Closed-loop control of a liquid-liquid mixer using MPC and GPR-models

Particulate processes are widely used in different industries. Due to their complexity and intransparency they are often controlled manually and open-loop in practice, with classic automatic control approaches being hard to apply. To close the control loop of a liquid-liquid mixer, we consider Model Predictive Control. The system is described by a Gaussian Process Regression-Model based on experimental data, since the well explored population balance models (PBM) are difficult to identify for a real system and require significant computational effort, making them hard to use in an automatic control scheme. The relevant cost function and weights take into account not only the mixing process, but also the favorable conditions for the subsequent settler.