Modelling open pit dynamics using discrete simulation

The objective in any mining operation is to exploit ore at the lowest possible cost with the prospect of maximizing profits. The planning of an open pit mine is an economic exercise, constrained by certain geological, operating, technological and local field factors. Heuristic methods, economic parametric analysis, operations research and genetic algorithms have been used to formulate periodic open pit planning problems. Open pit design, optimization and subsequent materials scheduling problems are governed by stochastic dynamic process. Thus, current algorithms are limited in their abilities to address problems arising from these random and dynamic field processes. The primary objective of this study is to use a discrete stochastic simulation to capture the random field processes associated with open pit design and materials scheduling. An open pit production simulator (OPPS), implemented in MATLAB, based on a modified elliptical frustum is used to model the geometry of open pit layout expansion. The simulator mimics the periodic expansion of the open pit layouts. The interaction of the open pit expansion model with the geological and economic block model returns the respective amount of ore, waste, stockpile materials, and the net present value of the venture. A case study of an iron ore deposit with 114 000 blocks was carried out to verify and validate the model. The optimized pit limit was designed using the Lerchs - Grossman algorithm. The best-case annual schedule, generated by the shells node in Whittle Four-X, yielded a net present value (NPV) of $414 million over a 21-year mine life at a discount rate of 10% per annum. The best scenario out of 5000 simulation iterations using OPPS resulted in an NPV of $422 million over the same time span. Further research, based on hybrid stochastic simulation in conjunction with reinforcement learning, can provide a powerful tool for addressing the random field and dynamic processes in long-term open pit planning.