Automated Tuning of an Inverse Controller for a MIMO Bulk Tailings Treatment Plant Using Reinforcement Learning

Despite the prevalence of established tuning methods, a significant proportion of industrial control loops remain poorly tuned, often requiring manual intervention or re-tuning due to changing process dynamics. Reinforcement learning (RL) offers a promising solution by enabling automatic controller tuning through interaction with the environment. This paper investigates the use of RL to tune an inverse controller for a nonlinear 2 × 2 bulk tailings treatment (BTT) surge tank system. A proximal policy optimisation (PPO) agent is trained to select safe and effective tuning parameters. Simulation results demonstrate that the RL-tuned controller exceeds benchmark performance.