived from field measurement of stratabehavioer; B L, Ld, and θ are acquired from the technicalparameters of the ZY11000/28/63 hydraulic support; and f1,f2, and f3 are obtained from Gong and Jin (2001), Yuan et al.(2008), and Yuan )2011).

The sensitive parameters, derived by using SAM, thataffect the support stability in the no.7219 working face arelisted in Tables III, IV, and V.

The parameters can be classified into three categories –sensitive parameters (S>0.5), less-sensitive parameters(0.1≤S≤0.5), and non-sensitive parameters (S<0.1) (Yuan,2011; Zhang and Zhu, 1993).

In the anti-gliding condition, α and f2 are sensitiveparameters, and f1 and P are less-sensitive parameters. In theanti-tilting condition, α and f1 are sensitive parameters, withP being less sensitive and h, C, and B being non-sensitiveparameters. In the anti-tail tilting condition, α, f2, f3, P, and θare sensitive parameters, f1, H, L, and Ld are less-sensitiveparameters, and B is the non-sensitive parameter. Therelationships between the hydraulic support stabilitycoefficient and the sensitivity are shown in Figures 4, 5, and6, derived using the MATLAB software package.

Figures 4, 5, and 6 indicate that:

Hydraulic support instability mechanism

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 445 ▲

Table II

The standard value and variation of each parameter

Parameter α (°) f1 L (m) f3 B (m) H (m)

Average value 12 0.3 1.2 0.2 1.75 6Range 5–25 0–0.4 0.5–1.6 0.1–0.3 1.67–1.83 4.0–6.5Parameter P (kN) f2 Ld (m) Θ (°) H (m)Average value 7000 0.4 2 50 15Range 0–8800 0.22–0.82 1.7–2.6 35–65 12–18.3

Table I

Main technical parameters of ZY11000/28/63 hydraulic support

Item Parameter Item Parameter

Centre distance (m) 1.75 Supporting height (m) 2.8–6.3Width (m) 1.67–1.83 Pump pressure (Mpa) 31.5Setting load (kN) 7648–8065 oscillation angle of tail beam (°) 35–65Working resistance (kN) 10627–11207 Top beam length (m) 3.12Support intensity (MPa) 1.25–1.32 Support weight (t) 42.6Pressure of base (Mpa) 2.04–2.45 Roof-control distance (m) 6.5

Table III

Sensitive parameters that affect stability capabilityof HSGParameter α f1 f2 PSensitivity 1.4295 0.4142 0.6151 0.4142

Table IV

Sensitive parameters that affect stability capabilityof HSTParameter α f1 h P C BSensitivity 1.43 0.5612 0.0896 0.1175 0.0343 0.0076

Table V

Sensitive parameters that affect stability capabilityof HSTTParameter α f1 f2 f3 HSensitivity 1.4295 0.2978 0.7373 1 0.25Parameter P L Ld θ BSensitivity 0.9597 0.1737 0.2106 1.3828 0.0479

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446 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Hydraulic support instability mechanism

Figure 4 – Relationship between sensitive parameters (α and f2 ) and anti-HSG stability coefficient (Kh)

Figure 5 – Relationship between sensitive parameters (α and f1) and anti-HST stability coefficient (Kd)

Figure 6 – Relationship between sensitive parameters (α, f2, f3, P and θ) and anti-HSTT stability coefficient (Kn)

➤ The anti-HSTT stability coefficient Kn is relatively high,indicating that HSG and HST are the main HSI forms inthe no.7219 working face

➤ The parameters of α and f2 have a greater impact onthe anti-gliding stability coefficient Kh compared with f1and P. To avoid HSG, it is essential to increase thevalue of f2 or to decrease the value of f1, for example,by making the layout of the working face with apparentdip and step pattern floor. Increasing the values of f1 orP is also an effective way to prevent HSG,, for example,by moving the hydraulic support under pressure

➤ The anti-tilting stability coefficient Kd is fairly sensitiveto f1, therefore emplacing the hydraulic supporttimeously after moving the support is an effective wayto prevent HSG.

ConclusionsHydraulic support instability (his) is likely to occur on steeplydipping coal seam working faces where fully-mechanizedmining with a large mining height (FMMLMH) is applied. Inorder to control HSI, the mechanism of HSI in FMMLMH wasmodelled and interpreted, and the critical instability anglesfor the three HSI forms, i.e. hydraulic support gliding (HSG),hydraulic support tilting (HST), and hydraulic support tailtwisting (HSTT) were obtained.

HSI in FMMLMH is a complex system issue influenced by11 factors. Sensitivity analysis is an effective way to analysethe sensitivity of these factors and improve the effectivenessof hydraulic support stability control.

The multi-parameter sensitivity analysis of HSI in theno.7219 working face indicated that HSG and HST are thetwo main instability forms for HSI, as the anti-HSTT stabilitycoefficient in this working face is relatively high. HSG issensitive to the dip angle of the working face and the frictioncoefficient between floor and hydraulic support, while the dipangle of the working face and the friction coefficient betweenroof and hydraulic support are the main controllingparameters for HST. Through the above analysis, the obliquelayout of the working face, step pattern of the floor, timelyadvance of support under pressure, and raising the settingload are the main technical measures to improve the supportstability in the no.7219 working face, which contribute to thesafety and efficiency of mining operations.

Acknowledgments Financial support for this work was provided by the PriorityAcademic Program Development of Jiangsu Higher EducationInstitutions, and the Fundamental Research Funds for theCentral Universities (No. 2014QNB32).

References

Gong, P.L. and Jin, Z.M. 2001. Research on influencing factors of tilt for fully-

mechanized mining support with large mining height. Journal of TaiyuanUniversity of Technology, vol. 32, no. 6. pp. 666–669.

He, F.L., Qian, M.G., and Liu, C.Y. 1997. Efficiency face bracket-rock security

system. China University of Mining and Technology Press, Xuzhou.

He, F.L., Qian, M.G., Liu, X.F., Chen, L.W., and Li, C.F. 1997. Tilt character-istics and control conditions of high powered support. Journal of ChinaUniversity of Mining and Technology, vol. 26, no. 4. pp. 20–24.

Hou, Z.S., Li, X., Wang, S.J., and Lu, S.B. 2005. Sensitivity analysis ofmechanical parameters to deformation of surrounding rocks for a tunnelin Jinchuan deposit II. Chinese Journal of Rock Mechanics andEngineering, vol. 24, no. 3. pp. 406–410.

Hua, X.Z. and Wang, J.C. 2008. Analysis and control of hydraulic supportstability in fully-mechanized longwall face to the dip with great miningheight. Journal of Coal Science and Engineering (China), vol. 14, no. 3.pp. 399–402.

Ju J.F. and Xu J.L. 2014. Structural characteristics of key strata and stratabehavior of a fully mechanized longwall face with 7.0 m height chocks.International Journal of Rock Mechanics and Mining Science, no. 58. pp.46–54.

Liu, J.F. 2006. Study on adaptability of shield support in large cutting heightworking face. Coal Science Research Institute, Beijing.

Tu, S.H., Yuan, Y., and Yang, Z. 2009. Research situation and prospect of fullymechanized mining technology in thick coal seams in China. ProcediaEarth and Planetary Science, vol. 1, no. 1. pp. 35–40.

Tu, S.H., Yuan, Y., Li, N.L., Dou, F.J., and Wang, F.T. 2008. Hydraulic supportstability control of fully mechanized top coal caving face with steep coalseams based on instable critical angle. Journal of Coal Science andEngineering (China), vol. 14, no. 3. pp. 382–385.

Wang, G.F. 2009. Research on mining technology with high mining height anddevelopment of powered support for high mining height. Coal MiningTechnology, vol. 14, no. 1. pp. 1–4.

Wu P., Chen H., and Long R.Y. 2012. Relationship between coal output andsafety in China. Disaster Advances, vol. 5, no. 4. pp. 551–556.

Yuan X.P. 2012. The characters and trend of accidents in the coal mining inChina. Disaster Advances, vol. 5, no. 4. , pp. 866–869.

Yuan, Y. 2011.Stability control mechanism of support-surrounding rocks atfully mechanized mining face with great cutting height. China Universityof Mining and Technology, Xuzhou.

Yuan, Y., Tu, S.H., Dou, F.J., and Wu, Q. 2008. Support instability mechanismof fully mechanized top coal caving face with steep coal seams and itscontrol. Journal of Mining and Safety Engineering, vol. 25, no. 4. pp.430–434.

Yuan, Y., Tu. S.H., Wang, Y., Ma, X.T., and Wu, Q. 2010. Discussion on keyproblems and countermeasures of fully mechanized mining technologywith high mining height. Coal Science and Technology, vol. 38, no. 1. pp.4–8.

Zhang, G. and Zhu, W.S. 1993. Parameter sensitivity analysis and optimizingfor test programs. Rock and Soil Mechanics, vol. 14, no. 1. pp. 51–58.

