Stochastic optimization for a mineral value chain with nonlinear recovery and forward contracts*

Abstract

When a new forward contract is signed between a mining company and a customer to hedge the risk incurred by the uncertainty in commodity market, the mining company needs to re-optimize the plans of the entire value chain to account for the change of risk level. A two-stage stochastic mixed integer nonlinear program is formulated to optimize a mineral value chain in consideration of both geological uncertainty and market uncertainty. A heuristic is developed to deal with the complexity incurred by the throughput- and head-grade-dependent recovery rate in the processing plant. Through a series of numerical tests, we show that the proposed heuristic is effective and efficient. The test results also show that ignoring the dynamic recovery rate will result in loss and severe misestimation in the mineral value chains profitability. Based on the proposed model and heuristic, an application in evaluating and designing a forward contract is demonstrated through a hypothetical case study.