Project Details
Projekt
Machine Learning for the Design and Control of Power2X Processes with Application to Methanol Synthesis
Applicants
Professor Dr.-Ing. Achim Kienle; Professor Dr. Sebastian Sager; Professor Dr.-Ing. Andreas Seidel-Morgenstern
Subject Area
Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
Chemical and Thermal Process Engineering
Mathematics
Chemical and Thermal Process Engineering
Mathematics
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 466495488
We 1) develop novel numerical methods that combine advantages of traditional modeling and optimization approaches with the power of data-driven machine learning (ML) and use them for 2) a new robust design and control methodology for power2chemicals processes. Methanol synthesis is considered as a challenging application example. Nonlinear dynamics due to strongly varying feed supplies are explicitly taken into account. The interdisciplinary work program reflects the complementary expertise of the three participating groups in experimental realization, conceptual design, control, and efficient algorithms.In a first step, a hybrid modeling methodology is developed. It combines experimental data from a gradientless kinetic reactor with available physico-chemical insight and efficient ML. We use universal differential equations and the differentiable end-to-end programming paradigm which allow to use deep learning for unknown or expensive model parts and to extend methods of mixed-integer optimal control (MIOC) and optimal experimental design (OED).In a second step, the hybrid models are used for robust plant design. In the first period the focus is on well-mixed isothermal reactors and nonisothermal spatially distributed reactors. We want to increase flexibility and tolerance against load and composition changes. For this, buffer tanks and different types of single stage and multi stage reactors with variable feed distribution are considered. The optimal configuration and optimal nominal open loop control profiles will be determined with MIOC using the dynamic hybrid models developed above for given characteristic feed profiles over time. Plant complexity will be constrained in the formulation of the optimization problem, in view of practical applicability.In addition to a robust design, in a third step, also a robust control strategy is developed to compensate plant model mismatch and unforeseen disturbances, which differ from the nominal case considered above. For this purpose we use repetitive online optimization (NMPC) and extend state-of-the-art concepts and techniques to the special case of hybrid ML models.Modeling, design and control results for a single gradientless reactor will be closely coupled to experiments, allowing for efficient data generation and validation at the same time. An investigation of more complex fixed-bed reactors will be done in silico using available mechanistic reference models. An experimental validation is planned for a possible second funding period, among others.We plan to generate advances in Optimal Decision Making via ML-embedded optimization and control. Our hybrid ML models enforce physical laws and at the same time show promise to extract new knowledge from data via symbolic regression. We develop offline and online experimental design methods to cope with heterogeneous data and develop robust optimization methods that shall increase safety in ML applications.
DFG Programme
Priority Programmes