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The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 447 ▲

Technical Conference andIndustry day

26–27 August 2015—Conference28 August 2015—Industry day

OBJECTIVESThe conference will focus on improvinghealth, safety and the environmentalimpact in the mining and metallurgyindustry and highlight actions to betaken. It will act as a platform for learningand allow people to share ideas onsafety, health and the environment.

This conference aims to bringtogether management, DMR, Chamberof Mines, Unions and health and safetypractitioners at all levels from theindustry to share best practice andsuccessful strategies for zero harm and avalue-based approach to health andsafety. It will address the mainchallenges in the mining industry such aslogistics, energy and safety ofemployees, contractors and thecommunities.

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The conference should be of value to:˙ Safety practitioners˙ Mine management˙ Mine health and safety officials˙ Engineering managers˙ Underground production supervisors˙ Surface production supervisors˙ Environmental scientists˙ Minimizing of waste˙ Operations manager˙ Processing manager˙ Contractors (mining)˙ Including mining consultants, suppliers

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IntroductionIn recent years, stoping methods have beenmagnified as an extraction strategy inunderground hard rock mining. According to areport from Austrade (2013), in 2013 70% ofunderground metal mines in Australia utilizedopen stoping, sublevel stoping, various narrowstoping methods, and other types of stopingand caving methods to extract ore. In addition,51% of underground metal production inCanada relies on open stoping methods(Pakalnis et al., 1996). A disadvantage ofpopular stoping methods is that many minesare severely affected by dilution problems.Pakalnis (1986) surveyed 15 open stopeCanadian mines and concluded that 47% of theoperations suffered from more than 20%dilution. Likewise, Henning and Mitri (2007)reported that approximately 40% of openstoping operations in Canada suffered from10% to 20% dilution.

Dilution can be defined as ‘the contami-nation of ore with lower grade material’, and itcan be generally classified into two categories:planned and unplanned. Planned dilution, alsoreferred to as primary or internal dilution, is

contamination by low-grade material withinthe ore block; whereas unplanned dilution,also known as secondary or external dilution,is contamination by lower grade materialexterior to the ore block. Both of these dilutethe ore stream value. Ore loss can similarly beclassified as planned and unplanned. Plannedore loss represents the ore-grade material thathas been excluded from the mining block atthe stope design stage, whereas unplanned oreloss is the part of the mining block thatremains in the stopes after mining.

This study includes unplanned dilutionand ore loss, which can be attributed to asover- and underbreak in underground stopingproduction. These phenomena can be dividedinto dynamic and quasi-static types; the quasi-static type occurs after blasting, while thedynamic type occurs immediately (Mandal andSingh, 2009). The dynamic over- andunderbreak types are the main interest of thisstudy, and a new terminology, ‘uneven break’(UB), is used to identify it. To extend thedefinition of dilution, UB can be defined as thetons of mined unplanned dilution (positive) orore loss (negative) per ton of ore mined,expressed as a percentage.

UB rate = (tons of unplanned dilution ororeloss/tons ore mined) x 100 [1]

UB affects not only the safety of theworkforce and machinery, but is also severelydetrimental to the viability of the operationthroughout all of the mining stages. It directlydowngrades the ore and causes unnecessarymucking, haulage, crushing, hoisting, andmilling activities, thus decreasing productivity.Despite these effects, current UB management

Unplanned dilution and ore lossprediction in longhole stoping mines viamultiple regression and artificial neuralnetwork analysesby H. Jang*, E. Topal*, and Y. Kawamura*

SynopsisUnplanned dilution and ore loss directly influence not only the produc-tivity of underground stopes, but also the profitability of the entire miningprocess. Stope dilution is a result of complex interactions between anumber of factors, and cannot be predicted prior to mining. In this study,unplanned dilution and ore loss prediction models were established usingmultiple linear and nonlinear regression analysis (MLRA and MNRA), aswell as an artificial neural network (ANN) method based on 1067 data-sets with ten causative factors from three underground longhole stopingmines in Western Australia. Models were established for individual mines,as well as a general model that includes all of the mine data-sets. Thecorrelation coefficient (R) was used to evaluate the methods, and thevalues for MLRA, MNRA, and ANN compared with the general model were0.419, 0.438, and 0.719, respectively. Considering that the currentunplanned dilution and ore loss prediction for the mines investigatedyielded an R of 0.088, the ANN model results are noteworthy. Theproposed ANN model can be used directly as a practical tool to predictunplanned dilution and ore loss in mines, which will not only enhanceproductivity, but will also be beneficial for stope planning and design.

Keywordsstoping, unplanned dilution, ore loss, artificial neural network.

* Department of Mining Engineering andMetallurgical Engineering, Western AustralianSchool of Mines, Curtin University, Australia.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. Paper receivedNov. 2014 and revised paper received Feb. 2015.

449The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

ISSN:2411-9717/2015/v115/n5/a13http://dx.doi.org/10.17159/2411-9717/2015/v115n5a13

Unplanned dilution and ore loss prediction in longhole stoping mines

relies on previous production results from similar stopes andveteran engineers’ intuitions. UB has been considered anunavoidable and unpredictable phenomenon in practice dueto the extremely complex mechanism of interaction betweenthe many causative factors.

In this study, new UB prediction models are establishedthrough multiple linear and nonlinear regression analysis(MLRA and MNRA) as well as an artificial neural network(ANN) method. The data was collected from threeunderground longhole stoping mines in Western Australia.The UB prediction models were established for individualmine sites. Moreover, entire data-sets were used to propose ageneral model to predict UB. The proposed models arecompared with actual stope dilution and the current UBpredictions for mines.

Overview of unplanned dilution and ore loss instoping Unplanned dilution and ore loss directly affect theprofitability of mining and are the most critical factorsaffecting the economics of underground stoping. Theimportance of unplanned dilution and ore loss control hasbeen emphasized by many researchers. Tatman (2001)reported that reducing unplanned dilution is the mosteffective way to increase mine profits, and Henning and Mitri(2008) stressed the significant influence of unplanneddilution on the profitability of mining operations. Morespecifically, in 2008, Stewart and Trueman (2008) reportedthat unplanned dilution costs A$25 per ton, compared withA$7 per ton for mucking and haulage, and A$18 per ton formilling in typical narrow-vein longhole stoping operations.Suglo and Opoku (2012) conducted a study on Kazansi Mineand calculated the financial loss from unplanned dilutionduring 1997 to 2006 at US$45.98 million. In 2002, US$11.30million was spent to control unplanned dilution at theKonkola Mine in Zambia (Mubita, 2005).

Previous studies on unplanned dilution and ore losscontrolUnplanned dilution and ore loss have been investigated bymany researchers, but a practical prediction model thatconsiders a broad range of causative parameters has not beenintroduced until now. Most of the previous studies haveattempted to discover the relationship between unplanneddilution and a few particular causative factors.

Germain and Hadjigeorgiou (1997) used simple linearregression to analyse stope overbreak at the Louvicourt Minein Canada. The stope performances were recorded by a cavitymonitoring system (CMS), and the actual stope geometriesand blasting patterns were recorded. The authors concludedthat the relationship between stope performance and other

dependent parameters is very complex, and the correlationcoefficient (R) values for the powder factor and Q-value wereonly -0.083 and 0.282, respectively. Despite this data, thestope volume showed a moderate positive correlation with theratio between stope volume and its real surface area (RVS).

Wang et al. (2002) found that dilution was greater for aparallel drilling pattern than for a fanned pattern, while Clarkand Pakalnis (1997) reported that dilution tended to increasewhen blast-holes fanned out. Stewart (2005) demonstratedthe difficulties of predicting unplanned dilution by comparingthe conflicting results; opposing observations on theunplanned dilution problem are common and evident whenan aspect of the unplanned dilution problem is restricted to afew causative parameters.

Henning and Mitri (2007) used a three-dimensionalelastic-plastic numerical analysis program (Map3D) toexamine the influence of depth, in-situ stress, and stopegeometry on the stope wall overbreak. The authorsconsidered two criteria: the area of zero tensile strength (σ3 = 0) and the tensile strength of the rock mass (σ3 = σt).The contour of the relaxation zone (σ3 = 0) remained nearlyconstant, while the potential overbreak associated with therock mass tensile strength increased with depth for a givenstope geometry or hangingwall dip. Subsequently, theinfluence of the mining sequencing on blast-hole stopingdilution was studied (Henning and Mitri, 2008) in 172sequentially mined longhole stopes. The authors concludedthat the overbreak significantly increased in stopes that hadone or more backfilled walls.

Recently, the stability graph method (Mathews et al.,1981; Potvin, 1988) has been commonly used to design andmanage stope stability. The stability graph method is a usefultool for constructing a site-specific database, and someauthors have indicated that it may facilitate stope dilutionprediction (Diederichs and Kaiser, 1996; Pakalnis et al.,1996). Despite of the popularity of the stability graphmethod, some limitations have been pointed out by severalresearchers. Martin et al. (1999) stated that the far fieldstress with respect to the stope orientation has not beenconsidered. In addition, Potvin and Hadjigeorgiou (2001)noted that the method is not appropriate for controllingrockburst conditions. Furthermore, the method does notconsider any blasting factors, induced stress by stopedeveloping sequences, and the exposure time of the stopewall. Essentially, the UB could not be predicted in practiceusing the stability graph method itself. For instance, thestability graph method was also used at the three mines inthe current investigation as a guideline for stope design and atool to predict unplanned dilution. The predicted perfor-mances are shown in Table I.

