19TH SAIIE & 35TH ORSSA

ANNUAL CONFERENCE

2005

BUILDING TOWARDS GROWTH AND SUSTAINABILITY IN SA

Emerald Resort, Vanderbijlpark, South Africa 28 - 31 August 2005

19th Annual Conference of the Southern African Institute for Industrial Engineering and the 35th Annual Conference of

the Operations Research Society of

South Africa.

“The fields of Industrial Engineering (IE) and Operations Research (OR) have a lot in common. Amongst other things, both use scientific methods to improve the way in which decisions are made in business and industry, in government and society. This, together with the economies of scale benefits utilised, lead the Vaal Triangle Chapter of the Operations Research Society of South Africa (ORSSA) and the Southern African Institute for Industrial Engineering (SAIIE) to host a joint conference”.

Optimisation of the Mittal Steel SA

metallurgical supply chain using linear

and mixed integer programming.

By L.F. Scheepers(*) – LSLPS lou@global.co.za (www.lslps.com) P Olivier - Mittal Steel South Africa Limited Pieter.Olivier@mittalsteel.com (www.mittalco.com) R.A. Featherstone - LSLPS robertjo@absamail.co.za (www.lslps.com)

Abstract During 1998, analysis of the available models serving Top Management in the field of optimised strategic planning and decision support, revealed that there was in fact a fundamental lack of support for the Decision Makers of the then Iscor (now Mittal Steel SA). This led to the development of a comprehensive optimisation model of the integrated mining and iron-and-steel making complexes with the objective of maximising the NPV of the group cash flow before tax. Although the Sishen and Thabazimbi iron ore mines as well as the Grootegeluk, Tshikondeni and Leeuwpan coal mines are included in this model, other mining complexes may be included generically by the user in the input data. The model includes both the Newcastle and the Vanderbijlpark Iron and Steel Works and interactions between them. Model techno-economics consist of the steel markets (prices and volumes per generic steel product within region within continent) and the various production units: - sinter, DRI, coke batteries, blast furnaces, EAF, BOF, casting and the mills. The system is driven by the coke CSR and the sinter percentage in the blast furnace feed, subject to detailed and calibrated metallurgical, thermodynamic, slag chemistry and stoichiometric balances. Fixed, capex and opex costs are included. The matrix dimension of a single time period model is 2553 variables (300 binaries) in 2761 constraints. The data entry vehicle is user-friendly Excel spreadsheets defining various classes and tables. Matrix generation, optimisation and report generation is conducted in less than 2 minutes. The model was used to demonstrate to Management the considerable costs locked up in sub-optimised planning and to optimise the Group’s long-term iron ore procurement strategy.

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1. Introduction The “Iscorians” responsible for the introduction of this technology to Iscor in 1998 were Mr. Zach de Beer and Ms. Rae Loader (now with Kumba Resources Ltd.). The Project Leader is Mr. Pieter Olivier, the manager of Mittal Steel SA’s Raw Material Development and Optimisation Department. The model development team members are Robert Featherstone, Cecile Bruwer and Lourens Scheepers. (Pieter is an industrial engineer, Robert a pyro-metallurgical engineer, Cecile a mathematical programmer and Lourens an OR practitioner. In addition, many technical and financial staff members of Mittal Steel SA contributed over the years. Messrs. Herman Scheepers and Thys Naude from the Vanderbijlpark and Newcastle Works respectively, justify special reference for their contributions). During 1998, analysis of the available models serving Top Management in the field of optimised strategic planning and decision support, revealed that there was in fact a fundamental lack of support for the Decision Makers of the then Iscor (now Mittal Steel SA). The main findings of this analysis were :-

• No applications existed to support decision making on a strategic level.

• Several corporate models were in the process of being developed to support decision making. • Only one optimisation model was being planned for decision support on a strategic level.

The result of this analysis led to the development of the comprehensive optimisation model of the integrated mining and iron-and-steel making complexes with the objective of maximisation of the NPV of the group cash flow before tax. The principle adopted throughout has been the “holistic optimisation” approach rather than the “integration of individually optimised plants”, thereby eliminating sub-optimisation and avoiding the subjectivity of input-output models such as spreadsheet calculated plans. “The whole is more than the sum of its parts – Aristotle”. [1]. In contrast to the holistic optimisation approach, the literature displays abundant references to the optimisation of individual plants and processes, of which the work of the IBM Steel Industry Solutions Group, is typical of many. [2]. The iron and steel manufacturing complexes that are modelled are the Vanderbijlpark Works and the Newcastle Works. The mines are the Sishen Iron Ore Mine, Thabazimbi Iron Ore Mine, the Grootegeluk Coal Mine, Tshikondeni Coal Mine and the Leeuwpan Coal Mine. Fluxes are available from nearby sources and coking coals can be sourced locally or imported from abroad.

