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Quantification of Uncertainty of Geometallurgical Variables in Mine Planning Optimisation Exequiel Sepulveda School of Civil, Environmental and Mining Engineering Supervisors: Peter Dowd and Chaoshui Xu

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Presentation at Universidad of Adelaide for international PhD students

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Page 1: Quantification of Uncertainty of Geometallurgical Variables in Mine

Quantification of Uncertainty of Geometallurgical Variables in Mine Planning Optimisation

Exequiel Sepulveda

School of Civil, Environmental and Mining Engineering

Supervisors: Peter Dowd and Chaoshui Xu

Page 2: Quantification of Uncertainty of Geometallurgical Variables in Mine

Outline

BackgroundLiterature reviewGapsAimsConclusion

Page 3: Quantification of Uncertainty of Geometallurgical Variables in Mine

Problem

Main cause of mine project’s failure:Real production is far of estimated production

Diagnostic:Unrealistic mine planning

Sources: www.hostpph.com & smallbussines.com

Background

Expected

Reality

Page 4: Quantification of Uncertainty of Geometallurgical Variables in Mine

What (ore-waste discrimination)

When (scheduling) Where (processing)

Under technical, operative and

economic restrictions

Mine Planning Background

Source: www.im-maining.com

Page 5: Quantification of Uncertainty of Geometallurgical Variables in Mine

Planning Optimisation

Extraction sequence

Layouts: Open pit Underground

Criteria: Maximising NPV Minimising

deviation on production targets

Source: www.womp-int.com

Background

Page 6: Quantification of Uncertainty of Geometallurgical Variables in Mine

3D discretisation

Many small blocks

Each block represents many features Quality Quantity Metallurgical

response

Resource Model Background

Page 7: Quantification of Uncertainty of Geometallurgical Variables in Mine

𝑣 𝑖=𝒕𝒊∗𝒈𝒊∗𝑹∗𝑷− 𝑡𝑖∗𝑪𝑴− { 0 ,𝑖𝑓 𝑤𝑎𝑠𝑡𝑒𝑡 𝑖∗𝑪𝑷 ,𝑒𝑙𝑠𝑒}∀ 𝑖∈𝐵

max∑i∈ B

❑ 𝑣𝑖(1+𝑟 )𝑖

Goal: maximising profit for all blocks

Quantity

Quality

Processing recovery

Price

Mining cost

Optimisation FormulationBackground

Economic value of block i

Processing cost (Newman et al. 2010)

Page 8: Quantification of Uncertainty of Geometallurgical Variables in Mine

Literature Review

Uncertainty sources Financial Geological Metallurgical

Risk analysis

(Dowd, 1994; Dimitrakopoulos, 1998)

Source: www.ni.com

Page 9: Quantification of Uncertainty of Geometallurgical Variables in Mine

Financial Price, Costs Foreign interchange

rates Statistical

distributions Historical (Grobler, Elkington

& Rendu, 2011)

Lognormal (Amankwah, Larsson & Textorius, 2013)

Wiener process (Evatt, Soltan & Johnson, 2012)

Uncertainty Sources Literature Review

Source: www.agmetalminer.com

Page 10: Quantification of Uncertainty of Geometallurgical Variables in Mine

Geological Grades (quality)

Spatial correlation Geostatistical

simulations

Better quantification of uncertainty

(Dowd 1994; Dimitrakopoulos 1998) Source: www.petrowiki.org

Uncertainty Sources Literature Review

Page 11: Quantification of Uncertainty of Geometallurgical Variables in Mine

Metallurgical Recovery Rock type

Can be simulated(Suazo, Kracht and Alruiz,2010)

They are not included in current research

Uncertainty Sources Literature Review

Source: ageofempiresonline.wikia.com

Page 12: Quantification of Uncertainty of Geometallurgical Variables in Mine

Monte Carlo simulations

Distributions of Net Present Value

Risk Assessment Literature Review

(Dowd, 1994)

Page 13: Quantification of Uncertainty of Geometallurgical Variables in Mine

Stochastic optimisation Probability

distributions Objective function Restrictions

(Lagos et al., 2011; Amankwah, Larsson & Textorius, 2013) Source: www.sciencedirect.com

Risk Assessment Literature Review

Page 14: Quantification of Uncertainty of Geometallurgical Variables in Mine

Real option valuation Static NPV Without

flexibility With flexibility

Risk Assessment Literature Review

(Dimitrakopoulos & Abdel Sabour, 2007).

Page 15: Quantification of Uncertainty of Geometallurgical Variables in Mine

Gaps

Geometallurgical features

Underground mines

Mine complexity

Page 16: Quantification of Uncertainty of Geometallurgical Variables in Mine

Gaps

(1) Key geometallurgical features are missed, fixed or predefined

Rock types

Recovery

Hardness

Page 17: Quantification of Uncertainty of Geometallurgical Variables in Mine

Gaps

(2) Underground mines Several

methods Block location

change Fracturing

modelling Source: www.technology.infomine.com

Page 18: Quantification of Uncertainty of Geometallurgical Variables in Mine

Gaps

(3) Mine Complexity Multi source Multi process Stockpiles

Source: www.minesight.com

Page 19: Quantification of Uncertainty of Geometallurgical Variables in Mine

Aims

Quantification of uncertainty of geometallurgical variables Grades Rock types Recovery

Page 20: Quantification of Uncertainty of Geometallurgical Variables in Mine

Aims

New optimisation formulations Stochastic

optimisation Multi-objective

optimisation Mine complexity

Page 21: Quantification of Uncertainty of Geometallurgical Variables in Mine

Aims

Efficient algorithms Meta-heuristic algorithms

Near-to-optimal Fast computing

Handle realistic problems

Page 22: Quantification of Uncertainty of Geometallurgical Variables in Mine

Conclusion

Geometallurgical variables can improve risk assessment

Complexity (real word) Better tools for decision makers

Page 23: Quantification of Uncertainty of Geometallurgical Variables in Mine

Quantification of Uncertainty of Geometallurgical Variables in Mine Planning Optimisation

Exequiel Sepulveda

School of Civil, Environmental and Mining Engineering

Supervisors: Peter Dowd and Chaoshui Xu


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