Grinding and Flotation Circuit Integration and

Optimisation

Kym RungeErico Tabosa

Peter Holtham

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Presentation Outline

• Link between Grinding and Flotation

• Bazin Analysis Technique

• 3 Industrial Case Studies

• Conclusions

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PTI Optimisation Consulting ServicesStage versus Overall Efficiencies

3

Drill and Blasting‐ Grade Control

‐ Tonnage

Comminution‐ Energy

‐ Particle Size Distribution

‐ Tonnage

Flotation‐ Grade

‐ Recovery

Optimising each stage in isolation may not result in overall process optimisationThe Process Integration and Optimisation (PIO) methodology optimises each stage in the context of the overall operation

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A relationship is required linking grinding circuit product size to overall flotation recovery

• Flotation recovery is a strong functionof particle size

• Overall flotation recovery andconcentrate grade tends to improve asthe grind size is reduced

• Finer grinding, however, increasesenergy consumption, processing costsand often limits grinding circuitthroughput

Link Between Grinding and Flotation

0102030405060708090100

Recovery (%

)

Size

Recovery loss dueto poor collision efficiency

Recovery loss due to poorliberation

Metso PTI uses a Bazin analysis technique to predict flotation recovery as a function of grind size

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Bazin Analysis AssumptionFundamentals

Bazin et al., 1994

Bazin et al (1994) presented a novel technique for predicting the distribution of metal within a particular grind size distribution

They observed a single relationship between the cumulative percent passing size distribution and cumulative percent passing metal distribution in both laboratory and industrial scale data

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Bazin Analysis AssumptionMetso PTI Observations

Bazin assumption holds as long as• Feed ore composition doesn’t significantly change• Fixed grinding circuit design

Case Study 2

Case Study 3

0

10

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100

0 20 40 60 80 100

Cumulative Mass %

 Passin

g

Cumulative Copper % Passing

January

February

March

April

June

August

September

October

December

0102030405060708090100

0 50 100

Cumulative Mass %

 Passing

Cumulative Copper % Passing

Plant P80=127

Plant P80=139

Plant P80=155

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• Alternative feed size distributions are created for different P80values (JKSimMet, Rosin-Rammler model)

• The metal distribution in the feed is predicted for eachalternative feed size distribution

• The metal distributions of the alternative feed size distributionsare inputted into a flotation model to predict recovery

• Flotation modelling can range from a JKSimFloat sized floatability component model to assuming the size versus recovery relationship remains constant

Bazin Analysis TechniqueThree-step process

1. Prediction of the grinding circuit product size distribution

2. Prediction of metal distribution within size distribution

3. Calculation of overall flotation recovery for each feed

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Analysis Performed

Copper/Gold circuit – Osborne, AustraliaCase Study 1

• Simultaneous flotation and grindingcircuit surveys

• Mass balancing of sizing and assaydata

• Mineralogical analysis of key streams

• Development of sized grinding andflotation models in JKSimMet andJKSimFloat

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Flotation circuit evaluation

0

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Cop

per D

istr

ibut

ion

(%)

Size Fraction

Tailing

0102030405060708090

100

Cop

per R

ecov

ery

(%)

Size Fraction

Concentrate Tail

FeedRougher

Scavenger 1AScavenger 1B

Scavenger 2

Pyrite RougherPyrite Scavenger

Findings

Copper/Gold flotation circuit – Osborne, AustraliaCase Study 1

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Grind Float Optimisation

Findings

Copper/Gold flotation circuit – Osborne, AustraliaCase Study 1

Integrated grinding and flotation modelling can be used to optimise total processrevenue rather than maximise performance in individual process stages

58.5

59.0

59.5

60.0

60.5

61.0

4 5 6 7 8 9 10 11

Nett reven

ue (A

$/t)

Energy consumption (kWhr/t)

240 t/hr265 t/hr290 t/hrBaseline

12000

13000

14000

15000

16000

17000

18000

4 5 6 7 8 9 10 11

Nett reven

ue (A

$/hr)

Energy consumption (kWhr/t)

240 t/hr265 t/hr290 t/hrBaseline

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Case Study 2Copper/gold operation – Australia

• Recoveries were being under-predicted by geometallurgical models

• Grinding circuit throughput and P80 had increased over time and wasinversely correlated with recovery

