Stochastic Open Pit Design with a Network Flow Algorithm: Application at Escondida Norte, Chile

In the optimization of open pit mine design, the Lerchs-Grossmann algorithm is the industry standard, although network flow algorithms are also well suited, efficient, and known. The stochastic version of the conventional (deterministic) network flow algorithm is based on the use of multiple simulated realizations of the ore deposit, thus accounting for geological uncertainty. In comparison, the conventional pit optimization methods use only one estimated or average-type model of the deposit and assume it represents the exact deposit in the ground. The use of multiple scenarios results in the ability to generate risk profiles in terms of both grade and material types for pit designs and production schedules. This thesis focuses on the application of the stochastic maximum flow algorithm for multiple ore processing destinations at the Escondida Norte copper mine, Chile. The case study shows the optimal pushback layout minimising geological risk during the life-of-mine. The limitation of this method is that it uses only a part of the local joint uncertainty of the block grades and material types. However, it can be extended to account for simulated commodity price forecasts as well as discounting.Section Resources EngineeringDepartment of GeotechnologyCivil Engineering and Geoscience