Learning reduced-order models for dynamic CO2 methanation using operator inference

• Published in: Computer Aided Chemical Engineering Vol. 53; pp. 3319 - 3324
• Main Authors: Peterson, Luisa, Goyal, Pawan, Gosea, Ion Victor, Bremer, Jens, Benner, Peter, Sundmacher, Kai
• Format: Book Chapter
• Language: English
• Published: 2024
• Subjects: Dynamic systems; Methanation; Model identification; Model order reduction; Operator inference; Power-to-X; Reactor modeling; Methanation; Reactor modeling; Dynamic systems; Operator inference; Power-to-X; Model identification; Model order reduction
• DOI: 10.1016/B978-0-443-28824-1.50554-8
• ISBN: 9780443288241; 0443288240
• ISSN: 1570-7946

The efficient modeling of dynamic systems in process engineering is becoming increasingly important in the modern industrial landscape. Our study addresses this challenge by employing reduced-order modeling and model order reduction techniques, with a focus on the non-intrusive operator inference method. This method excels at handling the complexity of nonlinear dynamics, a key factor in ensuring both computational efficiency and accuracy of approximations. We demonstrate the potential of operator inference by applying it to a CO2 methanation reactor model within the power-to-x framework. The results show the ability of the reduced-order model to provide an accurate yet streamlined solution, which is essential for the analysis of dynamic systems in the Industry 4.0 era.