Abstract
Flotation reagents are among the highest running costs in the concentrating stage of many mining operations. Overusing the reagents inflates these costs and affects the profitability of such mining operations. In addition, flotation reagents carried away with wastewater present an environmental hazard. Model predictive control (MPC) is a method that can be employed to optimise reagents' use. Experience suggests that linear MPC models are inadequate to explain reagent effects, and non-linear models for flotation effects are required. This study uses data from a lead-zinc flotation operation to compare linear and non-linear dynamic models. The results show that the non-linear models fit the training data better and provide an improved prediction of unseen test data. The implications of the results for MPC are described.
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