Xstrata Process Support (XPS) Falconbridge, Ontario COM 2009 Geometallurgy Short Course August 22 23, Peter J. Whittaker, Ph.D., P.Geo.

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1 Xstrata Process Support (XPS) Falconbridge, Ontario COM 2009 Geometallurgy Short Course August 22 23, 2009 Peter J. Whittaker, Ph.D., P.Geo. 1

2 Process Mineralogy at Xstrata Process Support: Outline Introduction Applications and Enabling Concepts Geometallurgy Data Review Sampling Mineralogical Input Metallurgical Input Examples / Case Studies Conclusions 2

3 XPS Process Mineralogy Sampling & Statistics Process Mineralogy 3

4 XPS Process Mineralogy XPS Process Mineralogy is an integrated multidisciplinary 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 4

5 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 5

6 Three Applications Replacement Orebodies at Existing Mines (Brownfields) On-site Support & Plant Optimization ( Statistical Benchmark Surveys, Troubleshooting, Lab Testwork ) New Orebodies, New Concentrators (Exploration, Plant Startup) 6

7 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 Outcome: Accurate Plant Design and Scaleup, Right Capex, No Surprises Methods and Procedures Developed to Support Enabling Technologies at the 95% Confidence Level 7

8 Geometallurgy A Geological and Metallurgical Definition 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 disseminated sulphides net-textured sulphides massive sulphides gabbro footwall 1m 8

9 XPS Flowsheet Development Drill Core Data Review Sample Plan GeoMet Unit Definition PTS or Coarse Composites QEMSCAN MicroProbe Grain Sizes Modal Analysis Mineral Compositions Blend 6 Blend 5 Blend 4 Blend 3b Blend 3a Blend 2 Summary Modal Mineralogy Averaged by Blends 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 Blend 1 Alteration Minerals Other Sulphides Other Silicates 0% 20% 40% 60% 80% 100% Accessory Minerals Other Virtual Flowsheet Results and Review Flotation Testing/DOE/ Optimization Mini Pilot Plant Bankable Design Criteria Grade/Rec for Economics 9

10 Ore Type Assessment Zone Variability Data Review 10

11 Proposed Zones for Geomet Unit Samples E G C N 11

12 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 metallurgical response and can be grouped, or a problematic ore type may be identified and isolated 12

13 Stratified Sampling A wide variance will require a larger sample mass Tighter variance for each unit can be Represented by smaller masses grade A B C Zone X frequency distribution 13

14 Stratified Sampling Sample Preparation Crushing & Blending Plant Features 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

15 Sampling Case Study Powerful Sampling Model By GU Reference Distributions Produces Representative Samples for Testing Spatially Representative Block Model Sample 0.6 Porphyry Cu, Chile Error of <1% on Cu grades gives confidence in samples to be used for metallurgical test work HHG HLG WES SES TUF 15

16 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 sample as 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 16

17 Mineralogical Input Data from automated mineralogy: QEMSCAN, MLA modal % grain size averages and distributions mineral associations mineral liberation texture % Data from quantitative mineral analyses; EPMA mineral compositions, particulary trace concentrations Combined data gives metal deportment to recoverable and non-recoverable minerals, identifies problematic minerals and textures for metallurgy 17

18 Quantitative Mineralogy: Minerals not Assays QEMSCAN & Microprobe 18

19 Impact of Texture on Mill 1.7 mm Particles (+106um) Lost to Tailing 19

20 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. 20

21 Modal mineralogy of gangue as critical as that of pay minerals 21

22 Ni Distribution by Mineral Ni % Distribution Ni (Pn) Ni (Po) Main North Shallow North Deep UM1A Ni in Pn - ~31% Co in Pn - ~2% 22

23 Mineralogical data from testing of deposit variability plus knowledge of current best practice leads to preliminary flowsheet Talc Rougher Copper Rougher Zinc Rougher Primary Grind MS Ball Mill p80 = 45 microns Talc Cleaner Copper 1st Cleaner Copper 1st Cleaner Scav Final Tailings To Final Tailings Copper Regrind Ball Mill p80 = 20 microns Copper 2nd Cleaner Final Tailings Copper 2nd Cleaner Concentrate Zinc 1st Cleaner Scavenger Zinc 1st Cleaner Zinc 1st Clnr Scav Tailings Zinc 1st Clnr Scav Conc Zinc Regrind Ball Mill p80 = 20 microns Zinc 2nd Cleaner Zinc 2nd Cleaner Tailings Zinc 3rd Cleaner Zinc 3rd Cleaner Tailings Zinc 3rd Cleaner Concentrate 23

24 Flotation products test by GU or variability composite for performance diagnostics Strong positive flotation by liberated chalcopyrite Locked Middling Liberated Fraction -420/ / / CS CS CS CS Calc CS Combined % Chalcopyrite Chalcopyrite Liberation in 2nd Cleaner Concentrate Locked Middling Liberated Liberated 5 Middling 0 Calc CS7 CS6 CS5 CS3-4 CS1-2 Size Fraction -106/ / /+212 Locked 24

25 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 25

26 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 ( 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 26

27 Raglan flowsheet developments tied to future ores program 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 NPV $12.7 million in 2002 financial terms 27

28 Montcalm Predicting Startup Timmins 28

29 Montcalm Predicting Startup 120 Comparison of Montcalm Start-up Curve with McNulty Curves Montcalm start-up Type 1 Equal to or better than Type 1 Ni output in Ni and Cu conc,% of design 100 Type 2 Type 3 80 Type Montcalm start-up - October 2004 Quarter after start-up 29

30 Montcalm Scaleup Successful Scale-Up High-Confidence Flotation Testing % Ni Recovery Statistical Benchmark Survey of Operations July % Recovery 75 HCFT Survey Type 1 Startup 30

31 XPS Process Mineralogy Highlights 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 31

32 Mini Pilot Plant Results MPP1 and 2 summary Grade/Recovery Ni % Grade 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 Ni% Recovery 32

33 Conclusions Geometallurgy is a powerful source of information to support Process Mineralogy Variability within a deposit in terms of mineralogy and its effect on metallurgical performance can be quantified Metallurgical risks can be identified and mitigated Flowsheet development time and validation to design criteria and bankable feasibility level can be minimized, this represents large savings for project costs and improved rates of return 33

34 Some of our Clients 34