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Xstrata Process Support (XPS)

Falconbridge, OntarioCOM 2009 Geometallurgy Short Course August 22 – 23, 2009

Peter J. Whittaker, Ph.D., P.Geo.

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Process Mineralogy at Xstrata Process Support: Outline

•Introduction

•Applications and Enabling Concepts

•Geometallurgy•Data Review

•Sampling

•Mineralogical Input•Metallurgical Input

•Examples / Case Studies•Conclusions

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XPS Process Mineralogy

Process

Mineralogy

Sampling & Statistics

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XPS Process Mineralogy

• XPS Process Mineralogy is an integrated multi-disciplinary study of geology, sampling, mineralogy, and

mineral processing that links the orebody to the concentrator process, providing improved mineral separation

• Designs more suitable flowsheets to treat the orebody

more efficiently

The orebody consists of minerals not assays

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Process Engineers &

Geoscientists

• Work closely together as integrated project teams

• Ore mineralogy data feeds into process design

[ can be concentrators, hydrometallurgy, pyrometallurgy, environment ]

• Product mineralogy leads to process optimization

• QA/QC embedded in all procedures

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Three Applications

On-site Support &

Plant Optimization

( Statistical Benchmark Surveys, Troubleshooting, Lab

Testwork )

New Orebodies, New Concentrators

(Exploration, Plant Startup)

Replacement Orebodies

at Existing Mines

(Brownfields)

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Enabling Technologies & Concepts

• Samples not Specimens• Drill-Core Sampling Models• Map Entire Orebody – Life-of-Mine View• Statistical Benchmark Surveying

• Minerals not Assays• QEMSCAN• Probe

• Geometallurgical Unit Definition• High-Confidence Flotation Testing• Distribution Modelling• Mini-Pilot Plant

Methods and Procedures Developed

to Support Enabling Technologies at

the 95% Confidence Level

Outcome:

Accurate Plant Design and Scale-up, Right Capex, No Surprises

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Geometallurgy

Ore type or group of ore types

that possess a unique set of

textural and compositional

properties from which it can be

predicted they will have similar

metallurgical performance

A Geological and Metallurgical Definition

gabbro footwall

massive sulphides

net-textured sulphides

disseminated sulphides

1m

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XPS Flowsheet Development

Drill Core

Data Review

Sample Plan

GeoMet Unit Definition

Virtual Flowsheet

Grain Sizes

Modal Analysis

Mineral Compositions

Summary Modal Mineralogy

Averaged by Blends

0% 20% 40% 60% 80% 100%

Blend 1

Blend 2

Blend 3a

Blend 3b

Blend 4

Blend 5

Blend 6

Pyrite

Chalcopyrite

Chalco_Text

Tennantite/Enargite

Sphalerite

Molybdenite

Quartz

Orthoclase

Plagioclase

Qtz-Plag-Orth/Text

Garnet

Garnet-Textures

Diopside

Amphibole

Muscovite

Biotite

Calcite

Fe Oxides

Alteration Minerals

Other Sulphides

Other Silicates

Accessory Minerals

Other

QEMSCAN

MicroProbe

PTS or

Coarse

Composites

Results

and Review

Mini Pilot Plant

Bankable

Design Criteria

Grade/Rec for

EconomicsFlotation

Testing/DOE/

Optimization

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Ore Type AssessmentZone Variability – Data Review

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Proposed Zones for Geomet Unit Samples

EG

N

C

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Sampling

Aim is to produce a representative sample of a given ore type

Composite samples from drill core enable this

Smaller sample masses can result

Ore type composite – average mineralogy and metallurgical results

for the given ore type

Variability composites – test variability within a given ore type

Outcome can be that different ore types may have similar metallurgicalresponse and can be grouped, or a problematic ore type may be identified

and isolated

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Stratified Sampling

frequency distribution

grade

Zone X

A B

C

A wide variance will require a largersample mass

Tighter variance for each unit can be Represented by smaller masses

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Capable of processing up to 150kg/hr

dry solids

Drillcore or 6 inch rock feed size

Any product (mesh) size down to

1.7mm can be achieved

Revolutionary blending technology

Primary Jaw Crusher

Rocklabs Boyd Crusher

Vibrating screens

Spinning rifflers and load cells ensure

replicate samples are produced

Commissioned October 2008

Cru

shin

g &

Ble

nd

ing

Pla

nt F

eatu

res

Stratified Sampling – Sample Preparation

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Sampling Case Study

•Powerful Sampling Model

By GU

Reference Distributions

•Produces Representative Samples for

Testing

Spatially Representative

Porphyry Cu, Chile

Error of <1% on Cu grades gives confidence in samples to be used for metallurgical test work

0

0.2

0.4

0.6

0.8

1

1.2

HHG HLG WES SES TUF

Block Model

Sample

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Stratified Sampling

•Benefit of stratified sampling applies to smaller

and representative masses

•Mineralogical work is more representative

•Metallurgical test work is tied to the same sampleas mineralogy

•Impact of variability on metallurgical performance

can be tested

•Provides a basis for modeling different blends of mill

feed or different life of mine blends

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Mineralogical Input

Data from automated mineralogy: QEMSCAN, MLA

modal %

grain size averages and distributionsmineral associations

mineral liberation

texture %

Data from quantitative mineral analyses; EPMAmineral compositions, particulary trace concentrations

Combined data gives metal deportment to recoverable and

non-recoverable minerals, identifies problematic minerals and texturesfor metallurgy

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Quantitative Mineralogy: Minerals not Assays

•QEMSCAN & Microprobe

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Impact of Texture on Mill

Particles (+106um)Lost to Tailing

1.7 mm

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Textures can create metallurgical problems –

quantification of textures can become critical

•Mineralogical measurements can be grouped into proportions of textural and/or

mineralogical populations within each composite which may have processing implications.

