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Geometallurgical Characterisation of Australian Iron Ores From Ore to Processed Product

MINERAL RESOURCES

Mark Pownceby | Team Leader - Geometallurgy 15 June 2016

• Consistency is more important than absolute quality for blast furnace operation. • Consistent blast furnace feed quality cannot be maintained on the basis of chemistry alone. • Iron ore shipments with chemistry within specification may have different metallurgical performance due to differences in ore types. • The mineralogy may be relatively simple but the textures are often complex and influence downstream processing response. • Consideration of ore texture is therefore particularly important in iron ore production.

• Lump iron ore (31.5-6.3 mm) fetches a premium price over fines (<6.3 mm) • Quality lump ore resources are gradually being depleted. • Sintered fines now make up more that 60% (often 80%) of the ferrous burden in the blast furnace. • Lump-fines ratio is an important parameter for mine planning and direct shipping ore (DSO) quality control.

• There is a need to maintain sinter quality despite declining ore grades. • Characterisation of sinter and its properties is important for correlating with blend characteristics and predicting how ore blends will perform.

A Geometallurgical framework has been developed to improve deposit characterisation, mine planning and operation, and design of sinter blends to produce products with consistent chemical and

metallurgical performance.

Geometallurgical Characterisation of Australian Iron Ores | Mark Pownceby

Background

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CSIRO’s texture-based Geometallurgy scheme links texture and mineralogy to ore performance and product quality

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Mine Development- geological model

- mineralogy- ore texture

Beneficiation- unit process models

Processing- unit process models- sintering, pelletising

Metal Production

Linking ore texture and mineralogy to ore performance and product quality

Process simulation and product quality forecast

Geological textural descriptions are quantified and linked to metallurgical performance using:

• A robust textural classification scheme suitable

for unprocessed iron ore bulk materials.

• Reliable methods to acquire and quantify textural information.

• Industry standard test procedures to evaluate performance.

• Tools to simulate ore performance at each stage of processing and forecast product quality.

• Important textural characteristics of Australian iron ores: • Grain size and shape, e.g. granulation of martite vs. microplaty hematite. • Porosity affects strength and ‘relative physical hardness’ (e.g. lump yield), density (e.g. gravity

classification) and moisture retention (e.g. dewatering).

• A tool for predicting the metallurgical response of lump and fine ore by defining textural groups • Correlated with properties and process parameters used to evaluate process behaviour and product quality.

• The classification scheme applies mainly to BIF-derived ores (particularly BID

ore) but it can be adapted to the more uniform CID and DID ores.


CSIRO ore group classification scheme for Australian iron ores

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Geometallurgical Characterisation of Australian Iron Ores | Mark Pownceby 5 |

Group Description

Group 1 Hard dense hematite

Group 2 Moderately dense hematite-martite to powdery “blue dust”

Group 3 Moderately dense martite-goethite

Group 4 Porous goethite-martite

Group 5 Hard dense hematite-hydrohematite

Group 6 Dense martite-goethite

Group 7 Dense goethite-martite

Group 8 Microplaty hematite-goethite

Group 9 Highly microporous ochreous goethite

Group 10 Moderately porous goethite

Group 11 Dense vitreous goethite

CSIRO ore group classification scheme (modified after Clout and Manuel, 2015)

Group 2

Group 1 Group

5 Group

6

Group 8 Group

3

Group 7

Group 4

Group 1 1

Group 9

Group 1 0

Hematite -- goethite Hematite Goethite

Goethite %

Hard

Medium

Friable

Powder

Dominant

ore

Hardness

Texture

Sample

Martite Microplaty Specular

Hard brown Vitreous Ochreous


Correlating texture and mineralogy to physical properties, processing and end-product quality

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• Representative textural groups have been sampled and tested quantitatively from a wide variety of Australian iron ore deposits in the Pilbara, Yilgarn, and Gawler Cratons. • Hand specimens, large sorted lump, blasthole cone chips, pit samples, DSO, blast furnace lump and sinter fines.

• Petrological characterisation of lump specimens and polished sections.

• Chemical assay.

• Strength and handling: drop tests, uniaxial compressive strength, impact crushability, Bond abrasion index, friction tests.

• Lump burden behaviour: tumble index (TI) and abrasion index (AI) (ISO 3271), decrepitation index (DI) (ISO 8371), reduction degradation index (RDI) (ISO 4696) and reducibility index (RI) (ISO 7215).

• Sinter assimilation tests of cores drilled from hand specimens and sinter fines.

