Claudia Velasquez
Transcript of Claudia Velasquez
Geometallurgical Grinding and Flotation Design: Maximizing the use
of available information
Leonardo Flores, Claudia Velásquez, Luis Valencia, Douglas Hatfield, David Hatton
SGS Minerals Services
Geomet Grinding & Flotation Design: SCOPE & AVAILABLE DATA
• World class porhyry copper deposit hosting hypogene Ore to be tested for grinding and flotation amenability, for subsequent concentrator plant design • Comprehensive database of information comprising ore geology, assays, minor elements, mineralogy (QEMSCAN, XRD, NIR), flotation rougher kinetics, MFT rougher kinetics, open cycle cleaner, and hardness / grindability (SPI, Bond Wi, JKDWT, SMC)
SUMMARY OF ASSAY & TESTWORK INFORMATION
Drillhole 15 m composites and related assays & met testwork resultsEXTRACCION PARCIAL 243 CuT, AS Cu, CuCN, Cu Fe3+, FeT,
Fe, ST, S2
Sulphide Mineralogy Spatial Modeling
ICP 360 35 element suite Minor Element Forecasting & Support of Minerals to
Metals Conversion for the MFT Rougher Kinetic Tests
NIR 243 Near Infrared Spectroscopy Alteration Spatial Modeling (phyllosilicates) & Support
flotability modeling
QEMSCAN 162 BMA, PMA Mineralogy Spatial Modeling & Support Minerals to
Metal Conversion for the MFT Rougher Kinetic Tests
Rougher Kinetic Tests Support Open Cycle Cleaner Tests
MFT Rougher Rec Kinetic 47 Rmax, Kave, alpha (per mineral) Kinetics for input into IGS Process Plant Modeling
SPI 386 Hardnesss variability input into IGS for SAG Grinding
Model (combines with Ball Mill Model)
Bond Wi 386 Hardnesss variability input into IGS for Ball Grinding
Model (combines with SAG Mill Model)
SMC 185 Hardnesss variability input into JKSimmet for SAG
Grinding Model
JK DWT 3 Hardnesss input into JKSimmet for SAG Grinding Model
Open Cycle Cleaner 16 Calibrate Regrind effect on Flotation Kinetic changes
• Grinding Circuit Design:
• Base Case and Sensitivity
• Flotation Circuit Design:
• Conceptual and Sensitivity
• Multivariate Data Analysis Seeking Cu Recovery Drivers
CONTENTS
GRINDING CIRCUIT DESIGN: DEFINITION OF BASE CASE &
OPTIMIZATION
GRINDING CIRCUIT DESIGN: BASE CASE & OPTIMIZATION
• Design Criteria •100 kTpd in average (SABC-A)
•P80 at 150 um
•Plant Availability at 95%
•Power Efficiency at 90% SAG and 95% BM
• Sensitivity Analyses • Mill Sizing
• Shell Power
• Base Case Definition
• Base Case Optimization • Tph & Specific Energy Consumption
13 GRINDING CIRCUITS EVALUATION FOR BASE CASE
HARDNESS INFORMATION:SPI VARIABILITY
HARDNESS INFORMATION: MBWI VARIABILITY
SIMULATIONS RESULTS: 13 SCENARIOS
SIMULATIONS RESULTS: 13 SCENARIOS
BASE CASE GRINDING CIRCUIT
OPTIMIZATION SAG POWER SHELL
OPTIMIZATION SAG POWER SHELL
OPTIMIZATION SAG POWER SHELL
OPTIMIZATION SAG POWER SHELL
OPTIMIZATION % BALL LOAD IN SAG
OPTIMIZATION PC80 (PEBBLE CRUSHED)
GRINDING CIRCUIT OPTIMIZED
CONCEPTUAL FLOTATION FLOWSHEET DESIGN
Flotation Design Objectives
• Simulate performance for various flowsheets
• Select the optimal flotation flowsheet
• Select the optimal grind size
• Predict recovery performance for life of mine
Available Data
• Flotation feed kinetics
– Determined from MFTs on core samples (47)
• Effect of regrind on flotation kinetics
– Rougher/cleaner test on composites (16)
• Cell and circuit operating conditions
– From 50 benchmarks of typical copper producers
Flowsheets in IGS
Flowsheets in IGS
IGS Simulations
Typical plant operating conditions from several copper producers in Chile, Peru, USA, Australia
Cleaner Kinetic Tests
Copper grade recovery relationship for one sample under a variety of operating conditions
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80 82 84 86 88 90
Co
pp
er
Gra
de
[%]
Copper Recovery [%]
Genetic algorithm in IGS determines the optimal grade recovery relationship
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80 82 84 86 88 90
Co
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Gra
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[%]
Copper Recovery [%]
Repeated for each sample
