Process modelling and control
Dynamical process models underpin advanced process control and obtaining this type of model is typically very costly and time consuming for the complex processes in question.
One focus of our research is thus to develop efficient methods to combine first principle models with data-driven modeling. Here we draw heavily upon state-of-the-art techniques in system identification (see this topic for details). Within process control, a current focus of our research is on integrating real-time optimization and feedback control. We consider both model-free and model-based optimization, notably Extremum Seeking Control and Economic Model Predictive Control, respectively. Applications are found in classical process industry, such as pulp and paper, as well as in the emerging field of advanced control of bioprocesses.