As shown in Table I, the UB predictions were extremely

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450 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table I

Comparisons between the actual and current predicted unplanned dilution as well as UB for Mine A, Mine B, andMine C as well as the general model

Correlation coefficient (R) Mine A Mine B Mine C General model

Unplanned dilution and ore loss (uneven break) 0.0734 -0.0518 0.0583 0.0884Unplanned dilution (overbreak only) 0.0971 -0.2159 0.2557 0.3101

unsatisfactory and were limited to only unplanned dilution,but not ore loss. In addition, comparisons between the actualand current predicted unplanned dilution as well as thecurrent UB predictions for the general model are shown inFigure 1.

Despite significant research effort, UB is still consideredan unpredictable phenomenon. This study provides anoptimistic perspective on predicting the potential UB usingthe advanced artificial neural network method.

Factors that influence unplanned dilution and ore lossUB can be defined as a dynamic-type over- and underbreakthat corresponds to unplanned dilution and ore loss.Numerous interacting factors affect UB. Thus, the mechanismunderlying UB cannot be properly analysed based only on asingle causative factor or a group of factors, but it isimperative to consider the entire range of factors that maycontribute.

Several researchers (Clark, 1998; Henning and Mitri,2007; Mathews et al., 1981; Mubita, 2005; Potvin, 1988;Stewart, 2005; Tatman, 2001; Villaescusa, 1998; Yihong andWeijin, 1986) have indicated that the factors that are likely tocause unplanned dilution and ore loss can be divided intothree core groups with one subsidiary group (i.e. blasting,geological, stope design, and human error and others).Certain representative recommendations were classified intofour groups and are summarized in Table II.

Different terminology has been used by various investi-gators, but the fundamental considerations for the UBphenomena causative factors correspond. The ten UBcausative factors employed in this study are also provided inTable II, and the details are given in Table III.

In this research, the adjusted Q rate (AQ) and averagehorizontal to vertical stress ratio (K) were used as therepresentative geological parameters, and five blastingparameters, including the average length of blast-hole (Blen)powder factor (Pf), angle difference between the hole andwall (AHW), diameter of blast-hole (Bdia), and space andburden ratio (SbR), were used as the likely causative factorsfor UB. The planned tons of stope (Pt), aspect ratio (AsR),and stope either breaking through into a nearby drift and/orstope or not (BTBL) were considered the likely UB causativefactors in the stope design category. Because the datacollection herein relied on historical records, human errors

such as blast-hole deviation and drilling error wereimpossible to obtain. Thus, the average blast-hole length(Blen) data was collected to indirectly compute the blast-holedeviation, because drilling accuracy for longhole drilling isgenerally expressed as a percentage of the blast-hole depth(Stiehr and Dean, 2011).

Data collectionData collection is a herculean task but vital for this study,and the prediction capability of the proposed model fullyrelies on the data quality. To avoid potential bias andestablish appropriate UB prediction models, the extensiverange of historical stope reconciliation data and geologicaldata was scrutinized. Initially, 1354 data-sets with 45parameters in three core causative categories were collectedvia a thorough review of over 30 000 historical documentsfrom three underground longhole and open stoping mines inWestern Australia. Ultimately, ten parameters were used asthe representative causative factors for UB. Figure 2 is anoverview of one investigated mine with an example of stopereconciliation data using a cavity monitoring system (CMS).

As shown in Figure 2, the planned stope model wascompared with the actual stope model, which was obtainedusing the CMS. The unplanned dilution volume and ore losspercentage were calculated not only for the hangingwall butalso the footwall. The geology, stope design, and blastingdata were collected in succession through examiningcorresponding documents.

For a coherent analysis, 287 abnormal data-sets wereremoved through examining univariate, bivariate, andmultivariate outliers because they could influence theanalysis in various ways and decrease the reliability of theproposed model.

Multiple regression analysisMultiple linear and nonlinear regression analysis (MLRA andMNRA) were applied to propose an appropriate UB estimationmodel for each mine site, as well as a general model based onall of the data-sets.

Multiple linear regression analysis Initially, all independent variables were forced into the MLRAmodel, which is referred to as the MLRA-enter model. Next,the insignificant variables were progressively removed to

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451The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

Figure 1 – Comparisons between the actual and current predicted unplanned dilution as well as UB for all of the data-sets from the three minesinvestigated. (A) unplanned dilution comparison, (B) UB comparison

Unplanned dilution and ore loss prediction in longhole stoping mines

obtain the optimised MLRA-stepwise model based on thestatistical significance levels for F and t values. The MLRA-enter and stepwise model results are shown in Table IV.

As can be seen from Table IV, for the Mine A data-set,the adjusted coefficients of the determinant (R2adj) for theMLRA-enter and stepwise models were 0.280 and 0.293,respectively. The K, Blen, Hdia, BTBL, and SbR were removedfrom the MLRA-stepwise model due to their low t-valuesignificance levels. The Mine B data-set MLRA models

yielded the highest R2adj values among the MLRA models; theMLRA-enter and stepwise models yielded 0.322 and 0.330,respectively. Hdia was not considered in the models becausethe Mine B data-set had only one value for this variable. Thet-values for BTBL, SbR, Pt, Pf, and AsR were inadequate;they were therefore removed from the MLRA-stepwise model.The MLRA-enter and stepwise models for Mine C had thelowest adjusted coefficients of the determinant (R2adj), 0.166and 0.163, respectively. The F and t values for K, Hdia, SbR,

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Table II

Summary from representative studies on causative factors for UB

Reference Geological Blasting Stope design Human error and others

Potvin (1988) Block size, stress, jointorientation, and gravitysupport

Blasting practice Stope geometryInclination

Backfill and adjacent stopetiming

Villaescusa (1998) Poor geological controlInappropriate supportschemes

Poor initial blast geometryIncorrect blast patternsSequences of explosivetypes

Poor stope designLack of proper stopesequencing

Deviation of blast-holesLack of supervision &communicationRushed stope planning andlack of stope performancereview

Clark (1998) Rock quality and majorstructuresstress

Blast-hole geometryUp- and down-holesBreakthroughsParallel and fanned holesExplosive typesBlast sequences

UndercuttingStoping sequence, supports,and geometryHydraulic radiusSlot raise location

Realistic collar locationBlast-hole deviationCommunication betweenengineers

Tatman (2001) Less-than-ideal wallcondition

High powder factor Improperly aligned drill-holes Equipment limitations

Mubita (2005) Inadequate ground condition Poor blasting results Stope boundary inconsis-tenciesInappropriate miningmethods

Poor mining discipline

Stewart (2005) Stress damage pillars Blasting damage Site-specific effectsUndercuttingExtraction sequence

Backfill abutmentDamage to cemented fill

Factors employed in thisstudy

Adjusted Q-rateAverage horizontal to verticalstress ratio

Length of blast-holePowder factorAngle difference betweenhole and wallDiameter of blast-holeSpace and burden ratio

Planned tons of stopeAspect ratioStope breakthrough to anearby drift /stope or not

Indirectly implied blast-holedeviation through theaverage blast-hole length

Table III

Description and summary of the ten UB causative factors

Category Abbr. Unit Range Note

Input Blasting Blen m 0.70 ~ 25.80 Average length of blast-hole(independent variables) Pf Kg/t 0.15~ 3.00 Powder factor

AHW ° 0.00 ~ 170.20 Angle difference between hole and wallBdia mm 76 ~ 89 Diameter of blast-holeSbR (S/B) 0.57 ~ 1.50 Space(S) and burden(B) ratio

Geology AQ - 6.30 ~ 93.30 Adjusted Q rateK (H/V) 1.74 ~ 14.38 Average horizontal (H) to vertical (V) stress ratio

Stope design Pt T 130 ~ 51,450 Tons of stope plannedAsR (W/H) 0.07 ~ 4.17 Aspect ratio (ratio between width (W) and height (H)

of stope)BTBL - Breakthrough (0) ~ Stope breakthrough to a nearby drift/ stope, or not

Blind (1)Output UB % -65.40 ~ 92.00 Percentage of uneven stope break

(dependent variable) (over -and under-break)

and Pt were unsuitable and were removed from the MLRAstepwise model. All of the data from the three mines was used forthe general model, and the R2adj was calculated as 0.171 for boththe MLRA-enter and stepwise models. The K, Hdia, SbR, and Ptwere gradually removed from the MLRA-stepwise model due totheir low F and t values.

