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“MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS
PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION”
Dr Eugene Donskoi - Project Leader, Process Modelling - Downstream Processing Performance
12 September 2016
Carbon Steel Futures group/Resources, Community and Environment Program/ CSIRO Mineral Resources
• Textural Characterisation
Chemical assay
Mineral composition
Textural composition
Texture – microstructure which relates both the spatial distribution of different minerals and porosity, and the spatial distribution of different grains and crystals
H = hematite (martite), HH = hydrohematite,vG = vitreous goethite, oG = ochreous goethite,K = kaolinite, P = pore, E = epoxy resin
WHY ?
1. Ore texture is important for prediction of downstream processing
2. Particles within the same textural group have analogous properties
• Reflected Light Microscopy1. Capability to reliably identify different iron oxides
and hydroxides2. Higher resolution, faster and cheaper than QEM
SCAN and Raman spectrography
• Increased productivity (up to 10 times higher than manual point counting)
• Higher level of characterisation (quality and amount of information, consistency)
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Examples of four hematitic particles exhibiting different textural types ranging from dense to highly porous
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
These different textures have different characteristics in terms of hardness, attrition
resistance, moisture absorption, and will behave differently during comminution and
beneficiation processes as well as granulation and sintering
Comparison of modelled and experimental TI values for runs included in the modelling
a) modelling without textural information (SD = 1.53, R-Sq = 75.6%
TI without textural information for runs
used in modelling
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60
65
70
75
80
55 60 65 70 75 80
EXPERIMENT
MO
DE
LL
ING
TI with textural information for runs used in
modelling
55
60
65
70
75
80
55 60 65 70 75 80
EXPERIMENT
MO
DE
LL
ING
(b) modelling with textural information (SD = 1.13, R-Sq = 86.9% )
Not a single outlier has been removed (either from the modelling set or the verification set) !!!
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
CSIRO OPTICAL IMAGE ANALYSIS (OIA) PACKAGE (‘MINERAL4’ and ‘RECOGNITION4’)
• Initially created for iron ore and now successfully used for other minerals
• Allows automatic, semi-automatic and manual acquisition and comprehensive processing of sets of images
• Works for any particle size including lumps
• Allows users to build their own iron ore classification scheme and to perform automated iron ore and gangue texture classification
• Imports images in all major formats including output from QEMSCAN and MLA
• performs calculation of mineral composition, chemical assay, density, dimensional or textural characteristics for every particle section, liberation class and ore texture class or any particle group based on specific mineral, dimensional, textural or chemical criteria
• performs calculation of mineral liberation, iron liberation and mineral association characteristics for any group of particles
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Parameters //Part Type Hardness
No. of
Part Freq
% Area
% Wt % Mean Fe Tot
Mean SG
Mean Mineral
Area (µm2)
Mean Shape Fact
Mean Elong
Shale-Kaolinitic Soft 52 0.43 0.36 0.18 10.02 2.11 3757 4.15 1.7
Microplaty hematite Medium 1016 8.39 8.37 8.05 69.05 4.15 4485.3 3.22 1.76
Dense hematite V.Hard 5143 42.45 43.87 48.93 69.2 4.81 4643.1 2.47 1.75
Vitr.-Ochr. goethite Soft 1097 9.05 8.66 6.66 58.42 3.32 4294.5 3.08 1.78
Martite-Goethite Hard 1022 8.44 8.7 9.34 67.96 4.64 4631.6 2.59 1.78
Martite-Goethite Medium 1158 9.56 9.83 9.41 67.19 4.13 4622.2 3.09 1.73
Goethite-Martite Soft 404 3.33 3.29 2.56 61.66 3.36 4429.8 3.28 1.72
Goethite-Martite Hard 858 7.08 6.97 6.52 63.53 4.04 4424.2 2.92 1.76
Ore texture group characterisation (example of output to Microsoft Word)
This tables includes the following parameters grouped by iron ore texture type: the number of particles, frequency %, area % and weight % of this ore texture group, total iron and specific gravity with coefficients of variation (standard deviation/mean), mean area with coefficient of variation, mean shape factor and elongation, percentages of each mineral within each class by area or by weight, and the percentage of pores by area.
