My research interests cover the wide area of modelling, identification and model-based control of dynamical systems, with applications in industrial processes, oil reservoir engineering systems and high-tech mechatronic systems with excursions to energy systems in automotive and power grid control. On the basis of an ERC Advanced Research Grant I am particularly focusing on the development of techniques for data analytics and identification in (large-scale) dynamic networks.

The development of models that are accurate and manageable to be used as a basis of model-based operations (control, monitoring, surveillance, detection, optimization, measurement) generally requires the combination of physical insights and dedicated measurement data. The models represent all aggregated knowledge that is available on the processes to be optimized/controlled, and allow the control/optimization of processes under uncertain conditions, and while adapting to changing environments and requirements.

Model-based X (control, monitoring, measurement, surveillance, detection, optimization, measurement) is the leading paradigm for high-performance operation of complex interacting engineering systems.

Many dynamical systems operate in interconnected structures (system-of-systems). Examples can be found in power grids, distributed processes in industry, and complex mechatronic systems, but e.g. also in systems biology. In this area our objective is to develop methods and tools for data-handling, data-analysis and data-driven modelling in dynamic networks. This includes aspects of optimizing  sensor and actuator locations, dependent on the goal of the models, and the online-monitoring of changing dynamics and topology. It requires the further extension of the classical theories for (closed-loop) identification into a much more generalized setting.  This research is executed in the scope of the ERC Advanced Research Grant SYSDYNET.

SYSDYNET: link to SYSDYNET website (not yet available).

The efficient production of oil from oil reservoirs can highly benefit from an effective use of model-based closed-loop reservoir management systems, based on systems and control concepts and tools. We particularly address the problem of water flooding of oil reservoirs, through controlled water injection into the reservoirs. This research is performed with a consortium of partners including TU Eindhoven, TU Delft, Shell, TNO and other oil companies. The challenge is to build model-based automation and reservoir management systems that can optimize the performance for situations of high uncertainty in the reservoir characteristics, and while maximally using informative data that is obtained during production.  

The objective is to develop model-based and data-based monitoring, surveillance, and control systems that have a high degree of autonomy, and that can continuously optimize the economic performance of an industrial process, while dynamically adapting to changing circumstances. This includes on-line performance modelling, data-driven model-maintenance, diagnosis and detection,  on-line controller tuning, and effective communication with the human operators.  It involves also the interaction between scheduling and planning, real-time optimization (RTO) and advanced process control (APC). Besides the handling of single process units and plants, it also extends to the interconnection of different plants and process sites, to optimize economic performance on a higher level of aggregation (site level or enterprise level). Examples of projects include AUTOPROFIT (2010-2014), IMPROVISE (2012-2016) and INSPEC (2017-2021).