Geometallurgical Characterisation of Australian
Iron Ores
From Ore to Processed Product
Mark Pownceby | Team Leader - Geometallurgy
15 June 2016
MINERAL RESOURCES
Background
• 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.
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CSIRO’s texture-based Geometallurgy scheme links texture and
mineralogy to ore performance and product quality
Geological textural descriptions are
quantified and linked to metallurgical
performance using:
Mine Development
- geological model
Linking ore texture and
mineralogy to ore
performance and
product quality
- mineralogy
- ore texture
Beneficiation
•
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.
- unit process models
Processing
- unit process models
Process simulation
and product quality
forecast
- sintering, pelletising
Metal Production
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CSIRO ore group classification scheme for Australian iron ores
• 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.
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Sample
Dominant
ore
Hardness
Hematite
Hematite -- goethite
Goethite
Martite
Microplaty
Specular
Hard
Group
1
Medium
Texture
Friable
Group
2
Hard brown
Vitreous
Ochreous
Group
5
Group
6
Group
8
Group
7
Group
3
Group
10
Group
11
Group
4
Powder
Group
9
Goethite %
CSIRO ore group classification scheme (modified after Clout and Manuel, 2015)
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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
Correlating texture and mineralogy to physical properties,
processing and end-product quality
• 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.
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Relative lump metallurgical qualities of common iron ore textural
groups
(Modified from Clout and Manuel, 2015)
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
TI: tumble index; AI: abrasion index; DI: decrepitation index; RDI: reduction degradation index; RI: reducibility index
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Example 1
Lump ore decrepitation
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Relationship between ore group
textures and decrepitation index (DI)
in a single mineralogical classification
category (hematite-goethite). Single
particle DI tests
(After Clout and Manuel, 2015)
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Automated textural characterisation of natural and sintered
iron ores
• 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.
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Automated identification of martite and microplaty hematite
textures using optical image analysis
(modified from Donskoi et al., 2015a)
m: martite
mh: microplaty hematite
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martite
microplaty hematite
Automated identification of SFCA and other phases in crushed
sinter using optical image analysis
(modified from Donskoi et al., 2015a)
Primary hematite
Secondary hematite
Magnetite
SFCA-I
SFCA
Glass
Pores
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Example 2
Modelling tumble index for iron
ore sinter using textural
information
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Comparison of experimental TI data with calculated TI data before
(After Donskoi et al., 2009)
and after including an ore textural factor
TI modelled with textural information for runs
used in modelling
80
80
75
75
MODELLING
MODELLING
TI modelled without textural information for
runs used in modelling
70
65
70
65
60
60
55
55
55
60
65
70
EXPERIMENT
75
80
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55
60
65
70
EXPERIMENT
75
80
Example 3
Influence of mineralogy and
texture on sinter strength, melt
formation and reactivity
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Laboratory scale sinter test methods
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
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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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Laboratory scale sinter test methods
• In situ X-ray Diffraction
In
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Summary
• 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.
• 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.
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Discovery
Pilot
Scale
sintering
CSIRO
Iron Ore
Geometallurgy
Ore
performance
testing
Characterisation
Beneficiation
References
• 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).
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.
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Thank you
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
MINERAL RESOURCES