Even though the MLRA models showed poor and moderatelylinear relationships with the dependent variable (UB), certainfundamental engineering aspects were elucidated. Using theproposed MLRA models, independent variables such as BTBL, Pf,Hdia, SbR, Pt, AsR, and K showed a positive relationship to theUB. In contrast, Blen, AHW, and AQ had a negative relationshipwith the UB. In MLRA-enter model, some irregular patterns werenoted. For instance, BTBL in Mine A, SbR and AsR in Mine B,and Hdia in Mine C showed a negative relationship; thesepatterns may be due to independent variables that were forced tointo the MLRA-enter models. To consolidate reliable models,these variables were removed from the MLRA-stepwise models.

When multiple regression analysis is applied,heteroskedasticity and multicollinearity problems must beconsidered because they may yield an erroneous conclusion. Inthis study, no suspicious results were found from the MLRA-stepwise and MNRA models through checking the residual plots,variance inflation factor (VIF), and the Durbin-Watson value(Durbin and Watson, 1950).

Multiple nonlinear regression analysisDilution and ore loss are the most complex phenomena inunderground mining production, and they are affected by variousfactors as described previously. The complex phenomena cannotbe fully interpreted using a linear model. Thus, MNRA wasperformed to obtain more significant relationships than from theMLRA. In this study, the twin-logarithmic model was used, andthe following nonlinear relation was assumed:

[2]

where Y denotes the predicted value corresponding to thedependent variables (X1,X2,...Xn), and β0 to βn are theparameters for the nonlinear relationship. Equation [2] can betransformed into a linear domain through log transformation asshown in Equation [3]; thus, the β values can be determinedbased on a multiple linear regression of log(Y) on log(X1,log(X2)...log(Xn) (Cankaya, 2009).

[3]

The dependent variable (UB) has negative values thatexpress the ore loss rate; and two independent variables (BTBLand AHW) contain zero values. The negative and zero valuescannot be applied to the logarithmic function. Hence, thedata-sets must be transformed into positive, real values. For

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Figure 2 – Overview of stope reconciliation with a CMS model

Table IV

Multiple linear regression analysis (MLRA) results for Mine A, Mine-B, Mine C, and the general model

Data-sets Model Equation R R2adj.

Mine A MLRA Enter2 UB = .969 – .018BTBL – .009Blen + .375Pf – .067AHW + .024Hdia + 0.175bR 0.581 0.2801

Stepwise3 UB = 1.088 + .377Pf – .048AHW – .093AQ + .039Pt + .044AsR 0.567 0.2937

Removed variables in stepwise-MLRA K, Blen, Hdia, BTBL, and SbRMine B1 MLRA Enter UB = .651 + .005BTBL – .162Blen + .123Pf – .137AHW – .52SbR – .756AQ 0.590 0.322

0Stepwise UB = .114 – .158Blen – .144AHW – .156AQ + 2.528K 0.584 0.330

Removed variables in stepwise-MLRA BTBL, SbR, Pt, Pf, and AsRMine C MLRA Enter UB = 1.129 + .034BTBL – .125Blen + .140Pf – .138AHW – .26Hdia – .123Sb 0.422 0.166

2Stepwise UB = 1.489 + .33BTBL – .127Blen – .134Pf – .143AHW – .125AQ – .190AsR 0.412 0.163

2Removed variables in stepwise-MLRA K, Hdia, SbR, and Pt

General model MLRA Enter UB = 1.521 + .029BTBL – .119Blen + .123Pf – .152AHW + .013Hdia + .015Sb 0.423 0.1713

Stepwise UB = 1.595 + .031BTBL – .105Blen + .122Pf – .149AHW – .106AQ + .073AsR 0.419 0.1719

Removed variables in Stepwise-MLRA K, Hdia, SbR, and Pt

1 Hdia was removed from the MLRA and MNRA because the Mine B data-set had only one value for this variable. 2 Enter: all independent variables were considered in a single model. 3 Stepwise: a proposed model includes only independent variables that are satisfied with the criteria (F<=0.050).

Unplanned dilution and ore loss prediction in longhole stoping mines

an unbiased transformation, unity was added after the data-sets were normalized between zero and one. Thus, the data-sets yielded values between one and two. MNRA wasaccomplished using the Levenberg-Marquardt iterativeestimation algorithm (Marquardt, 1963). Subsequently,optimal models for Mine A, Mine B, Mine C, and the generalmodel were obtained after eight, twelve, six, and sixiterations, when the residual sum of squares reached 1.0E-008. The MNRA model results are shown in Table V.

The adjusted coefficients of the determinant (R2adj) forMine A, Mine B, Mine C, and the general model were 0.317,0.368, 0.181, and 0.192, which are slightly higher than fromthe MLRA-stepwise models. In fact, the MNRA models werealso insufficient for clarifying the relationship between theUB and given independent variables.

Artificial neural networkAn artificial neural network (ANN) can be defined as aparallel computational inference model whose functionality issimply mimicked by a biological neuron. The fundamentalstructure of an ANN comprises input, hidden, and outputlayers and numbers of a simple mathematical element, theartificial neurons, which are in each layer. The neurons arefully interconnected to each layer, and the connectionintensity is expressed by the weight. The optimum weight ofall connections can be obtained through continuous forwardand backward processes with a certain learning algorithmduring the training stage. Next, a new set of inputs can yielda prediction only through the forward process with theoptimized weight from the previous training. Indeed, the ANNis optimized by simply discovering the optimum weights ofthe model connections. The structure of the multilayer feed-forward ANN used in this study can be seen in Figure 3.

In the proposed ANN model, the conjugate gradientalgorithm (CGA) (Hestenes and Stiefel, 1952) was applied asan learning algorithm of the ANN model and the weights canbe updated as follows:

[4]where w(p+1) indicates the updated weight for p+1 step, g isthe error gradient (g = ∂E(x,w)/∂w), and βp+1 is the conjugate

gradient algorithm constant. In the CGA, the search directionis determined with a conjugate direction that generally yieldsmore rapid convergence than with the steepest descent(Møller, 1993). Various types of combination coefficients (β)were introduced to determine the appropriate conjugatedirection. A well-known algorithm was introduced by Fletcherand Reeves (1964), and the β for p+1 step can be calculatedas follows:

[5]

UB prediction using a conjugate gradient ANNANN performance is greatly affected by the learningalgorithm, transfer function, and ANN architecture. Specialcare must be taken when designing the ANN architecture andchoosing its activation function for computational elements.The hyperbolic tangent (tansig) and log sigmoid (logsig)functions are widely used as nonlinear activation functions.The tansig and logsig output vary between [0, 1] and [-1, 1],respectively. In this study, tansig was used because itshowed better performance than logsig in the previous over-and underbreak prediction study conducted by Jang andTopal (2013). In this context, data-sets were scaled into therange -1 to +1using Equation [6]. After the model estimates

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Table V

Multiple nonlinear regression analysis (MNRA) results for Mine A, Mine B, Mine C, and the general model

Data-sets Model Equation R R2adj.

Mine A MNRA Selected2 UB = 1.407(Pf.306)(AHW–.040)(AQ–.090)(Pt.35)(AsR.042) 0.56 0.3173

Variables removed in MNRA K, Blen, Hdia, BTBL, and SbRMine B1 MLRA Selected UB = 1.669(Blen–.164)(AHW–.162)(AQ–.652)(K1.259) 0.60 0.368

7Variables removed in MNRA BTBL, SbR, Pt, Pf, and AsR

Mine C MLRA Selected UB = 1.455(BTBL.033)(Blen–.129)(Pf.129)(AHW–.126)(AQ–.127)(AsR.167) 0.425 0.1815

Variables removed in MNRA K, Hdia, SbR, and PtGeneral model MLRA Selected UB = 1.473(BTBL.031)(Blen–.116)(Pf.107)(AHW–.144)(AQ–.112)(AsR.079) 0.428 0.192

8Variables removed in MNRA K, Hdia, SbR, and Pt

1 Hdia was removed from the MLRA and MNRA because the Mine B data-set had only one value for this variable. 2 Selected: independent variables were selected from the –MLRA-stepwise model.

Figure 3 – Architecture of the multilayer feed-forward ANN model forthe UB prediction system

the output, the scaled output can be standardized usingEquation [7].

[6]

[7]

Where, x presents values of dependent parameters. xs, xminand xmax indicate normalised, minimum, and maximum valuesrespectively. To consolidate the appropriate ANN architecture,the optimum number of hidden neurons must be defined.Certain empirical suggestions have been introduced, such as byHecht-Nielsen (1987) and Kaastra and Boyd (1996). However,the proper number of hidden neurons may differ in simulationseven for the same problem; thus, so an iterative loop operationwas designed to determine the optimum number of neurons ineach model.

Furthermore, one of the obstacles that may be encounteredduring the training of the ANN model is over-fitting. The fittingmay appear to be excellent during the training stage, but theproposed model cannot predict a correct output for untrainedsamples. In other words, the model is not general. To avoid theover-fitting problem, the cross-validation (Hansen andSalamon, 1990) method was applied. The data-sets used forvalidation are not involved in the training process, and thegeneralization for each training step is cross-checked usinguntrained validation data-sets to prevent the over-fitting. Inthis study, the data-sets were randomly divided into threesubsets; training, validation, and test. 70% of the data-setswere used for training, 15% were assigned to the validation,and 15% were assigned to the test stage. The entire CGA-ANNwas programmed using Matlab code (MathWorks, 2013), andoptimum models for Mine A, Mine B, Mine C, and the generalmodel were generated. The UB prediction model details aredemonstrated in Table VI.

