Advanced Predictive Process Control for Industrial Thickeners

Efficient control of industrial thickeners is crucial for optimizing solid-liquid separation processes, especially in fields like mining and wastewater treatment. Traditional model predictive control (MPC) strategies, even though useful in most applications, can face trouble trying to maintain their performance when faced with time-varying dynamics due to factors such as wear and tear of equipment or changes in feed properties. To address these limitations, this paper highlights an adaptive model predictive control (AMPC) strategy that uses real-time parameter identification to update the prediction model of the usual MPC algorithm. The results show that while AMPC improves the robustness of the controller significantly, keeping critical process parameters such as slurry density well within operational limits under changing conditions, it still faces a number of challenges. AMPC struggles to compensate for unknown disturbances or to optimize flocculant consumption, resulting in economic problems. These results suggest that, despite the improvements offered by AMPC, further research is required to develop advanced disturbance rejection mechanisms and incorporate flocculant optimization strategies for more efficient and cost-effective performances.