2. Salient Features of the Model

2.1 User-friendliness. Data input by the end-user is in Excel spreadsheet Classes and Tables. Matrix and report generation code is developed in Haverly Inc.’s OMNI+ code linked with the H/Cplex or H/Xpress optimisation packages. [3].

2.2 Generics. The user can enter as many pre-coded generic production entities

in the input as is required, i.e. any number of:-

2.2.1 iron ore mines / external iron ores 2.2.2 PCI coal mines / external PCI coals 2.2.3 coking coal mines / external coking coals 2.2.4 external coke types 2.2.5 sinter plant inputs e.g. fluxes, reductants and waste materials such as

mill scale and blast furnace off-gas particulates 2.2.6 other raw materials, e.g. fluxes, sinters, injected oils/tars and injected

gases, etc. 2.2.7 final steel sales products 2.2.8 time periods for multi-time horizon planning

The matrix generation is totally data driven on the above generic items and the user is not at all involved in matrix mathematics manipulations.

2.3 Plants/processes incorporated

2.3.1 Sinter Plant - with both belt protection and blast furnace pre-screen ore recycle streams. In order to permit a convergence of the recycle streams’ chemical analyses to within a pre-specified tolerance of the main sinter product stream’s analysis, recursive linear programming techiques are employed with excellent results. Alkali removal is included. The carbon requirement inputs are based on empirically determined figures and typical actual values, as no heat balance is included. A detailed stoichiometric balance is included.

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2.3.2 DRI (Direct Reduced Iron) - which is in direct competition with iron

and steel scrap purchased from external sources. This activity’s inputs are dependent on a number of empirically determined relationships and typical actual values. No heat balance is included but there is a stoichiometric balance of the most important chemical species.

2.3.3 Coke Batteries - taking full account of the coking coals’ qualities

blended to required coke specifications, in particular the empirical CSR (cold strength after reaction – ISO standard NSC test) index. The slag balance in the blast furnace includes the effects of the coke ash content on slag composition and volume.

2.3.4 Blast Furnace - with full stoichiometric, metallurgical, slag chemistry

and heat balance and their interrelationships. In particular, the empirically determined relationships between the percentage sinter in the blast furnace burden and HM (hot metal) productivity as well as the effect of the coke CSR index on HM productivity are modelled. The shaft and hearth zone reactions are modelled as well as a full carbon balance including the Boudouard reaction. [4]. Contaminants such as sulphur and phosphorous are modelled using empirical partition factors.

2.3.5 BOF (Basic Oxygen Furnace) - with full stoichiometric, metallurgical,

slag chemistry and heat balance and their interrelationships as well as oxygen blowing and effects thereof on levels of desulphurisation and iron losses in the form of vapour. Exothermic silicon to silica reactions are included. Trimming additives (FeMn, SiMn, FeSi, to mention a few) are included.

2.3.6 EAF (Electric Arc Furnace) - with full stoichiometric, metallurgical,

slag chemistry and heat balance and their interrelationships.

2.3.7 Continuous casting.

2.3.8 Rolling Mills - flat products (Vanderbijlpark) and profile products (Newcastle) are considered together with scrap generation.

2.3.9 Iron/steel scrap balance

2.4 Logistics. Intermediate and final product stockpiles are included. Byproducts

and final product sales volumes on an ex Works basis are also included. 2.5 Costs. The following cost types are included :-

2.5.1 Raw materials on a delivered basis 2.5.2 Variable production costs 2.5.3 Fixed costs 2.5.4 Ongoing capital expenditure (grouped with fixed costs)

2.6 Revenue. Selling prices and exchange rates are included in the input data.

2.7 Objective function. The objective function is the maximisation of NPV (net

present value), i.e. the maximisation of the discounted difference between escalated revenues and escalated costs.