In all models, the training and validation RMSE decreaseddramatically within 30 iterations, and it gradually reducedthrough 100 iterations. The optimum number of hiddenneurons for the ANN model for Mine A, Mine B, Mine C, andthe general model were 7, 16, 16, and 40 respectively. TheRMSE for the training stages was less than 2.36E-2, andstrong positive correlations (R > 0.7) were observed. Figure 4 illustrates test performance graphs of each model.

Considering that UB has been neglected as anunpredictable phenomenon, these results are noteworthy. Theresult will be thoroughly scrutinized for comparison with thecurrent UB prediction results for mines in the following section.

Results and discussion

MLRA, MNRA, and ANN were used to develop the optimumUB prediction model based on 1067 data-sets with ten UBcausative factors from three longhole stoping undergroundmines in Western Australia. The UB prediction model wasestablished not only for each mine, but also for all of themine data-sets. Ultimately, the MLRA, MNRA, and ANNprediction performances were compared with the currentprediction mine results (Figure 5).

As can be seen from Figure 5, the MLRA and MNRAmodels yielded poor and moderate UB prediction perfor-mances. Their correlation coefficients (R) were between0.412 and 0.607. In contrast to multiple regression analyses,the ANN models yielded fair and excellent UB predictionperformances. The correlation coefficients (R) for Mine A,Mine B, and Mine C were 0.944, 0.801, and 0.704, respec-tively. The general model for ANN was trained with 747 data-sets, and 160 untrained data-sets were used to validate thesystem during the training process to prevent over-fitting.Ultimately, the optimized ANN model was established withthe RMSE of 1.90E-2 and tested with 160 untrained data-sets. The correlation coefficient (R) for the test was 0.719.

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Table VI

UB prediction model details for Mine A, Mine B, Mine C, and the general model

Model Number of data-sets Structure of ANN RMSE4 R5

TR1 VA2 TE3 Total Input-hidden-output TR1 VA2 TR1 TE3

Mine A 88 19 19 126 10 – 7 – 1 1.04E-2 6.75E-2 0.92 0.944Mine B 161 35 35 231 10 – 16 – 1 2.15E-2 8.04E-2 0.86 0.801Mine C 496 107 107 710 10 – 16 – 1 2.36E-2 3.19E-2 0.74 0.704General model 747 160 160 1067 10 – 40 – 1 1.90E-2 2.80E-2 0.66 0.719

1TR: training; 2VA: validation; 3TE: test; 4RMSE: root mean square error; 5R:correlation coefficient

Figure 4 – Test performance of UB prediction ANN models for Mine A,Mine B, Mine C, and the general model

Unplanned dilution and ore loss prediction in longhole stoping mines

The proposed ANN engine can be easily applied tounderground stoping mines because the input parameters ofthe proposed ANN are generally collected for each undergroundstope. Engineers can examine potential unplanned dilution andore loss prior to the actual stope development by inputtingthese parameters to the proposed ANN.

ConclusionsNew unplanned dilution and ore loss prediction models wereestablished using multiple linear regression analysis (MLRA),multiple nonlinear regression analysis (MNRA), and anartificial neural network (ANN) method for 1067 historicalstope data-sets collected from three underground longholestoping mines in Western Australia. Models were establishednot only for each mine but also for the data-sets as a wholeto propose a general model for predicting UB. Correlationcoefficients (R) were used to evaluate the MLRA, MNRA, andANN performance; their values for the general model were0.419, 0.438, and 0.719, respectively. Considering thecurrent UB prediction in investigated mines yielded an R of0.088, the ANN model results are significant.

Although the exact causes of unplanned dilution and oreloss are extremely complex, the general trends underlying theUB mechanism were somewhat elucidated using the ANNmodel. The established ANN models were calibrated usinguntrained data-sets during the test stage. Thus, they can bedirectly used as a tool for practical prediction for anyunderground longhole stoping operation. As such, this paperwill create significant benefit for underground stope planningand design.

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Figure 5 – Comparison of UB prediction performances with currentprediction results from mines, MLRA, MNRA, and ANN

IntroductionThe main technologies adopted in the copperand lead reduction industries include theVanyukov, QSL, SKS, Kivcet, Ausmelt, and ISAfurnaces (Hongjiu, 2001; Kojo, Jokilaakso, andHanniala, 2000). From a fundamentaltheoretical viewpoint, all of these technologiescan be classified as reduction bath smeltingfurnaces, which are the major research focusin nonferrous metallurgy. However, some ofthe important physical phenomena andchemical processes inside the furnace remainunknown because of the harsh reactionenvironments. Fortunately, numericalsimulation methods, particularly computationalfluid dynamics (CFD), provide an efficient wayto study their internal processes.

With the development of computersoftware, many good CFD platforms have beenreleased, such as Fluent and CFX. CFD hasbecome an indispensable tool for the designand optimization of complex chemical reactors.Typical applications included the blast furnace(BF) and aluminum reduction cell. In thecopper industry, some papers have beenpublished on numerical studies of the flowpattern. The representative work in this areawas carried out by Valencia and co-workers at

the Institute for Innovation in Mining andMetallurgy, University of Chile ((Vaencia et al.,2004, 2006; Fuentes et al., 2002). Theyconducted numerical and experimental studiesof the fluid dynamics in a Teniente-type copperconverter. A three-dimensional simulation ofthe three-phase system was carried out usingthe volume of fluid (VOF) and the standard k - ε turbulence models implemented in acommercial solver. Their numerical modelincluded the white metal and slag liquidphases, and gas phase through air injectionfrom 50 submerged tuyeres, and experimentalobservations were carried out in a 1:5-scalewater container. The results of these investi-gations enabled the operation conditions to beoptimized. Real (2007) also studied the flowcharacterization of Peirce-Smith copperconverters. Although good results wereobtained from the slice model, unfortunately itcould not provide the entire flow field distri-bution of the furnace. Liow and Gray (1990)experimentally studied the formation ofstanding waves in a water model of a Peirce-Smith converter. The results showed that itwas possible to obtain regions in the bathdepth and tuyere angle/tuyere submergenceplots where no standing waves were foundand spitting was minimal. Kulkarni and Joshi(2005) presented a comprehensive review ofbubble formation and bubble rise velocity ingas-liquid systems. In China, Professor Chi Meiand his group at Central South University (Li,Mei, and Zhang, 2001; Rao, 2010; Li, Chi, andZhang, 2001; Chen, 2002; Mei et al., 2003)have focused mainly on the reaction kinetics,flow field, and industrial experiments on thecopper flash smelting furnace.

Numerical simulation of multiphase flowin a Vanyukov furnaceby H.L. Zhang*†, C.Q. Zhou†, W.U Bing†, and Y.M. Chen*

SynopsisMultiphase flow in the widely used Vanyukov furnace was numericallystudied. An unsteady three-dimensional and three-phase flow model wasfirstly built using the computational fluid dynamics (CFD) software ANSYSFLUENT®, and then solved with the volume of fluid (VOF) and k - ε model.The results showed that the proposed model could be used to predict themultiphase movement, the slag/air fluctuation, the vortex formation, andeffects of structural and operational parameters. By fast Fourier transform(FFT), the dominant frequency of density with time signal was calculatedas 0.29 Hz. The analysis of different injection flow rates of enriched airindicated that this variable has a major effect on the mean slag velocity.The peak mean velocity increased from 2.17 to 4.99 m/s while the flow rateof enriched air varied from 70 to 160 m/s. The proposed model provides amethod to optimize the furnace structure and operating conditions for thebest furnace performance and lowest energy consumption.

KeywordsVanyukov furnace, multiphase flow, numerical simulation, fast Fouriertransform, structure optimization, operation condition optimization.

* School of Metallurgical Science and Engineering,Central South University, China.

† Center for Innovation through Visualization andSimulation (CIVS), Purdue University Calumet,USA.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. Paper receivedMar. 2015 and revised paper received Apr. 2015.

457The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

ISSN:2411-9717/2015/v115/n5/a14http://dx.doi.org/10.17159/2411-9717/2015/v115n5a14

Numerical simulation of multiphase flow in a Vanyukov furnace

These studies have demonstrated that CFD and physicalmodels are very effective ways to study the flow fields andother physical and chemical processes in these furnaces.However, there still remain many problems to be solved. Afew papers have been published on heat and mass transferinside the Vanyukov furnace. V.G. Lisienko presented amodel to predict the behaviour of the furnace duringemergency operation (Lisienko, 1993, Lisienko et al.,2012). Unfortunately, the multiphase flow features of theVanyukov furnace, which could be critical for optimizing theactual operation and furnace design, have attracted littleattention.

