This project is part of CRC 1615: SMART Reactors for Future Process Engineering.

Using a reactor for multiple purposes as well as autonomous process operation can only be achieved through pushing automation and control to unprecedented levels of data-driven and learning-based adaptation. Moreover, feedback control allows to compensate and alleviate unforeseen disturbances and faults, i.e., control fosters resilience. Transfer between locations and scales means that also the underlying control architectures must be designed with adaptation in mind. On this canvas, project C05 considers two main research questions: How to enable multipurpose operation through process-informed learning-based control and how to reconcile sustainability and resilience through measurement-based feedback optimization? The former is approached through a hybrid modelling strategy, wherein first-principles models are combined with reaction and process specific data-driven model components. The latter is approached using safe variants of Wiener kernel regression for real-time optimization.

Principle Investigator: Prof. Dr.-Ing. Timm Faulwasser

Coworker: Dr.-Ing. Guanru Pan