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

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 me...

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Bibliographic Details
Published inComputer 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
LanguageEnglish
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
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Summary: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.
ISBN:9780443288241
0443288240
ISSN:1570-7946
DOI:10.1016/B978-0-443-28824-1.50554-8