The objective of present work is to create a model that canpredict the internal movement, the fluctuations, and thevortex formation in a Vanyukov furnace. The multiphasetheories were first introduced, then an unsteady three-dimensional and three-phase flow model was built in ANSYSFLENT® and calculated by using VOF and the k - ε model.The flow pattern, vortexes formation, and spectrum werethoroughly analysed, and finally the effects of air flow rateswere calculated and analysed.

Methdology and theoryThe flow inside the Vanyukov furnace is a typical complexmultiphase flow. Currently, there are two approaches for thenumerical calculation of multiphase flows: the Euler-Lagrange approach and the Euler-Euler approach. The latterwas adopted in this work. In ANSYS FLUENT, three differentEuler-Euler multiphase models are available: the VOF model,the mixture model, and the Eulerian model. The VOF modelwas used in the current investigation. The theories of VOFand the k - ε model are introduced in the following sections.

VOF modelThe VOF formulation relies on the fact that two or more fluids(or phases) do not interpenetrate. If the αth volume fractionof fluid in the cell is denoted as VFα then the following threeconditions are possible:

VFα=0: there is no fluid α in the cell VFα=1: fluid α fills the cell 0<VFα<1: the cell contains an interface between fluid α

and one or more other fluids.Tracking of the interface(s) between the phases is

accomplished by the solution of a continuity equation for thevolume fraction of one (or more) of the phases. For the αthphase, this equation has the following form:

[1]

where rα is the volume fraction of phase α, ρα is the densityof phase α, Uα is the velocity of phase α, Sα is the sourceterm, mαβ is the mass transfer from phase α to phase β, andmβα is the mass transfer from phase β to phase α.

In the VOF model, only a single momentum equation issolved throughout the domain, and the resulting velocity fieldis shared among the phases. The momentum equation,shown below, is dependent on the volume fractions of allphases through the properties ρ and μ.

[2]

where ρ is density, U is velocity, μ is viscosity, and F is force.For the three-phase system studied in this paper, the

volume-fraction-averaged density and viscosity are calculatedas follows:

[3]

[4]

k - ε modelThe multiphase flow in the Vanyukov furnace should besolved with a fluid-dependent turbulence model. Due to itslow computational cost and good numerical stability, thehomogeneous k - ε turbulence model was applied in thisstudy. The isotropic eddy viscosity (μT) is characterized bythe turbulence kinetic energy (k) and its dissipation rate(ε),which are given by:

[5]

[6]

where, ρm and Um are the mixture density and velocity,respectively, μT,m is the turbulent viscosity, Gk,m is theproduction of turbulence kinetic energy, and σk and σε are theturbulent Prandtl numbers for k and ε, respectively. Theempirical constants appearing in the model are Cε1 =1.44, Cε2=1.92, σk=1.0, and σε=1.3.

Geometry and simulation conditions

Physical modelA typical hypothetical Vanyukov furnace (Figure 1) wascreated using information in the literature (Hongjiu, 2001).There were 10 tuyeres on each side of the furnace to supplyoxygen for the chemical reactions that take place in the slaglayer. The tuyeres were located at the lower side of thefurnace to ensure the high-speed enriched air could agitatethe slag layer and provide enough oxygen for reaction insidethe furnace.

The main dimension parameters of the furnace were asfollows: total length 19 m, total width 2.5 m, height 6 m,exhaust gas tunnel height 9 m, tuyere height 2.5 m, slaglayer depth 3.5 m, metal layer depth 0.9 m, metal outputregion length 2 m, and slag output region length 2 m.

As the objective of this work is to study the multiphaseflow inside the furnace, the following simplifications weremade:

➤ Heat and mass transfer were not considered➤ The furnace structure was simplified, and could be

modified according to the actual furnace dimensions➤ Granular raw material feeding and liquid metal

discharge were not considered.

CFD modellingIn order to carry out the CFD calculation, a multi-purposegeometry containing the fluid phases (exhaust gas, slag, andliquid metal) was created in Pre-Processor of ANSYS®. Thegeometry was then meshed with hexahedral elements. The

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458 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

˙˙

→

→ →

→

3D hexahedral mesh, as shown in Figure 1, consisted ofapproximately 500 000 elements. Since the structure of thisVanyukov furnace is composed of rectangular modules, itcould be meshed with a hexahedral-structured mesh with anexcellent fit.

After the mesh was generated, it was imported intoANSYS CFX® and then read into FLUENT®. Thecorresponding solver-type settings, material properties,boundary conditions, and operation conditions must bespecified properly. The detail settings for FLUENT are shownin Table I. The boundary conditions and material propertiesare shown separately in Table II and Table III.

Simulation procedureEquations [1]–[6] were solved using the commercial solverFluent 14.0. This package is a finite volume solver, usingbody-fitted grids. The pressure-velocity coupling wasobtained using the SIMPLEC algorithm. For the time-dependent VOF and k - ε calculations, the explicit timemarching scheme with small time step △t=1×10-4 s wasadopted. There were 443 264 control volumes, and the meshwas composed of hexahedral mesh elements. Thecomputation of 6.6 seconds of operation of the furnace modelconsumed nearly 72 hours on a DELL® T7400 workstationwith dual Xeon® CPUs (X5492, 3.4 GHz) and 16Gb memoryrunning Windows® 7 Enterprise as operation system. Thisfacility was provided by the Center for Innovation throughVisualization and Simulation (CIVS) at Purdue University,Calumet in the USA.

Results and discussionThe numerical simulation of this unsteady three-dimensionaland three-phase flow can reveal many characteristics thatcannot be measured or observed directly in a runningfurnace, such as the fluctuation of the slag surface in theinjection zone, melt movements in the sedimentation region,and displacement of the slag layer to the sedimentationregion. It is important to find the correct distribution of theflow field, so that the furnace can operate efficiently withproper inlet speed and with proper metal and slag height.

Phase interface configurationDistributions of the transient interfaces of three phases suchas exhaust gas, metal, and slag at t=6.62 s are displayed inFigure 2. The 3D interfaces of overall slag-exhaust gas andslag-metal are presented in Figure 3, where the interfaceevolutions from t=0.29 s to t=6.62 s are listed separately.

Numerical simulation of multiphase flow in a Vanyukov furnace

459The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 ▲

Figure 1 – CFD model of Vanyukov furnace

Table III

Material properties

Item Value

Metal density, kg.m-3 5000Metal viscosity, kg.m-1.s-1 0.004Slag density, kg.m-3 3000Slag viscosity, kg.m-1.s-1 0.012Exhaust gas density, kg.m-3 1.29Exhaust gas viscosity, kg.m-1.s-1 1.52×10-5

Table II

Boundary conditions

Item Value

Enriched air flow rate, m.s-1 70Outlet pressure, Pa -2000Wall treatment Standard wall functions

Table I

FLUENT settings

Solver type Transient

Multi-phase model VOF modelTurbulent model Standard k-ε turbulent modelDiscretization scheme 1st-order upwindSolver method Standard SIMPLE algorithm

Figure 2 – Contours of slag volume fraction at different axial positionswhen t=6 s: (a) X=4.2 m, (b) Y=0.5 m

Numerical simulation of multiphase flow in a Vanyukov furnace

The dynamic pressure of air injected into the furnace ismuch greater than the pressure head due to the depth of slag.Therefore, there is a blow-through distance, which is an airjet termed the ‘gas jet core’ that initially enters the liquid, andbubbles are created in the molten slag as shown in Figure3(a) to Figure 3(c). As the air bubbles impinge the slagintermittently, the wave at the interface of slag-metal andexhaust gas-slag is formed as a result of horizontal jet and

upwelling flow in the air injection. The configuration of theslag surface at the interface between slag and exhaust gas isdisplayed in Figure 3, where the spout peak of combined flowcan be observed. The slag becomes more active above thetuyere position, while it is more quiescent under it. As theflows are unsteady, the configurations of the phase interfaceare transient phenomena. The interface between the slag andmetal is very quiescent, which is beneficial for metal

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460 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 3 – Contours for slag volume fraction of 0.5 in different time steps: (a) t=0.29 s; (b) t=0.55 s; (c) t=0.95 s; (d) t=2.15 s; (e) t=2.95 s; (f) t=4.15 s; (g) t=4.95s; (h) t=6.62 s

separation. It is very important that with this flow field distri-bution, the chemical reaction can proceed to completion in theinjection region, while the metal can settle in the sedimentregion with no stirring.

Velocity field distributionThe velocity vectors and contours at the main section for allzones, including air, metal, and slag phases, are shown inFigures 4, 5, and 6 respectively.

The flow pattern is in accordance with previous results(Hongjiu, 2001). Nevertheless, the flow pattern near the slaglayer is very complex and unstable, and some vortices areobserved. Variations in flow patterns are caused by thedifferences in physical properties of slag, air, and metal.

