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CSIRO MINERAL RESOURCES FLAGSHIP

Iztok Livk MATLAB Tour, Perth, 12 August 2014

Mathematics Improving Mineral Processing Efficiency

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Australian Minerals Research Centre

Perth (Waterford)

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DIGITAL PRODUCTIVITY & SERVICES

ENERGY

BIOSECURITY

CSIRO Research Flagships

OCEANS AND ATMOSPHERE

FOOD, HEALTH & BIO-PRODUCTS

AGRICULTURE MINERAL RESOURCES

FUTURE MANUFACTURING

LAND & WATER

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Presentation Outline

1. Modelling and Simulation of a

Gibbsite Crystallisation Circuit

2. A New Technique for Quantifying

Particle Breakage Behaviour

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Australian Alumina Production

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Gibbsite Crystallisation - defining the product

• Generating solids from clear solution

• Process productivity

• Product quality, chemical purity

Gibbsite Crystallisers

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A Simplified Gibbsite Crystallisation Circuit

Gibbsite Crystals

Fine

See

d

Coa

rse

See

d

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Gibbsite Crystallisation in the Plant

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Modelling the Crystallisation Process

mm(a)

Agglomeration Crystal Growth

Nucleation

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A Single Crystalliser Model - standalone

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Gibbsite Crystallisation Circuit - SIMULINK

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Alumina and Solids Concentrations across the Circuit

Yield= 90.27 g/L

Alumina concentration Solids concentration

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PSDs in Different Streams

S

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Dynamic response of the crystallisation circuit

Increased Fines Generation

14

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Last Crystalliser Dynamic Response

Alumina concentration: Last Crystalliser

0 10 20 30 40 50 60560

580

600

620

640

660

10th

tank

sol

ids

conc

entra

tion,

g/L

Time, day

NormalIncreased Nucleation

Solids concentration: Last Crystalliser

0 10 20 30 40 50 6068.5

69

69.5

70

10th

tank

Al 2O

3 con

cent

ratio

n , g

/L

Time, day

NormalIncreased Nucleation

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Seed Recycle Dynamic Response

Fine Seed Coarse Seed

0 10 20 30 40 50 602.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

Fine

See

d ra

te, k

g/s

Time, day

NormalIncreased Nucleation

0 10 20 30 40 50 6060

62

64

66

68

70

72

74

76

Coar

se S

eed

rate

, kg/

s

Time, day

NormalIncreased Nucleation

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Production Rate Dynamic Response

Leads to severe

plant instabilities –

model based

compensation

required

0 10 20 30 40 50 6017.2

17.4

17.6

17.8

18

18.2

Pro

duct

rate

, kg/

s

Time, day

NormalIncreased Nucleation

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Alumina Calcination

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Alumina Particles - agglomerates (a) (b)

• What is the strength of these particles? • How do they break? • What effect does the production process have on their breakage?

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Quantifying Particle Strength and Breakage Mechanism

jS

Breakage

Parent Daughters

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Population Balance Breakage Model Data

Parent Size, l

Dau

ghte

r Siz

e, v

Toughness, t

• One cube of data for each time instant (4-D double)

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Breakage Mechanism Identification Software

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Calcined Particle Breakage - animation

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Breakage Maps - animation

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Breakage Map of Sample A - cleavage

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Breakage Map of Sample B - attrition

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Conclusions

• Extremely flexible tools for developing customised applications

• Deployment of developed applications using standalone or

dynamically linked libraries

• Developments used to facilitating improvements in multi-billion

dollar minerals processing industries

• Allowing for further integration of developed applications, e.g.

process optimisation and control

• MathWorks tools used in the examples presented: èMATLAB èMATLAB Compiler èOptimisation Toolbox è SIMULINK

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Acknowledgement Andrey Bekker, CSIRO Neil Francis, CSIRO

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CSIRO Mineral Resources Flagship Iztok Livk t +61 8 9334 8902 e iztok.livk@csiro.au w www.csiro.au/MDU

Thank you