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Modal mineralogy of gangue as

critical as that of pay minerals

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Ni Distribution by Mineral

0

10

20

30

40

50

60

70

80

90

100

Ni

% D

istr

ibu

tio

n

Ni (Pn) Ni (Po)

Main North Shallow North Deep UM1A

Ni in Pn - ~31%

Co in Pn - ~2%

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Mineralogical data from testing of deposit variability plus knowledge of current best

practice leads to preliminary flowsheet

Zinc 1st Clnr Scav

Tailings

Zinc 1st Clnr

Scav Conc

Copper Rougher

Primary Grind

MS Ball Mill

p80 = 45 microns

Copper Regrind

Ball Mill

p80 = 20 microns

Zinc Rougher

Final Tailings

Zinc Regrind

Ball Mill

p80 = 20 microns

Zinc 1st Cleaner

Zinc 3rd Cleaner

Zinc 1st Cleaner

Scavenger

Zinc 3rd Cleaner

Concentrate

Zinc 2nd Cleaner

Tailings

Copper 2nd Cleaner

Concentrate

Zinc 3rd Cleaner

Tailings

To Final Tailings

Talc Rougher

Talc Cleaner

Copper 1st

CleanerCopper 1st

Cleaner Scav

Copper 2nd

Cleaner

Zinc 2nd Cleaner

Final Tailings

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Flotation products – test by GU or

variability composite for performance diagnostics

-420/+

212

-212/+

106

-106/+

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CS1-2

CS3-4

CS5

CS6

Calc

CS7

Locked

Middling

Liberated

0

5

10

15

20

25

30

35

% C

halc

op

yrit

e

Size Fraction

Chalcopyrite Liberation in 2nd Cleaner Concentrate

Locked

Middling

Liberated

Locked Middling Liberated

Fraction -420/+212 0.03 0.03 0.06

-212/+106 0.15 0.39 3.32

-106/+53 0.24 1.07 18.15

CS1-2 0.30 1.53 30.96

CS3-4 0.22 1.24 20.87

CS5 0.04 0.26 6.72

CS6 0.17 0.96 9.54

Calc CS7 0.09 0.53 3.12

Combined 1.24 6.01 92.75

Strong positive flotation by liberated chalcopyrite

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Bench scale high-confidence flotation

trials test the flowsheet

•Minimises Metal Balance Errors•Develops Reproducible Results•Sampling and Results at 95% Level of Confidence•Quality Control System

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Flow sheet validation is carried out with a mini-pilot plant; smaller masses made possible by geomet units feed the MPP as

representative samples

• Sized for Drill-Core

• Continuous Operation

• Campaign 3-12 days (750-3000

kg ore)

• 10 kg/h nominal feed rate

• 1 Flowsheet/3 day run

• Allows for Testing by GU

• Wide range of Flowsheets

• Produces Design Level

• Mass/value Balance

• Grade/recovery Performance

• 95% Level of Confidence

– Quantitative Quality

Assurance Programme

Ability to Simulate Existing Concentrators Has Been Demonstrated – no need for pilot shaft

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Raglan flowsheet developments tied to future oresprogram on a Geomet foundation with Process Mineralogy test

work validation

• Statistical Benchmark Surveying• Extracts Sample Suite

– at 95% Confidence Level

• QEMSCAN Measurement• Identifies Flowsheet Opportunities

Payback on Overall Investment

92% IRR

Raglan 1998-2002

NPV $12.7 million in 2002 financial terms

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Montcalm – Predicting Startup

Timmins

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Montcalm – Predicting Startup

Comparison of Montcalm Start-up Curve with McNulty Curves

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20

40

60

80

100

120

0 2 4 6 8 10 12 14

Quarter after start-up

Ni

ou

tpu

t in

Ni

an

d C

u c

on

c,%

of

de

sig

n

Type 4

Type 3

Type 2

Type 1 Montcalm start-up

Montcalm start-up - October 2004

Equal to or better than Type 1

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Montcalm Scaleup

•Successful Scale-Up

• High-Confidence Flotation Testing 2001-2003

– 82.9% Ni Recovery

• Statistical Benchmark Survey of Operations July 2005

– 84.0% Recovery

82.984

75

80

85

90

HCFT Survey

Type 1 Startup

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XPS Process MineralogyHighlights of Kabanga Process

Development

• Scoping Phase 1 (2005)

– Ore Characterisation/Geo Met Unit Definition

• Scoping Phase 2 (2006)

– Recovery and Grade Validation/Minor Element and Self Heating assessment

• PFS (2007)

– Flowsheet Optimization

– Reagent/Circuit Configuration DOEs

– Mini Pilot Plant/Design Basis

• FS (2009)

– Self Heating Full Scale Trials

– Water Recycle Impacts

– Final QEMSCAN on Flotation Products

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Mini Pilot Plant Results

MPP1 and 2 summary Grade/Recovery

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16

17

18

19

20

21

22

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60 65 70 75 80 85 90 95

Ni% Recovery

Ni %

Gra

de

MPP 1 Run 2 North

MPP 1 Run 3 North - Tembo

MPP 2 Run 1 North - Tembo

MPP 2 Run 2 North - Tembo - Main

MPP 2 Run 3 Tembo

HCFT North Opt.

HCFT Math. Blend North - Tembo

open sampling data

Tembo HCFT

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Conclusions

•Geometallurgy is a powerful source of information to support Process Mineralogy

•Variability within a deposit in terms of mineralogy andits effect on metallurgical performance can be quantified

•Metallurgical risks can be identified and mitigated

•Flowsheet development time and validation to designcriteria and bankable feasibility level can be minimized, this represents large savings for project costs and improved ratesof return

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Some of our Clients