The enriched air injected from the tuyere into the slaglayer could stirr the slag layer and accelerate the chemicalreaction. The transient maximum values of exhaust gas, slag,and metal are 22.894 m/s, 70.00 m/s, and 0.0555 m/srespectively. Figures 4(f), 5, 6(c), and 6(d) show that thevelocity in the slag settling region is very low. This could bedue to the separate wall blocking the vigorous stirring in theinjection region. The same phenomenon can also be found inthe metal settling region. Therefore, by using this model, theheight, spatial position, and thickness of the separate wallcan be optimized to obtain the best flow pattern in thesettling regions.

The oxygen-enriched air also causes the vortexmovement and fluctuation. The air will push the slag towardsthe centre of the furnace and the upper interface. Severalsmall vortices can be found in slag region, as shown in

Figure 5. It is apparent that the velocity would be signifi-cantly reduced away from the tuyere zone. This is because ofthe huge density difference between the slag and enriched air(nearly 300). From this point of view, the model can also beused to optimize the tuyere structure and its operationalconfigurations, such as tuyere diameter, tuyere angle,number and arrangement of tuyeres, and air flow rates.

Air flow distributionFigure 7 depicts the path lines of the enriched air. Most of theair is injected directly into the slag layer and escapes the fromthe slag-exhaust gas interface in the area located at nearlyone-quarter of the width. This indicates that the current airspeed (or tuyere pressure) and tuyere configuration is notgood enough for the air to penetrate though the slag layer.The air flow above the slag is turbulent, which can be animportant basis for determining the granular raw materialdistribution.

Spectrum analysisWave formation at the slag/exhaust duct interface is anotherimportant factor for slag emulsification. It is known that asthe wave fluctuation becomes stronger, the slag layerbecomes easier to break up and be mixed with the granularraw material. The fluctuation of density with time and powerdensity spectrum with frequency at the selected point P1(X=4m, Y=0.8 m, Z=2.2 m) are shown in Figures 8 and 9 respec-tively. The fluctuation of density indicates that at this pointthe two phases (slag and air) are present in differentinstants. The density first drops to nearly zero in 0.3

Numerical simulation of multiphase flow in a Vanyukov furnace

The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 461 ▲

Figure 4 – Distributions of velocity in X cross-section: (a) X=1.75 m; (b) X=3.15 m; (c) X=8 m; (d) X=9.25 m

Numerical simulation of multiphase flow in a Vanyukov furnace

seconds, then increases to 3000 kg/m3 and begins oscillatingbetween 1500 kg/m3 and 3000 kg/m3. This indicates that theenriched air injected into the slag layer at first creates ahollow near the tuyere area, and then, as shown in Figure 3,stirs the slag layer vigorously. From the fast Fouriertransform (FFT) of this density signal, we obtain thedominant frequency of the density variation, ω= 0.29 Hz, asshown in Figure 9.

Effect of air flow rateSince the viscosity of the slag and the interface tensionbetween slag and air are large, a large momentum is needed

for air to overcome the interface tension and viscous force inthe slag layer. This strong flow, which is directed horizontallyinitially and then upward and sideways, may drag the slaginto the air flow, resulting in vigorous emulsification.Consequently, the effect of air flow rate is of interest. Using

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462 MAY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Figure 8 – Fluctuation of density with the time at point P1: (a) variationof density with the time; (b) position of P1 (X=4 m, Y=0.8 m, Z=2.2 m)

Figure 9 – Variation of power density spectrum of density withfrequency at point P1

Figure 5 – Distribution of velocity in Y cross-section: (a) Y=0 m; (b) Y =0.5 m; (c) Y=1.0 m

Figure 7 – Paths of the enriched air and exhaust duct

Figure 6 – Distribution of velocity in Z cross-section: (a) Z=4.55 m; (b)Z=1.86 m; (c) Z=0.56 m

the same model, the flow fields with air flow velocities of 100 m/s, 130 m/s, and 160 m/s were calculated.

The time-averaged velocities at 13 points along the lineL1(X=4 m and Z=2.2 m), as shown in Figure 8(b), arepresented in Figure 10. The effect of air flow rate on slagvelocity above the tuyere is significant. Along the line fromthe tuyere end to the furnace centre-line, the slag velocity atfirst increases to a peak value and then decreases. Secondly,the peak value positions are moved to the furnace centre withthe increase in air flow rate. The slag velocity increases withincreasing air flow rate. The peak mean velocity changesfrom 2.17 to 4.99 m/s as the flow rate of enriched air variesfrom 70 to 160 m/s.

From this simulation, it can be demonstrated that the airflow rate should have a significant effect on slag emulsifi-cation through the effects of higher slag velocity and higherslag/exhaust duct interface wave frequency. In other words,the efficiency of the desulphurization is enhanced at higherair flow rates.

Future work will focus on the effects of furnace structuraland operational parameters, such as the number of tuyeres,the angle of injection, the heights of the slag and metallayers, the dimension of the furnace, and the inlet of granularraw material. These parameters could all be investigated withthe same model that was used in this work.

ConclusionsThe main contribution of this work is to investigate themultiphase flow behaviour in the Vanyukov process. Thefollowing conclusions drawn from this study can be usefulfor improving furnace design and operation.

➤ When enriched air is injected into the slag layer, a gasplume is formed and bubbles are moved into theexhaust duct. The rising gas bubbles impinge the slagintermittently and break through the slag layer,resulting in splashing. Meanwhile, an unsteady wave isformed at the slag–exhaust duct interface

➤ Significant deformation of the slag layer occurs duringenriched air stirring operation, and the slag becomesmore active above the tuyere. The more complicatedvortices in the slag layer, which are produced as aresult of the different physical properties of the threephases and non-uniform external effects, wereobserved by simulation

➤ The injection flow rate of argon gas has a major effecton the mean slag velocity. The peak mean velocityincreases from 2.17 m/s to 4.99 m/s as the flow rate ofenriched air increases from 70 m/s to 160 m/s. Ahigher efficiency of desulphurization can be achieved athigher air flow rates

➤ The proposed model provides a method to optimizefurnace structural and operational conditions, such asthe number of tuyeres, the angle of injection, theheights of the slag and metal layers, the dimension ofthe furnace, and the inlet of granular raw material.

AcknowledgementThe authors would like to thank the Center for Innovationthrough Visualization and Simulation at Purdue UniversityCalumet for offering this research opportunity and forassistance during the course of this work. The authors arealso grateful for the financial support of the National NaturalScience Foundation of China (51274241, 61321003).

ReferencesCHEN, Z. 2002. Numerical simulation of and on-line monitor of inner hearth-

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LIOW, J.L. and GRAY, N.B. 1990. Slopping resulting from gas injection in aPeirce-Smith converter: water modeling. Metallurgical Transactions B, vol.21B, no. 12. pp. 987–996.

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LISIENKOA, V.G., MALIKOVA, G.K.., MOROZOVA, M.V., BELYAEVB, V.V., and KIRSANOV,V.A. 2012. Modeling heat and mass exchange processes in the Vanyukovfurnace in specific operational conditions. Russian Journal of Non-FerrousMetals, vol. 53, no. 3. pp. 272–278.

MEI, C., XIE, K., CHEN, H., LI, X., CHEN, Z., ZHOU, J., WANG, X., MARARU, T., andZHELING, G.E. 2003. Generating condition and applying results of highefficiency core in copper flash smelting. Nonferrous Metals, vol. 55, no. 4.pp. 85–88. (In Chinese).

RAO, Y.J. 2010. Experimental feeding segregation model of copper flashsmelter. Master's thesis, Central South University. (In Chinese).

REAL, C., HOYOS, L., CERVANTES, F., MIRANDA, R., PALOMAR-PARDAVE, M., BARRON,M., and GONZALEZ, J. 2007. Fluid characterization of copper converters.Mecánica Computacional, vol. XXVI. pp. 311–1323.

VALENCIA, A. ROSALES, M., PAREDES, R., LEON, C., and MOYANO, A. 2006.Numerical and experimental investigation of the fluid dynamics in aTeniente type copper converter. International Communications in Heatand Mass Transfer, vol. 33. pp. 302–310.

