Dual-Rate Operational Optimal Control for Flotation Industrial Process With Unknown Operational Model

This paper studies the two-timescales operational optimal control problem for the flotation industrial process with unknown operational model in the presence of setpoint constraints on the device layer. A novel dual-rate data-driven algorithm based on lifting technology and reinforcement learning (RL) is proposed. First, a dual-rate flotation process model including the device layer and the operational models is formulated. Then, a proportional integral (PI) controller for device layer is designed, by using lifting technology, a unified timescale controlled plant model with partially unknown dynamics is established. Based on such a model, an online learning algorithm using neural network is presented so that the operational indices, namely concentrate and tail grades, can be kept in the target range while maintaining the setpoints of the device layer within the specified bounds. At last, emulation experiments in a hardware-in-the-loop system are used to verify the effectiveness of the proposed method.