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Part Type / Mineral
Keno-Magnetite
(Wt)
Hematite
(Wt)
Hydro-Hematite
(Wt)
Vitreous Goethite
(Wt)
Ochreous Goethite
(Wt) Kaolinite
(Wt)
Quartz
(Wt)
Pores %
(Area)
Shale-Kaolinitic 0.0876 1.83 0 5.31 9.65 80.12 2.99 23.08
Microplaty hematite 0.0971 97.92 0.0569 1.26 0.49 0.13 0.0405 18.11
Dense hematite 1.57 97.02 0.15 0.85 0.36 0.0493 0.0033 5.27
Vitr.-Ochr. goethite 0.053 1.39 0.0408 75.65 21.3 1.53 0.0262 17.56
Martite-Goethite 0.93 84.88 0.59 11.65 1.86 0.0573 0.026 6.24
Martite-Goethite 0.58 79.19 0.19 15.86 3.94 0.22 0.0265 14.84
Goethite-Martite 0.37 34.07 0.18 45.94 18 1.41 0.0403 21.28
Goethite-Martite 1.18 38.97 0.61 53.08 5.97 0.13 0.0493 10.9
Sample of Brockman ore with some addition of martite/kenomagnetite ore
corresponding mineral mapOptical photomicrograph
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Sample of Marra Mamba ore
corresponding mineral mapOptical photomicrograph
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Identification of martite and microplaty hematite using textural identification
Original reflected light photomicrograph Mineral map (martite - yellow, microplatyhematite - magenta)
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Automated identification of primary and secondary hematite
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Problem – the same mineral, so similar reflectivitySolution – segmentation by textural identification
primary hematite – light blue, secondary hematite – dark blue, magnetite –magenta, SFCA – green, glass – cyan, porosity and epoxy – yellow
Automated identification of Fibrous, Columnar SFCA and Dense SFCA(silicoferrite of calcium and aluminium)
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Problem – the same mineral, so similar reflectivitySolution – segmentation by textural identification
primary hematite – light blue, secondary hematite – dark blue, magnetite – magenta, fibrous SFCA – light green, Columnar SFCA – cyan (relatively large elongated grains, prismatic structure), glass – dark green, porosity and epoxy – yellow
a)
Comparison of sinter phase quantification by manual point counting (PC) and automated optical image analysis (OI) techniques
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
0
10
20
30
40
50
60
70
80
90
100
Blend1 -PC
Blend1 -OI
Blend2 -PC
Blend2 -OI
Are
a %
glass
dicalcium silicate (larnite)
dense SFCA
prismatic SFCA
micro-platy SFCA
magnetite
secondary hematite
primary hematite
Automatic Identification of Inert Maceral Derived Components (IMDC) from Reactive Maceral Derived Components (RMDC) in Coke
Currently there are three methods used for IMDC identification :
1) bulk identification of IMDC2) small size porosity IMDC
identification3) Identification of “washed out” areas
Magenta – IMDC, blue – RMDC, yellow - porosity
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Textural identification of IMDC based on identification of small size porosityMap of fine porosity
IMDC areas obtained based on map of fine porosity
Photomicrograph of coke
Porosity map
Final IMDC map from fine porosity method
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Segmentation of IMDC boundary layersInitial photomicrograph of coke Identified IMDCs and RMDCs
IMDCs only with allocated boundary layers
The wall abundance (RMDC) in the IMDC boundary area is 7.4%, while the overall abundance of RMDC in the sample is 23.6%)
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
The change of RMDC abundance and thickness in IMDC boundary area depending on IMDC area.
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
20
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80
1 10 100 1000
RM
DC
ab
un
da
nce
Area /10000 (µm2)
RMDC abundance in IMDC boundary
Coke1
Coke 2
Coke3
Coke 4
0
5
10
15
20
25
30
35
40
1 10 100 1000
RM
DC
th
ick
ne
ss
Area /10000 (µm2)
RMDC thickness in IMDC boundary
Coke1
Coke 2
Coke3
Coke 4
Correlations between parameters characterising parent coal composition, coke structure and measured coke properties.
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
6055504540
90
80
70
60
50
40
Porosity
Vit
rin
ite
12
11
10
9
8
76
5
4
3
2
1
Scatterplot of Vitrinite vs Porosity
0.00240.00220.00200.00180.00160.00140.00120.0010
80
70
60
50
40
Specific Neck Thickness
Vit
rin
ite
12 9
8
76
5
4
3
2
1
Scatterplot of Vitrinite vs Specific Neck Thickness
250002000015000100005000
65
60
55
50
45
IMDC Boundary Length
MC
S
Scatterplot of MCS vs IMDC Boundary Length
MCS - arithmetic mean coke size in mm. determined by hand sizing over 125.0, 106.0, 75.0, 50.0, 25.0 and 13.2 mm square mesh screensAREA_1 – average pore area, ModWT- modified wall thickness, ECD-equivalent circle diameter
700000600000500000400000300000200000
66
65
64
63
62
61
60
59
AREA_1
AS
TM
Har
dn
ess
12
9
7
6
5
4
3
2
1
Scatterplot of ASTM Hardness vs AREA_1
0.0550.0500.0450.0400.0350.030
66
65
64
63
62
61
60
59
Ratio of ModWT and ECD
AS
TM
Har
dn
ess
12
9
7
6
5
4
3
2
1
Scatterplot of ASTM Hardness vs Ratio of ModWT and ECD
50454035302520
65
60
55
50
45
Percent of Nodes Area
MC
S
12
9
8
7
65
4
3
2 1
Scatterplot of MCS vs Percent of Nodes Area
Dependence between coke strength indices and cumulative amount of IMDC
ACARP Project C25048 - kickoff meeting | Eugene Donskoi
-1
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
154 217 307 435 615 869 1229 1738 2458
Co
rre
lati
on
Co
eff
icie
nt
IMDC ECD (µm)
Correlation Coefficients between M40 and Cumulative amount of IMDC down to certain size fraction
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
154 217 307 435 615 869 1229 1738 2458
Co
rre
lati
on
Co
eff
icie
nt
IMDC ECD (µm)
Correlation Coefficients between I40 and Cumulative amount of IMDC down to certain size fraction
Dependence of correlation coefficient of M40(AS1038) and I40 on cumulative amount of IMDC down to a specific size of IMDC expressed in equivalent circle diameter (ECD).