3. Graphical Display of the Model Flows

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4. “Steady State” Model Assumption It is important to note that the model is a “steady state” model, defined over a predetermined period of time, as opposed to a dynamic or chemical kinetic reaction model. It is far from the purpose of this application and neither has the classic linear programming approach the ability to deliver a moment to moment prediction of the pyro-metallurgical and thermodynamic process behaviour inside the operating plants of large integrated iron and steel complexes. The request by the Decision Makers and Planners of Mittal Steel SA’s business operations, is to provide a tool that will :-

4.1 limit “quick and dirty” planning assumptions by means of a comprehensive structure which will eliminate “short-cuts” and “not-thought-of” errors

4.2 ensure a maximum NPV plan within well calibrated and known constraints

4.3 provide “value in use” functionality – objective and unbiased answers on the

relationships of quantity and quality versus price on an holistic basis The model optimally allocates the chemical species ex raw materials to final products over successive pre-determined time periods (months, quarters, years), subject to: -

• iron and steel pyro-metallurgical stoichiometry • metallurgical balances • slag chemistry • heat balance • interaction of these

5. Model Accuracy and Calibration

Prior to any optimisation runs being conducted, the model is calibrated to match practice within a high degree of accuracy. Tests used are:-

5.1 Historical data tests where actual inputs to the plants over a selected production period are force-fed into the model and model results are then compared to the corresponding actual production reports

5.2 Reasonability tests where experts inspect scenario inputs and outputs and

then give their opinions as to whether model outcomes are reasonable

While these testing phases may be time consuming, experience has, however, shown them to be valuable confidence builders. In the case of unacceptable deviations from the above test guidelines, a number of corrective “handles” have been built into the model. In general, these “handles” are input factors under user control for manipulating metallurgical and heat balance efficiencies. For example, carbon in the blast furnace is used to create the heat levels required to reach the temperatures to smelt the ores as well as to act as a reductant to release the metal from the ores. A wrong coke rate can be adjusted to come closer to the actual coke consumption rate by adjusting the carbon efficiency factor. This may cause an over or under supply of heat which can be adjusted by manipulating the heat loss factor. In order to establish a well calibrated model, it is very important for the user to have an intimate knowledge of the plant and processes involved.

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6. The Two Major Value Drivers: Coke CSR Index and Percentage Sinter in the Blast Furnace Feed.

6.1 Coke CSR Index. Empirical relationships exist between CSR and blast furnace coke consumption rates as well as productivity in HM (hot metal). In short, the higher the CSR, the better the “carbon efficiency” of the furnace, the more ore and sinter can be charged and smelted resulting in higher volumes of HM produced. Reduced coke consumption follows; cheaper reductant consumption such as PCI can be increased with a net saving on overall costs. [5]

In the Mittal Steel SA model, these relationships are formulated as integer variables, each denoting a point on the CSR-PCI-curve. The user defines the CSR index range and number of points on the curve and the model will then procure and blend coking coals from its list of coking coals (containing the available quantities, chemical analysis and costs), to a coke CSR value that will yield optimal NPV for the entire system.

6.2 Percentage Sinter in the Blast Furnace Feed. Sinter in this context refers to

agglomerated ore that includes pellets or other forms of agglomerated iron ore fines. Empirical relationships exist between the sinter percentage in the blast furnace charge and blast furnace coke consumption rates as well as HM productivity rate. The higher the percentage sinter, the better the “carbon efficiency” of the furnace; the more ore and sinter can be charged and smelted, resulting in higher volumes of HM produced and reduced coke consumption rates. [6].

As in the case of CSR, these relationships are formulated as integer variables, each denoting a point on the sinter percentage curve. The user defines the sinter percentage range and number of points on the curve and the model will then produce (and/or procure) sinter(s) to a volume and chemical specification that will again yield the optimal NPV for the entire system. As an example, the model will source sinter ore within the permitted constraints that will result in a sinter that will optimally meet the demands of the blast furnace in techno-economic equilibrium with the ores and other raw materials available.

6.3 CSR combined with Percentage Sinter. Combined formulations in one matrix results in a powerful value driver and the case study below illustrates the immense ability of this technique to unlock value within a large integrated mining and iron and steel manufacturing complex.

6.4 Economics dictate Views. It is important to note that the above philosophies

and related methodologies are not universally true. In countries where low ash coal is delivered cheaply to the Works, CSR is ignored and techniques such as “stamp-charging” the coke batteries become the norm. Such a plant is TISCO (Tata Iron and Steel Company in Jamshedpur, India). [7]. Slag volumes are higher and HM productivity lower, but due to the cheaply available low ash coals, it is more economical to “stamp-charge” than to import and blend high cost high CSR index coking coals. This reasoning ignores other aspects of coke making economics such as coke battery life and pollution.