R² = 0.3142

0 50 100 150 200

Copp

er Recov

ery (%

)

P80 (Micron)

Recovery = ‐0.11 P80 + InterceptR² = 0.239

0 50000 100000 150000Co

pper Recov

ery (%)

Dry Tonnes Milled

Recovery = ‐0.11 Solids + Intercept

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Case Study 2Copper flotation circuit – Australia

• Site had monthly composite data of product streams, suitable for massbalancing on a size by assay basis

The size‐by‐size recovery remained reasonably constant

0

20

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60

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100

0 20 40 60 80 100

Cumulative Mass % Passing

Cumulative Copper % Passing

Plant P80=127 µmPlant P80=139 µmPlant P80=155 µm

Relationship between cumulative mass and metal distribution was similar

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Case Study 2Copper flotation circuit – Australia

0

20

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100

10 100 1000

Cumulative Mass % Passing

Size (µm)

P80 µm1751571461361271121049889

50 100 150 200

Concen

trate Re

covery

Effective Feed P80 (µm)

Recovery = ‐0.09 P80 + Intercept

Different flotation feed size distributions were created

Bazin analysis predicted a 0.9%drop in copper recovery forevery 10 µm increase in feedP80

- 0.9% in Cu Recovery every 10 µm

Drop in recovery due to grind size

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Case Study 3Copper/molybdenum operation

• Metso PTI provided monthly composite data for review with the objectiveof determining opportunities for circuit improvement

• Data suitable for mass balancing on a size by assay basis

Rougher and Scavenger

CleaningFinal Concentrate

Final Tailing

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Case Study 3Copper/molybdenum flotation circuit

The size‐by‐size recovery variable in terms of coarse particles

Large variation in amount of ultrafines in feed

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Case Study 3Copper/molybdenum flotation circuit

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100

0 10 20 30 40 50 60 70

Copp

er Rou

ger Re

covery, %

% of Copper in Feed Ultrafine (25µm)

+106um

+150um

+212um

+295um

Coarse particle recovery proportional to ultrafines in feed

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Case Study 3Copper/molybdenum flotation circuit

Optimum flotation recoveryat P80 65 µm

Grinding finer drops recovery(higher amount of ‐10 µm)

Excessive ultrafines in feed dropsrecovery by 6%

Drop is primarily due to poorcoarse particle recovery

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0 20 40 60

Cu Con

centrate Recovery, %

% of Copper in Feed Ultrafine (25µm)

Coarse Particle Recovery Unchanged

Coares Particle Recovery Variable

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2952121501067553453825‐25

Copp

er Recovery, %

Size (micron)

45

40

39

34

29

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Copp

er Recovery, %

Size (micron)

44

40

39

34

29

Step 1

Step 2

Step 3

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Limitations of Bazin Analysis

• The Bazin assumption no longer holds if there has been a significant change ingrinding circuit design or ore type. Recalibration is required.

• If regrind is used within a flotation circuit, use of an overall size versus recoveryrelationship for overall flotation recovery prediction is not possible. In thesecases, useful insights of the feed grind size effect can be drawn by performinganalysis over the rougher scavenger section of the circuit.

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Conclusions

• Integrated simulation of grinding and flotation circuits can be carried outusing the Bazin assumption. It allows integrated simulation to estimatethe relationship between grind size and flotation circuit performance.Metso PTI has used this approach to:

- Determine the throughput and grind size which optimises total process profitability

- Effect of grind size for use in geometallurgical ore prediction

- Assess impact of grind size parameters (e.g. effect of ultrafines) on overall recovery

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Conclusions

• In its simplest form, only sized and assayed flotation plant feed,concentrate and tailing are required (often readily available)

• Many mining companies routinely collect monthly composite samples ofkey flotation streams. This information is often under-utilised. Bazinanalysis is a tool that can help interpret this information.

• Integrated grinding-flotation simulation is superior to simulation of eachstage in isolation

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Acknowledgements

• Metso PTI and company personnel who were involved in collecting datafor these studies or assisted with the analysis

• Companies involved who gave permission to publish the data

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Process Technology and Innovation

Thank you!

Grinding and Flotation Circuit Integration and OptimisationKym Runge, Erico Tabosa and Peter Holtham

www.metso.com/pti

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