The relevant trends in process industry could be summarized as: better profitability, increased flexibility and the incorporation of sustainability. The current research focus is on profitability and flexibility and could be summarized as model-based control/optimization of ``difficult'' unit operations and complete plants. In this research three aspects can be recognized: Modeling: this includes modeling, validation, identification and reduction. After modeling we should end up with a model that has sufficient accurate predictive power at acceptable computational cost. Observation: Attention is paid to extended Kalman filters and horizon estimators. Control/optimization: The research in this area concentrates on dynamic optimization (sequential and simultaneous approach) but also on the integration of control and optimization. This research is done in close cooperation with the process industry: Bayer, Shell, Solvay, AKZO, Unilever, ...
- Advanced Autonomous Model-Based Operation of Industrial Process Systems
- Batch-to-batch learning for process control with application to cooling crystallization.
- Block structured based model reduction
- Control in reservoir engineering under model uncertainty
- Dynamic modeling, optimization and control of intensified production of fine chemicals in continuous reactors
- Economic optimal plantwide control
- Exploiting the interface between process intensification and process control
- Least costly identification experiment for control
- Nonlinear Model Predictive Control of MSWC plants
- System identification of hydrocarbon reservoirs