The ecological costs of open pit metal mining are quantified, which include lost value of direct eco-services, lost value of indirect eco-services, prevention and restoration costs, and cost of carbon emission from energy consumption. These ecological costs are incorporated in an iterative ultimate pit optimization algorithm. A case study is presented to demonstrate the influence of ecological costs on pit design outcome. The results show that it is possible to internalize ecological costs in mine designs. The pit optimization outcome shifts considerably to the conservative side and the profitability decreases substantially when ecological costs are accounted for.
Three important aspects of phase-mining must be optimized:the number of phases,the geometry and location of each phase-pit(including the ultimate pit),and the ore and waste quantities to be mined in each phase.A model is presented,in which a sequence of geologically optimum pits is first generated and then dynamically evaluated to simultaneously optimize the above three aspects,with the objective of maximizing the overall net present value.In this model,the dynamic nature of the problem is fully taken into account with respect to both time and space,and is robust in accommodating different pit wall slopes and different bench heights.The model is applied to a large deposit consisting of 2044 224 blocks and proved to be both efficient and practical.
In order to maximize the overall economic gain from a metal mine operation, selection of cutoff grades must consider two important aspects: the time value of money and the spatial variation of the grade distribution in the deposit. That is, cutoff grade selection must be dynamic with respect to both time and space. A newly developed method that fulfills these requirements is presented. In this method, the deposit or a portion of it under study is divided into "decision units" based on the mining method and sample data. The statistical grade distribution and the grade-tonnage relationship of each decision unit are then computed based on the samples falling in the unit. Each decision unit with its grade-tonnage relationship is considered as a stage in a dynamic programming scheme and the problem is solved by applying a forward dynamic programming based algorithm with an objective function of maximizing the overall net present value (NPV). A software package is developed for the method and applied to an underground copper mine in Africa.
