Abstract

As processes involved in mineral processing operations increase their complexity, automation and control become critical to ensure an economically viable and environmentally sustainable operation. In the context of modern mineral processing, paste thickening stands out as a relatively new method for producing high density slurries that has proven challenging for standard control algorithms. In this setting, the use of machine-learning-based models within a predictive control strategy arises as an appealing alternative. This work presents a Random Forest Model Predictive Control scheme for paste thickening based on a purely data-driven approach for modeling and evolutionary strategies for solving the associated optimization problem. Results show that the proposed strategy outperforms conventional predictive control both qualitatively and quantitatively.