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7. Case Study. One of the first runs of the model was to illustrate that value can be unlocked, at least on paper, by avoiding sub-optimisation in the planning. Two optimisation runs were conducted: - The base case run, depicting the present annual production budget at the time. Inherent in this plan was the in-built stipulation of the BOF manager’s strict maximum sulphur specification on hot metal receipts from the blast furnace. In his turn, the blast furnace manager prescribed strict maximum sulphur specifications on the receipts from the sinter plant and coke batteries. This again led to strict low sulphur in coal procurement policies by the coke plant manager. At all the transfer points mentioned, i.e. from imported and inter-plant, the input data was generated so as to reflect the required maximum sulphur specifications as prescribed by the relevant managers. In the case one run, maximum sulphur specification was only defined at the point of transfer from the BOF to the continuous caster, i.e. to ensure final sales products are within specification. The assumption was that the qualities of the final sales products in the two cases had to be identical. All other maximum sulphur specification points were left open between 0% and 100%. The case one run was then conducted with the following outcomes:-

7.1 A cheap high sulphur coking coal mix was selected 7.2 This caused the sulphur in the hot metal to be “unacceptably” high as seen

by the blast furnace and BOF Managers

7.3 This high sulphur content hot metal entering the BOF, required severe and expensive oxygen blowing resulting in an “unacceptable” 4% iron mass vapour loss. This in turn had a back-lash in that, upstream, more raw materials had to be procured and processed in order to make up for the 4% loss, i.e. in order to equal the base case steel sales.

7.4 The difference in NPV between the two cases was R450 million in favour of

case one. (±3% Of annual turnover).

7.5 Conclusion : the savings in procuring and processing the cheaper high sulphur coking coal mix far outweighed the higher cost associated with the 4% iron vapour loss option.

8. Other Applications. The Mittal Steel SA model has been used in many situations for many applications, such as:-

8.1 Evaluation and price/volume break-even studies for the Moatize coking coal reserves in neighbouring Mozambique

8.2 Optimisation of Mittal Steel SA’s long term iron ore procurement strategy 8.3 Studying the techno-economics of the sinter return stream magnitudes with the

extreme being a zero return using ceramic grate bars 8.4 Studying the balance between the manufacturing and selling of lower grade

market coke as opposed to manufacturing coke for the blast furnace in conjunction with imports of Chinese coke.

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8.5 Capital expenditure motivational studies for the substitution of injected oxyfuels by PCI

8.6 Studying the techno-economic competition between the “electric steel” and

“oxygen steel” manufacturing routes with special reference to scrap availability and delivered prices of steel scrap

8.7 Optimised annual production budgets 8.8 Effects of DRI metallisation levels on the “electric steel” manufacturing process 8.9 Effect on profitability of processing Steel Works wastes, e.g. the use of oily mill

scale as a sinter plant feed and the determination of a new optimal feed mix.

9. Concluding Remarks. Large scale mathematical programming models such as the linear and mixed integer programming model approach to Mittal Steel SA’s operations have reached maturity levels where effective decision support can be rendered. It must be stressed however that the quality of such decision support is a direct function of Management’s belief in this type of advanced planning system. It is also dependent upon the effort put into managing the model and its environment by Management such as data collection, calibration and communication to the decision makers - not only on communicating optimal plans and strategies, but even more importantly, to understanding their decision making problems with the view to translating them into formats which are solvable by the model.

10. References.

[1] “System Analysis Techniques”. J Daniel Couger and Robert W Knapp. p9 & p28. John Wiley & Sons Inc 1974. ISBN 0-471-17735-0. [2] http://www.research.ibm.com/pdos/ [3] Haverly Inc. of New Jersey USA. http://www.haverly.com [4] “Mineral and Metal Extraction An Overview”/ p277 – p280. L.C. Woolacott and R.H. Eric. The South African Institute of Mining and Metallurgy. Monograph Series M8. 1994. ISBN 1-874832-42-0. [5] “Study of Gasification Reaction of Cokes Excavated from Pilot Blast Furnace”. SCANMET 2. 2Nd International Conference on Process Development in Iron and Steelmaking. 6-9 June 2004. Lulea Sweden. V Sahajwalla; T Hilding; S Gupta; B Björkman; R Sakurovs; M Grigore & N Saha-Chaudhury. http://www.lkab.se/pdf/pdf_papers/2004_Study_of_Gasification_Reactions_of_Cokes.pdf [6] “Principles of Blast Furnace Iron Making Theory and Practice”. p395 – p398. Anil K. Biswas. Cootha Publishing House. Brisbane Australia.1981. ISBN 0-949917- 00-1. [7] “Meeting Customer’s demands for Quality Coking Coal” by D.C. D’Souza and A.K. Bhatnagar. p83 – p90. Tata Steel, West Bokaro Division – 825 314, India. TATA SEARCH 2001. ISSN 0971-5975.

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