Abstract
Traditionally, mining engineers plan an open pit mine considering pre-established conditions of operation of the plant(s) derived from a previous plant optimization. By contrast, mineral processing engineers optimize the processing plants by considering a regular feed from the mine, with respect to quantity and quality of the materials. The methods implemented to optimize mine and metallurgical plans simultaneously are known in the mining industry as global or simultaneous optimizers. The development of these methods has been of major concern for the mining industry over the last decade. Some algorithms are available in commercial mining software packages however, these algorithms ignore the inherent geological uncertainty associated with the deposit being considered, which leads to shortfalls in production, quality, and expected cashflows. This paper presents a heuristic method to generate life-of-mine production schedules that consider operating alternatives for processing plants and incorporate geological uncertainty. The method uses iterative improvement by swapping periods and destinations of the mining blocks to generate the final solution. The implementation of the method at a copper deposit shows its ability to control mine and processing capacities while increasing the expected net present value by 30% when compared with a solution generated using a standard industry practice.
Highlights
Mining complexes contain multiple sequential activities that are strongly interrelated: (1) mining the materials from one or multiple sources; (2) blending the material considering stockpiling, (3) transforming the material in different processes or processing paths; (4) transporting the products to final destinations, etc
This is a problem with high complexity due to the link between time periods and discounting, the blending requirements, the flexibility generated from the stockpiles, the multiple processing alternatives, and the variability and uncertainty associated with grades and physical characteristics (Whittle 2007)
The method can handle processes with multiple operating alternatives, additives and blending constraints. It shows substantial gains when compared with solutions generated using standard industry practices; further work includes testing the ability of the method to handle complex blending operations and assessing the possibility for further improvements based on other heuristic strategies
Summary
Mining complexes contain multiple sequential activities that are strongly interrelated: (1) mining the materials from one or multiple sources; (2) blending the material considering stockpiling, (3) transforming the material in different processes or processing paths; (4) transporting the products to final destinations, etc. Ramazan and Dimitrakopoulos (2013) formulate the mine scheduling problem as a two-stage stochastic integer program (SIP) in which the first stage variables represent mining decision variables and the second stage variables represent deviation from grade and production targets evaluated on a set of orebody simulations. Menabde et al (2007) develop and implement a method that accounts for geological uncertainty and simultaneously optimizes the sequence of extraction of the mining blocks and the cut-off grade policy. The SIP formulation generates substantial improvements in terms of NPV and meeting production targets, industry standard optimizers such as CPLEX are unable to solve big size problems due to the large amount of integer variables, alternative solution avenues are being sought (Lamghari and Dimitrakopoulos 2012). Goodfellow and Dimitrakopoulos (2013) develop a simulated annealing implementation for pushback design to control deviation from pushback size targets considering different material types and processing plants.
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