Mineral value chains consist of material from multiple sources, several processing streams, and transportation systems that combine to generate various saleable products. These sources of material can be mineral deposits, stockpiles or material purchased from external vendors. Typically, there is a high level of uncertainty associated with these sources of material, which propagates through the mineral value chain. Uncertainty in material types/grades can be modelled and used in the assessment of risk in mining plans. This uncertainty can also be incorporated during optimization in stochastic frameworks.
This paper presents an approach that first utilizes a set of scenarios derived from stochastic simulations of the different sources of material to perform risk analysis and then applies a simultaneous stochastic optimization framework to develop a robust mine plan for a multipit gold complex. The scenarios include stochastic simulations of initial stockpiles and incorporate the uncertainty of external material by means of Monte Carlo simulations over historical data. The analysis allows for potential risks associated with mine plans to be clearly quantified, particularly the extraction schedule's ability to meet key project targets on a yearly basis. The implementation of a stochastic optimization framework generates a mine plan with feasible additive consumption and expected NPV 10% higher than a base case plan.
Paru en novembre 2016 , 18 pages