Rule-based operator decision support for grinding circuit control

Abstract

This work investigated the application of interpretable machine learning methods for process control in the mineral processing industry. The thesis presents a methodology to extract intelligible rules from grinding circuit data using decision tree algorithms. The rule sets can aid process operators tasked with supervisory control of grinding circuits.