Integrating online mineral liberation data into process control and optimisation systems for grinding–separation plants

Abstract This paper evaluates the benefits of explicitly integrating online mineral liberation data in control systems for grinding–separation circuits. Although liberation is a critical variable for separation processes, this endeavour has not been attempted mainly because sensors providing continuous online or even at line measurements are yet to be developed. The ore particle size is seen as the key variable influencing mineral liberation. In this study, a phenomenological two-stage comminution circuit simulator previously calibrated from industrial and laboratory data was supplemented with a three-cell flotation line in open circuit. An economic real-time optimisation (RTO) layer coordinates the setpoints of a linear model predictive controller (MPC) of the grinding circuit. Assumed measurable, mineral liberation data feeds the RTO to update the particle size target parameter in an internal model predicting the flotation concentrate mass flow rate, grade, and recovery. Profits, derived from concentrate production rate, grade, and metal recovery, can improve by up to +5% compared with the standard approach, i.e. keeping the flotation feed particle size target constant.