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.

本文评估了在研磨分离回路的控制系统中明确集成在线矿物释放数据的好处。尽管解放是分离过程的关键变量，但尚未尝试这种努力，主要是因为尚未开发提供连续在线甚至在线测量的传感器。矿石粒度被视为影响矿物释放的关键变量。在这项研究中，先前根据工业和实验室数据校准的现象学两级粉碎电路模拟器补充了开路中的三单元浮选线。经济实时优化 (RTO) 层协调研磨回路的线性模型预测控制器 (MPC) 的设定点。假设可测量，矿物释放数据提供给 RTO 以更新预测浮选精矿质量流量、品位和回收率的内部模型中的粒度目标参数。与标准方法（即保持浮选进料粒度目标恒定）相比，源自精矿生产率、品位和金属回收率的利润最多可提高 +5%。