Large Object Characterization: Pellets, Lumps, Pebbles - Magnetite pellet heated to 620OC
Ø 12.7 mm, compound image made of 18 x 18 2 x 2 MosaiX imagesclear hematite presence in outer layers
Individual image:
2 x 2 MosaiX at magnification x100
magnetite – pinkhematite – whiteporosity – dark
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Large Object Characterization: Pellets, Lumps, Pebbles -Magnetite pellet heated to 620OC
compound mineral map
clear hematite presence in outer layers
magnetitehematitefluxporosity
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Magnetite pellet heated to 620OCMineral/porosity distributions
magnetite hematite porosity
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Graph of average hematite, magnetite, and porosity abundances as a function of distance from pellet centre.
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Conclusions• Iron ore textural information is important for prediction of downstream
processes.
• Textural identification in Mineral4 allows identification of different morphologies of the same mineral in iron ore and sinter.
• Textural identification allows differentiation of IMDC and RMDC in cokes and comprehensive characterisation of coke structure.
• Unique features and capabilities created in CSIRO’s Mineral4/Recognition4 software allow comprehensive and, in some cases, unique characterisation of important features relevant to ironmaking researchers and industry plant operators.
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Thank you
Eugene Donskoi - Project Leader, Process Modelling - Prediction of Downstream Processing Performance
Phone: +61 7 3327 4158
Email: [email protected]
Carbon Steel Futures group/Resources, Community and Environment Program/CSIRO Mineral Resources
Optical Image Analysis Advantages and Drawbacks (compared with SEM methods)
ADVANTAGES
• Fast and cheap
• High resolution
• Reliable identification of minerals with close chemical compositions (like hematite, hydrohematite, magnetite)
• Porosity identification
DRAWBACKS
• No simultaneous chemical composition
• Possible misidentifications
• Problems with identification of non-opaque minerals
within epoxy blocks
COMMON PROBLEMS
• Representability of the studied sample
• Stereological error
• Touching particles or particle sections separated by cracks or pores.
Donskoi E., Manuel J. R., Austin P., Poliakov A., Peterson M. J., and Hapugoda S. Comparative Study of Iron Ore Characterisation
by Optical Image Analysis and QEMSCANTM”, Iron Ore 2011, Perth, WA, Australia, pp 213-222
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Average presence of iron ore minerals in different layers of thin 1mm settled sample in epoxy polished block.
0
10
20
30
40
50
60
Hem
atite
Hydro
-H
em
atite
Vitr
eous
Goeth
ite
Ochre
ous
Goeth
ite
Kaolin
ite
Quart
z
Pore
s
% in
blo
ck l
ayer
Mineral
Layers Top to Bottom-150+106 µm fraction
Top
Middle
Bottom
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Possible sectioning of non-liberated particles during block polishing
a)
b)
132
Side view
Plan view
a) sectioning of three similar two-mineral particles - side view; b) corresponding particle sections: – two particles appear as fully liberated and only the one in the middle is not liberated
Lin, D, Gomes C O, and Finch J A, 1995, Comparison of stereological correction procedures for liberation measurements, Transactions of the Institutionof Mining and Metallurgy, 104:C155-C161.
•Dependence from texture.•Iron ore much more complex
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Unique feature - Automatic identification of non-opaque minerals based on relief
(a)
(b)
(c)
(a) Ore photomicrograph, (b) Image enhanced for quartz identification, (c) Coloured mineral map (Q = Quartz)
Q
Q
Q
Q
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi
Applications of “Mineral4/Recognition4”OIA of iron ore sinter
Determination of coal maceral composition
OIA of manganese sinter
Manganese ore analysis
MINERAL4/RECOGNITION4: A UNIVERSAL OPTICAL IMAGE ANALYSIS PACKAGE FOR IRON ORE, SINTER AND COKE CHARACTERIZATION | Presenter - Dr. Eugene Donskoi