Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference

• URL: http://arxiv.org/abs/2402.17698v1
• Date: Tue, 27 Feb 2024 17:21:10 GMT
• Title: Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
• Authors: Ion Victor Gosea, Luisa Peterson, Pawan Goyal, Jens Bremer, Kai Sundmacher, Peter Benner
• Abstract summary: This work addresses the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. The application in our study is carbon dioxide methanation, an important reaction within the Power-to-X framework.
• Score: 7.9164922475424815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. This is a non-intrusive, data-driven method for learning dynamical systems from time-domain data. The application in our study is carbon dioxide methanation, an important reaction within the Power-to-X framework, to demonstrate its potential. The numerical results show the ability of the reduced-order models constructed with operator inference to provide a reduced yet accurate surrogate solution. This represents an important milestone towards the implementation of fast and reliable digital twin architectures.

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