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Innovation en géométallurgie et
comminution
Frédéric Couët, ing., Ph. D.
© COREM (2018)
© COREM (2018)
Comminution Crushing and grinding
2
Energy bandwidth study (DOE, 2010)
4%
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Geometallurgy Spatial model and process model
3
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COREM
© COREM (2018) From Metso.com
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Drop weight test
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GeM Comminution index
Geometallurgical mapping
7
Limitations
Low correlation with A x b
Variable feeding
Large variations in shapes
Closed side setting (CSS)
drift
Use of a power meter to
quantify the energy
consumed in crushing
Kojovic et al. (2010) XXV International mineral processing congress.
© COREM (2018)
AG/SAG mill testing Procedures available to characterize grindability
8
Verret, F.-O., Chiasson, G. & McKen, A., 2011. SAG mill testing - An overview of the test procedures available to characterize ore grindability. SGS.
❶
❷ (SVT)
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SAGDesign SAG variability test (SVT)
9
Uses a bench scale mill
similar to SAG mill
operation.
Is similar to Bond
methodology.
Gives SAG grindability
in kWh/t.
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Crusher indicator versus SVT Set-up and methodology
10
10
❶ ❷
Some samples were tested at two different closed size settings.
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Linear regression model SVT ~ b0 + b1 F70 + b2 P70 + b3 P70 Em
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(5 times 5-fold cross-validation)
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Repeatability 16 repetitions on a reference material
12
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Robustness Closed side setting drift
13
❶
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Effect of iron grade on rock hardness
14
Composition is correlated to Sag Variability Tests
for drill cores.
𝑊𝑜𝑖 ↑ ~𝐼𝑟𝑜𝑛% ↓
Pérez-Barnuevo L. et al. (2016) IMPC, Paper # 304 -
1.0
2.0
3.0
4.0
5.0
58.0 60.0 62.0 64.0 66.0 68.0
SVT
(kw
h/t
)
%FeT
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Characterization A typical characterization campaign scenario
15
50: SVT (±10%)
500: Crusher index (±17%)
All: Chemical analysis
Models
– SVT ~ Crusher index (with energy)
– SVT ~ Crusher index (without energy)
– SVT ~ Chemical analysis
Grindability for all samples but with different
confidence intervals
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Sequential spatial sampling
SVT Crusher indicator
Couet, F. et al., 2015. A new methodology for geometallurgical mapping of ore hardness. Proceedings of SAG conference, Vancouver.
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Real-time control and reconciliation Online hardness indicator at plant scale
17
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Segmentation of bimodal size
distributions
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3D vision system Plant data acquisition and analysis
Estimate the conveyor
geometry.
Measure the speed of
the conveyor (real-time).
© COREM (2018) 20
3D vision system Plant data acquisition and analysis
Region of interest is
discretized in bands.
Compute minimum of each
band to estimate the
amount of fines.
Make an histogram of
everything above the
minimum to describe the
shape of size distribution.
Calibrate α to estimate how
much coarse particles float
on fines.
© COREM (2018) 21
3D vision system Plant data acquisition and analysis
Use volume and speed
to estimate the volume
flow rate.
Possibility to estimate
bulk density and iron
ore content.
R² = 0.964
0
500
1000
1500
2000
2500
3000
3500
0 1000 2000 3000
Ton
nag
e f
rom
cam
era
(t/
h)
Nuclear scale (t/h)
Tonnage validation (AMEM)
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3D vision system
Plant data acquisition and analysis
© COREM (2018)
Real-time control and reconciliation Online hardness indicator at plant scale
23
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Real-time control and reconciliation
Reconciliation of the block model
24
Wambeke, T. & Benndorf, J., 2017. Mathematical Geosciences, 49(1).
Information from the
concentrator is used
to update locally the
block model.
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Traceability – Silo model To
ns
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Traceability – From crusher to AG mill
26
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Traceability – From mine to AG mill
27
corem.qc.ca
SAGTools
© COREM (2018)
What is SAGTools?
Mill filling to optimize throughput
29
Powell & Mainza (2006) Extended grinding curves are essential to the comparison of milling performance.
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Charge volume soft sensor
30
Austin SAG Model
https://fredcou.shinyapps.io/demo_power_model/
F(DEM) SAG Tools
Calibration process
© COREM (2018) 31
Pilot plant mill instrumentation
Installation of 3D cameras in a 6’x2’ AG/SAG mill
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What is SAG Tools? Discrete element method
Quebec Mine 2015
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What is SAG Tools? Inputs and outputs
33
AG or SAG
PLC
SAG Tools
Web browser
Inputs
Power
RPM
% solids
Pressure (overload model)
Tonnage (wear model)
Geometry, media, wear, etc.
Outputs
Charge volume
Overload risk
Media wear
Liners wear
Angles
© COREM (2018)
What is SAG Tools?
User interface
34
Shell feed Shell middle Shell discharge
Quebec Mine 2015
corem.qc.ca
Mineralogy
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Mineralogical tools
36
Lévesque, S. et al., 2016. Mineralogical tools for ore characterization – key data at all steps of iron ore concentration. IMPC.
© COREM (2018)
Bulk mineralogical analyser
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Bulk mineral analyzer
38
IMPC 2016
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Drill core textures
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Drill core textures
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Drill core textures
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Geometallurgy Spatial model and process model
42
© COREM (2018)
Forward
Integrated - Horizontally (Mine-to-mill) - Vertically (Variable resolution) Native support - Multicomponent - Optimization - Machine learning - Stochastic Mineralogy
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