VALENCIA, A., PAREDES, R., ROSALES, M., GODOY, E., and ORTEGA, J. 2004. Fluiddynamics of submerged gas injection into liquid in a model of copperconverter. International Communications in Heat and Mass Transfer, vol.31, no. 1. pp. 21–30. ◆

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The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 MAY 2015 463 ▲

Figure 10 – Effect of air flow rate on slag velocity in line L1 (X=4 m, andZ=2.2 m) (t=6.62 s)

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17–18 May 2016 — The SAMREC/SAMVALCompanion Volume ConferenceJohannesburgContact: Yolanda RamokgadiTel: +27 11 834-1273/7Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.zaWebsite: http://www.saimm.co.za

May 2016 — PASTE 2016 International Seminar onPaste and Thickened TailingsKwa-Zulu Natal, South AfricaContact: Raymond van der BergTel: +27 11 834-1273/7Fax: +27 11 838-5923/833-8156E-mail: raymond@saimm.co.zaWebsite: http://www.saimm.co.za

9 –10 June 2016 — 1st International Conference onSolids Handling and ProcessingA Mineral Processing PerspectiveSouth AfricaContact: Raymond van der BergTel: +27 11 834-1273/7Fax: +27 11 838-5923/833-8156E-mail: raymond@saimm.co.zaWebsite: http://www.saimm.co.za

1–3 August 2016 — Hydrometallurgy Conference2016‘Sustainability and the Environment’in collaboration with MinProc and the Western CapeBranchCape TownContact: Raymond van der BergTel: +27 11 834-1273/7Fax: +27 11 838-5923/833-8156E-mail: raymond@saimm.co.zaWebsite: http://www.saimm.co.za

16–19 August 2016 — The Tenth InternationalHeavy Minerals Conference ‘Expanding the horizon’Sun City, South AfricaContact: Camielah JardineTel: +27 11 834-1273/7Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.zaWebsite: http://www.saimm.co.za

INTERNATIONAL ACTIVITIES

▲

xii MAY 2015 The Journal of The Southern African Institute of Mining and Metallurgy

Company AffiliatesThe following organizations have been admitted to the Institute as Company Affiliates

AECOM SA (Pty) Ltd

AEL Mining Services Limited

Air Liquide (PTY) Ltd

AMEC Mining and Metals

AMIRA International Africa (Pty) Ltd

ANDRITZ Delkor(Pty) Ltd

Anglo Operations Ltd

Anglo Platinum Management Services (Pty) Ltd

Anglogold Ashanti Ltd

Atlas Copco Holdings South Africa (Pty) Limited

Aurecon South Africa (Pty) Ltd

Aveng Moolmans (Pty) Ltd

Axis House (Pty) Ltd

Bafokeng Rasimone Platinum Mine

Barloworld Equipment -Mining

BASF Holdings SA (Pty) Ltd

Bateman Minerals and Metals (Pty) Ltd

BCL Limited

Becker Mining (Pty) Ltd

BedRock Mining Support (Pty) Ltd

Bell Equipment Company (Pty) Ltd

BHP Billiton Energy Coal SA Ltd

Blue Cube Systems (Pty) Ltd

Bluhm Burton Engineering (Pty) Ltd

Blyvooruitzicht Gold Mining Company Ltd

BSC Resources

CAE Mining (Pty) Limited

Caledonia Mining Corporation

CDM Group

CGG Services SA

Chamber of Mines

Concor Mining

Concor Technicrete

Council for Geoscience Library

CSIR-Natural Resources and theEnvironment

Department of Water Affairs and Forestry

Deutsche Securities (Pty) Ltd

Digby Wells and Associates

Downer EDI Mining

DRA Mineral Projects (Pty) Ltd

DTP Mining

Duraset

Elbroc Mining Products (Pty) Ltd

Engineering and Project Company Ltd

eThekwini Municipality

Exxaro Coal (Pty) Ltd

Exxaro Resources Limited

Fasken Martineau

FLSmidth Minerals (Pty) Ltd

Fluor Daniel SA (Pty) Ltd

Franki Africa (Pty) Ltd Johannesburg

Fraser Alexander Group

Glencore

Goba (Pty) Ltd

Hall Core Drilling (Pty) Ltd

Hatch (Pty) Ltd

Herrenknecht AG

HPE Hydro Power Equipment (Pty) Ltd

Impala Platinum Limited

IMS Engineering (Pty) Ltd

JENNMAR South Africa

Joy Global Inc. (Africa)

Leco Africa (Pty) Limited

Longyear South Africa (Pty) Ltd

Lonmin Plc

Ludowici Africa

Lull Storm Trading (PTY)Ltd T/A WekabaEngineering

Magnetech (Pty) Ltd

Magotteaux(PTY) LTD

MBE Minerals SA Pty Ltd

MCC Contracts (Pty) Ltd

MDM Technical Africa (Pty) Ltd

Metalock Industrial Services Africa (Pty)Ltd

Metorex Limited

Metso Minerals (South Africa) (Pty) Ltd

Minerals Operations Executive (Pty) Ltd

MineRP Holding (Pty) Ltd

Mintek

MIP Process Technologies

Modular Mining Systems Africa (Pty) Ltd

MSA Group (Pty) Ltd

Multotec (Pty) Ltd

Murray and Roberts Cementation

Nalco Africa (Pty) Ltd

Namakwa Sands (Pty) Ltd

New Concept Mining (Pty) Limited

Northam Platinum Ltd - Zondereinde

Osborn Engineered Products SA (Pty) Ltd

Outotec (RSA) (Proprietary) Limited

PANalytical (Pty) Ltd

Paterson and Cooke Consulting Engineers (Pty) Ltd

Polysius A Division of ThyssenkruppIndustrial Solutions (Pty) Ltd

Precious Metals Refiners

Rand Refinery Limited

Redpath Mining (South Africa) (Pty) Ltd

Rosond (Pty) Ltd

Royal Bafokeng Platinum

Roymec Tecvhnologies (Pty) Ltd

Runge Pincock Minarco Limited

Rustenburg Platinum Mines Limited

SAIEG

Salene Mining (Pty) Ltd

Sandvik Mining and Construction Delmas(Pty) Ltd

Sandvik Mining and Construction RSA(Pty) Ltd

SANIRE

Sasol Mining(Pty) Ltd

Scanmin Africa (Pty) Ltd

Sebilo Resources (Pty) Ltd

SENET

Senmin International (Pty) Ltd

Shaft Sinkers (Pty) Limited

Sibanye Gold (Pty) Ltd

Smec SA

SMS Siemag South Africa (Pty) Ltd

SNC Lavalin (Pty) Ltd

Sound Mining Solutions (Pty) Ltd

SRK Consulting SA (Pty) Ltd

Technology Innovation Agency

Time Mining and Processing (Pty) Ltd

Tomra Sorting Solutions Mining (Pty) Ltd

TWP Projects (Pty) Ltd

Ukwazi Mining Solutions (Pty) Ltd

Umgeni Water

VBKOM Consulting Engineers

Webber Wentzel

Weir Minerals Africa

2015◆ CONFERENCE

Copper Cobalt Africa Incorporating The 8th Southern African Base Metals Conference6–8 July 2015, Zambezi Sun Hotel, Victoria Falls, Livingstone, Zambia

◆ CONFERENCEVirtual Reality and spatial information applications in the mining industry Conference 201515–16 July 2015, University of Pretoria, Pretoria

◆ CONFERENCEMINPROC 2015: Southern African Mineral Beneficiation andMetallurgy Conference6–7 August 2015, Vineyard Hotel, Newlands, Cape Town

◆ CONFERENCEThe Danie Krige Geostatistical Conference 201519–20 August 2015, Crown Plaza, Johannesburg

◆ CONFERENCEMINESafe 2015—Sustaining Zero Harm: Technical Conference andIndustry day26–28 August 2015, Emperors Palace Hotel Casino, Convention Resort,Johannesburg

◆ CONFERENCEWorld Gold Conference 201528 September–2 October 2015, Misty Hills Country Hotel and Conference Centre, Cradle of Humankind, Muldersdrift

◆ SYMPOSIUMInternational Symposium on slope stability in open pit mining and civil engineering12–14– October 2015In association with the Surface Blasting School15–16 October 2015, Cape Town Convention Centre, Cape Town

◆ COLLOQUIUM13th Annual Southern African Student Colloquim 201520 October 2015, Mintek, Randburg, Johannesburg

◆ CONFERENCEYoung Professionals 2015 Conference21–22 October 2015, Mintek, Randburg, Johannesburg

◆ CONFERENCEAMI: Nuclear Materials Development Network Conference28–30 October 2015, Nelson Mandela Metropolitan University, North Campus Conference Centre, Port Elizabeth

◆ SYMPOSIUMMPES 2015: Twenty Third International Symposium on MinePlanning & Equipment Selection8–13 November 2015, Sandton Convention Centre, Johannesburg, South Africa

SAIMM DIARY

Forthcoming SAIMM events...

For further information contact:Conferencing, SAIMM

P O Box 61127, Marshalltown 2107Tel: (011) 834-1273/7

Fax: (011) 833-8156 or (011) 838-5923E-mail: raymond@saimm.co.za

F or the past 120 years, theSouthern African Institute ofMining and Metallurgy, has

promoted technical excellence in theminerals industry. We strive tocontinuously stay at the cutting edgeof new developments in the miningand metallurgy industry. The SAIMMacts as the corporate voice for themining and metallurgy industry in theSouth African economy. We activelyencourage contact and networkingbetween members and thestrengthening of ties. The SAIMMoffers a variety of conferences thatare designed to bring you technicalknowledge and information ofinterest for the good of the industry.Here is a glimpse of the events wehave lined up for 2015. Visit ourwebsite for more information.

Website: http://www.saimm.co.za

EXHIBITS/SPONSORSHIP

Companies wishing to sponsor

and/or exhibit at any of these

events should contact the

conference co-ordinator

as soon as possible

© New Concept Mining 2015Patents Pending

Integrated systems of support

+27 11 494 6000www.ncm.co.za

Applying Poka Yokesin the mining industry