Affiliation:
1. Department of Mining and Materials Engineering, McGill University, Canada
Abstract
Technical and financial uncertainties present significant risks to the profitability and efficiency of mining operations. Unexpected realizations (e.g., price or grade) may result in catastrophic consequences. This phenomenon forces mining industries to use probabilistic decision-making tools to assess, mitigate, and manage the risks associated with these uncertainties. In this context, mining operations need robust schedules, which are insensitive to market changes and/or unexpected grade realizations. The mine production scheduling problem consists of three sub-problems: extraction sequencing (timing), ore-waste discrimination (classification) and production rates (utilization). The solutions to these problems are generated under significant parameter uncertainties. This paper proposes an extraction sequencing approach in which the net present value of a mining project is, for a given risk tolerance, maximized and the actual risk tolerance is then verified through Monte-Carlo simulations. The risk tolerance is a measure of uncertainty and that secures the project net present value with a given probability. Risk tolerance is derived through the use of standard deviations of block economic values in the medium of multiple grade and economic images of orebody. The proposed approach is demonstrated on a case study using gold mine data. The results of the case study show that the proposed approach, combining chance-constrained programming and Monte-Carlo simulation, can be used to solve the mine extraction sequencing problem in an uncertain financial and technical environment.
Funder
Natural Sciences and Engineering Research Council of Canada
Subject
Computer Graphics and Computer-Aided Design,Modeling and Simulation,Software
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献