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
Outline
BackgroundLiterature reviewGapsAimsConclusion
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
What (ore-waste discrimination)
When (scheduling) Where (processing)
Under technical, operative and
economic restrictions
Mine Planning Background
Source: www.im-maining.com
Planning Optimisation
Extraction sequence
Layouts: Open pit Underground
Criteria: Maximising NPV Minimising
deviation on production targets
Source: www.womp-int.com
Background
3D discretisation
Many small blocks
Each block represents many features Quality Quantity Metallurgical
response
Resource Model Background
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)
Literature Review
Uncertainty sources Financial Geological Metallurgical
Risk analysis
(Dowd, 1994; Dimitrakopoulos, 1998)
Source: www.ni.com
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
Geological Grades (quality)
Spatial correlation Geostatistical
simulations
Better quantification of uncertainty
(Dowd 1994; Dimitrakopoulos 1998) Source: www.petrowiki.org
Uncertainty Sources Literature Review
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
Monte Carlo simulations
Distributions of Net Present Value
Risk Assessment Literature Review
(Dowd, 1994)
Stochastic optimisation Probability
distributions Objective function Restrictions
(Lagos et al., 2011; Amankwah, Larsson & Textorius, 2013) Source: www.sciencedirect.com
Risk Assessment Literature Review
Real option valuation Static NPV Without
flexibility With flexibility
Risk Assessment Literature Review
(Dimitrakopoulos & Abdel Sabour, 2007).
Gaps
Geometallurgical features
Underground mines
Mine complexity
Gaps
(1) Key geometallurgical features are missed, fixed or predefined
Rock types
Recovery
Hardness
Gaps
(2) Underground mines Several
methods Block location
change Fracturing
modelling Source: www.technology.infomine.com
Gaps
(3) Mine Complexity Multi source Multi process Stockpiles
Source: www.minesight.com
Aims
Quantification of uncertainty of geometallurgical variables Grades Rock types Recovery
Aims
New optimisation formulations Stochastic
optimisation Multi-objective
optimisation Mine complexity
Aims
Efficient algorithms Meta-heuristic algorithms
Near-to-optimal Fast computing
Handle realistic problems
Conclusion
Geometallurgical variables can improve risk assessment
Complexity (real word) Better tools for decision makers
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
Top Related