CRC 1615 - Project C05: Adaptive and Learning-based Control Architectures for SMART Reactors

Reactors of the future need to be “SMART”: Sustainable, Multipurpose, Autonomous (self-adaptive), Resilient, and Transferable. Control and process engineering leveraging learning, feedback, and online adaptation are pivotal in bringing forth these attributes: Modern optimization-based process control fosters sustainability as it enables to optimize process performance while satisfying operational constraints. 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. Put differently, feedback control is indispensable for realizing the SMART attributes in reactor operation.On this canvas, this project investigates tailored control methods for SMART reactors. We consider 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 first question is approached through a hybrid modelling strategy, wherein first-principles models are combined with reaction and process specific data-driven model components. In this context, a key tool are data-driven models based on composite reproducing kernel structures which allow for flexible system description capturing different use cases. We then use these models in optimization-based and predictive control schemes.For the second question, we consider a Bayesian approach to real-time optimization for SMART reactors. In particular, we explore the use of safe variants of Wiener kernel regression for a real-time optimization of process performance. We investigate how to combine existing first-principles models with correction terms obtained from kernel regression – with and without distributional uncertainty surrounding the additive measurement disturbance. Eventually, this leads to the consideration of risk measures in the objective of the real-time optimization.The developed methods are tested on the van de Vusse scheme before we consider other reactions investigated in project areas B and C of the CRC.

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