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80 82 84 86 88 90 92 94
Co
pp
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Gra
de
[%
]
Copper Recovery [%]
Combined for yearly optimal grade –
recovery relationships
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80 82 84 86 88 90 92
Cu
gra
de
[%
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Cu recovery [%]
Laguna Seca flowsheet annual grade vs recovery
2017 2020 2026 2031 2039
TORONTO
Flowsheet Copper recoveries
compared at 26% grade
85.0
85.5
86.0
86.5
87.0
87.5
88.0
88.5
89.0
IC LC MC 3C 2C111N LS12N 2C111M LS12M 2C112M LS22M
Re
co
ve
ry [%
]
Flowsheet option
10 year cumulative copper recovery
TORONTO
Best flowsheets selected
85.0
85.5
86.0
86.5
87.0
87.5
88.0
88.5
89.0
IC LC MC 3C 2C111N LS12N 2C111M LS12M 2C112M LS22M
Re
co
ve
ry [%
]
Flowsheet option
10 year cumulative copper recovery
Flowsheet recoveries compared in detail at 26% Copper grade for each year
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85
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95
Re
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ve
ry [%
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Year
Annual copper recovery for selected flowsheets
Two column Laguna Seca
TORONTO
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90 R
eco
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[%
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Flowsheet option
Laguna Seca 5 & 10 year cumulative copper recovery grind comparison
5 year 10 year
Comparison of recovery vs grind size at 26% Copper grade
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Re
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[%]
Year
LS12M Annual Copper Recovery
Cu Recovery @ 24% Cu grade Cu Recovery @ 26% Cu grade
Cu Recovery @ 28% Cu grade Cu Recovery @ 30% Cu grade
Production forecast for 25 years at various concentrate grades
MULTIVARIATE DATA ANALYSIS SEEKING Cu RECOVERY DRIVERS
MULTIVARIATE DATA ANALYSIS SEEKING Cu RECOVERY DRIVERS
• Univariate Statistics • Bivariate Statistics • Multivariate Analysis
• Principal Components
• Clusters
• Decision Trees
MULTIVARIATE DATA ANALYSIS SEEKING Cu RECOVERY DRIVERS
• Univariate Statistics •Bivariate Statistics • Multivariate Analysis
• Principal Components
• Clusters
• Decision Trees
•RESULT: Allows for minimizing the overlap between groups to improve their differenciation
Geomet Grinding & Flotation Design: SPATIAL EXPRESSION AND VALIDITY OF Cu RECOVERY DRIVERS
G4: Lito ind - Ortoclasa NIR > 10.5 - Caolinita NIR > 5.5 - % Cpy wt < 0.95 ( Rec_prom_Cu = 73.17 % )
G6: Lito ind - Ortoclasa NIR > 10.5 - Caolinita NIR 5.5_12.5 - % Cpy wt 0.95_2.3 - Albita NIR < 6.5 – Sericita NIR > 22.5 ( Rec_prom_Cu = 78.62 % )
G9: Lito ind - Ortoclasa NIR > 10.5 - Caolinita NIR > 5.5 - % Cpy wt 0.95_2.3 - Albita NIR > 6.5 – Plagiocclasa NIR < 6.5 ( Rec_prom_Cu = 75.14 % )
Conclusions • GRINDING DESIGN:
– Hardness Information SPI & BWI for 476 samples (1 to LOM)
– Target 100,000 TPD & P80 150microns
– 13 Circuits were evaluated (Sizing, # mills, Shell Power)
– Base Case:
• SABC-A: 1 SAG 40’ x 26’ (24 MW shell) , 2 BM 27’ x 45’ (37,5 MW shell)
• CSS 203mm
• 95% availability
• Power efficiency: 90% SAG & 95% BM
• Grate size 76 mm, Screen size 19 mm
• 12 % Ball load in SAG
• PC50 6 mm, PC 80 13 mm
• GRINDING OPTIMIZATION:
– SAG Power shell 24 & 26 MW
– % Ball load in SAG: 10% to 20%, with 16% chosen
– PC80: 13mm & 15mm
Conclusions
• FLOTATION DESIGN:
• A calibrated flotation simulator has been fitted from: flotation feed kinetics, typical plant operating conditions and regrind modifier effects
• Optimum grade recovery curves simulated for each ore type, for each circuit
• Curves combined based on sample representivity to obtain yearly grade recovery curves
• Circuits compared at same grades and best circuit selected
• The effect of grind size evaluated
• From final selection a 25 year production forecast is simulated
• Project turn around time of 6 weeks from test results together with client
Thank You