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

20

22

24

26

28

30

32

34

80 82 84 86 88 90

Co

pp

er

Gra

de

[%]

Copper Recovery [%]

Genetic algorithm in IGS determines the optimal grade recovery relationship

20

22

24

26

28

30

32

34

80 82 84 86 88 90

Co

pp

er

Gra

de

[%]

Copper Recovery [%]

Repeated for each sample

20

22

24

26

28

30

32

34

36

38

40

80 82 84 86 88 90 92 94

Co

pp

er

Gra

de

[%

]

Copper Recovery [%]

Combined for yearly optimal grade –

recovery relationships

20

22

24

26

28

30

32

34

80 82 84 86 88 90 92

Cu

gra

de

[%

]

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

70

75

80

85

90

95

Re

co

ve

ry [%

]

Year

Annual copper recovery for selected flowsheets

Two column Laguna Seca

TORONTO

80

81

82

83

84

85

86

87

88

89

90 R

eco

very

[%

]

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

70

72

74

76

78

80

82

84

86

88

90

92

94

Re

co

very

[%]

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