Ore group Porosity TI AI DI RDI RI 1. Dense martite-hematite Very low High Low Low Low Medium 2. Microplaty hematite Medium-high Low-medium High-medium Very low Medium-high High 3. Martite-goethite Medium Medium Medium Medium Medium Medium 4. Goethite-martite High Low-medium High-medium Medium Medium High

5. Dense hematite / martite / hydrohematite

Low Very high Low Very high Low Medium

6. Dense martite-goethite Low High Low High Medium Medium 7. Dense goethite-martite Medium Medium Medium High Medium Medium 8. Microplaty hematite-goethite Medium Medium Medium Medium High High 9. Brown goethite Low-medium Medium Medium Low-medium Low High 10. Ochreous goethite Very high Low High Low High Very high 11. Vitreous goethite Medium Medium Medium Medium Medium High


Relative lump metallurgical qualities of common iron ore textural groups

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TI: tumble index; AI: abrasion index; DI: decrepitation index; RDI: reduction degradation index; RI: reducibility index

(Modified from Clout and Manuel, 2015)

Example 1 Lump ore decrepitation



Relationship between ore group textures and decrepitation index (DI) in a single mineralogical classification category (hematite-goethite). Single particle DI tests

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(After Clout and Manuel, 2015)

• Various analytical techniques (QEMScan, MLA, EPMA, XRD) are available for quantitative chemical and mineralogical classification of iron ore and their processed components.

- many lack features for deriving detailed quantitative textural information.

• CSIRO’s preferred technique is point counting or optical image analysis (OIA) using

reflected light microscopy.

• Purpose-built software has been developed for automated OIA of natural and

sintered iron ores, which can segment and classify grains or particles by texture group as well as performing traditional liberation and mineral association analysis.


Automated textural characterisation of natural and sintered iron ores

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Automated identification of martite and microplaty hematite textures using optical image analysis

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m: martite mh: microplaty hematite martite microplaty hematite

(modified from Donskoi et al., 2015a)


Automated identification of SFCA and other phases in crushed sinter using optical image analysis

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Primary hematite Secondary hematite

Magnetite

SFCA-I

SFCA

Glass

Pores

(modified from Donskoi et al., 2015a)

Example 2 Modelling tumble index for iron ore sinter using textural information



Comparison of experimental TI data with calculated TI data before and after including an ore textural factor

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55

60

65

70

75

80

55 60 65 70 75 80

MO

DEL

LIN

G

EXPERIMENT

TI modelled without textural information for runs used in modelling

55

60

65

70

75

80

55 60 65 70 75 80

MO

DEL

LIN

G

EXPERIMENT

TI modelled with textural information for runs used in modelling

(After Donskoi et al., 2009)

Example 3 Influence of mineralogy and texture on sinter strength, melt formation and reactivity



Rapid determination of the relative sintering performance of ore blends faster and at much lower cost than at plant or pilot scale. This approach enables: • isolation of individual ore behaviour within a sinter blend • determination of matrix strength • characterisation of nucleus stability • determination of fundamental behaviour of ore types - linked with CSIRO’s ore characterisation scheme • direct correlation with pot-grate sintering

Laboratory scale sinter test methods

• Matrix reactivity testing • -1 mm sample material compressed into a compact and fired • mechanical strength (TI) test. • strength achieved when TI>80% (=65% pot grate TI)

• Nucleus assimilation testing to examine relative reactivity of particles from particular ore types

• - 6 mm cores in typical sinter blend matrix, fired under a typical sinter heating profile and gas composition

a) Group 6 dense martite-goethite, b) Group 9 microporous ochreous goethite, c) Group 3 moderately porous martite-goethite.



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In • In situ X-ray Diffraction

• The Geometallurgical approach for iron ore developed by CSIRO involves linking mineral and texture information to forecast key metallurgical properties from the mine through to the blast furnace.


Summary

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CSIRO Iron Ore

Geometallurgy

• A practical quantifiable ore classification that includes

mineralogical and textural information, which can be applied at macroscopic and microscopic scales in the mine and in the laboratory.

• Automated microscopic textural classification of iron ore and sinter.

• Correlating classification groups to physical properties, processing response and end-product quality through standard laboratory tests.

• Ongoing development of models that incorporate all of the above aspects with the aim of predicting sinter properties from the ore.

Discovery

Characterisation

Beneficiation Ore

performance testing

Pilot Scale

sintering

• Clout, J M F and Manuel, J R, 2015. Mineralogical, chemical, and physical characteristics of iron ore, in Iron Ore. Mineralogy, Processing and Environmental Sustainability (ed: L Lu), pp 45-80 (Woodhead Publishing: Cambridge).

• Donskoi, E, Manuel, J R, Lu, L, Holmes, R J, Poliakov, A and Raynlyn, T, 2009. Advances in mathematical modelling of sintering performance of iron ore fines, in Iron Ore Conference 2009, pp 341-350 (The Australasian Institute of Mining and Metallurgy: Perth).

• Donskoi, E, Hapugoda, S, Lu, L, Peterson, M and Haileslassie, A, 2015a. Advances in optical image analysis of iron ore sinter, in Proceedings Iron Ore 2015, pp 543-548 (The Australasian Institute of Mining and Metallurgy: Melbourne).


References

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Acknowledgements • The paper co-authors: Steve Suthers, James Manuel, Natalie Ware, Eugene Donskoi and Andrei Poliakov.

• Dr John Clout for his substantial contributions to the application of iron ore texture for predicting downstream processing.

Mineral Resources Dr Mark Pownceby Team Leader – Iron Ore Geometallurgy t +61 3 9545 8820 e [email protected] w http://www.csiro.au/en/Research/MRF

Thank you

MINERAL RESOURCES

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