Practical performance of an open pit mine scheduling model considering blending and stockpiling
Keywords: software, mine planning, open pit mining, Stockpiling, Linear and integer programming
Open pit mine production scheduling (OPMPS) is a decision problem which seeks to maximize net present value (NPV) by determining the extraction time of each block of ore and/or waste in a deposit and the destination to which this block is sent, e.g., a processing plant or waste dump. Spatial precedence constraints are imposed, as are resource capacities. Stockpiles can be used to maintain low-grade ore for future processing, to store extracted material until processing capacity is available, and/or to blend material based on single or multiple block characteristics (i.e., metal grade and/or contaminant). We adapt an existing integer-linear program to an operational polymetallic (gold and copper) open pit mine, in which the stockpile is used to blend materials based on multiple block characteristics, and call it ((P) over cap (la)). We observe that the linear programming relaxation of our objective function is unimodal for different grade combinations (metals and contaminants) in the stockpile, which allows us to search systematically for an optimal grade combination while exploiting the linear structure of our optimization model. We compare the schedule of ((P) over cap (la)) with that produced by (P-ns) which does not consider stockpiling, and with ((P) over tilde (la)), which controls only the metal content in the stockpile and ignores the contaminant level at the mill and in the stockpile. Our proposed solution technique provides schedules for large instances in a few seconds up to a few minutes with significantly different stockpiling and material flow strategies depending on the model. We show that our model improves the NPV of the project while satisfying operational constraints. (C) 2019 Elsevier Ltd. All rights reserved.
|Título según WOS:||Practical performance of an open pit mine scheduling model considering blending and stockpiling|
|Fecha de publicación:||2020|