For over a decade, stochastic optimization has emerged as a framework that is capable of generating a life-of-mine production schedule that increases net present value, while simultaneously reduces the risk associated with geological uncertainty. This paper focuses on the application of the stochastic strategic mine planning for technical risk management in a real-world iron ore deposit in Quebec, Canada, demonstrating the key steps of the framework. The related approach, firstly, quantifies both the volumetric and multi-element grade uncertainty of the deposit by generating a set of equally-probable scenarios of the orebody. In the case study presented, the boundaries of the iron ore deposit are generated using the pattern-based wavelet simulation algorithm, while the pertinent geological properties, namely, head iron, Davis Tube weight recovery, Davis Tube concentrate iron, and silica content are jointly simulated using the direct block minimum/maximum autocorrelation factor algorithm. Subsequently, the simulated scenarios of the iron deposit serve as input to a life-of-mine stochastic integer programming production scheduling model. The latter optimization model is employed to minimize and manage the risk associated with the geological uncertainty of the deposit, in terms of meeting production targets, while generating an optimal mining sequence of extraction maximizing net present value. The results of the case study serve as valuable input for the conceptual stage of the specific mining project and, in particular, quantify the risk associated with the product's silica content, the total iron production and the expected annual cash flows.
Paru en novembre 2017 , 21 pages