Model predictive control (MPC) and reinforcement learning (RL) are two powerful optimal control methods. However, the performance of MPC depends mainly on the accuracy of the underlying model and the prediction horizon. Classic RL needs an excessive amount of data and cannot consider constraints explicitly. This work combines both approaches and uses Q-learning to improve the closed-loop performance of a parameterized MPC structure with a surrogate model and a short prediction horizon. The parameterized MPC structure provides a suitable starting point for RL training, which keeps the required data in a reasonable amount. Moreover, constraints are considered explicitly. The solution can be obtained in real-time due to the surrogate model and the short prediction horizon. The method is applied for control of a flash separation unit and compared to a MPC structure that uses a rigorous model and a large prediction horizon.

Continuously operated (bio-)chemical processes increasingly suffer from external disturbances, such as feed fluctuations or changes in market conditions. Product quality often hinges on control of rarely measured concentrations, which are expensive to measure. Semi-supervised regression is a possible building block and method from machine learning to construct soft-sensors for such infrequently measured states. Using two case studies, i.e., the Williams-Otto process and a bioethanol production process, semi-supervised regression is compared against standard regression to evaluate its merits and its possible scope of application for process control in the (bio-)chemical industry.

For economic nonlinear model predictive control and dynamic real-time optimization fast and accurate models are necessary. Consequently, the use of dynamic surrogate models to mimic complex rigorous models is increasingly coming into focus. For dynamic systems, the focus so far had been on identifying a system's behavior surrounding a steady-state operation point. In this contribution, we propose a novel methodology to adaptively sample rigorous dynamic process models to generate a dataset for building dynamic surrogate models. The goal of the developed algorithm is to cover an as large as possible area of the feasible region of the original model. To demonstrate the performance of the presented framework it is applied on a dynamic model of a chlor-alkali electrolysis.