Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911971698278400 |
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| author | Gosea, Ion Victor Peterson, Luisa Goyal, Pawan Bremer, Jens Sundmacher, Kai Benner, Peter |
| author_facet | Gosea, Ion Victor Peterson, Luisa Goyal, Pawan Bremer, Jens Sundmacher, Kai Benner, Peter |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17698 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference Gosea, Ion Victor Peterson, Luisa Goyal, Pawan Bremer, Jens Sundmacher, Kai Benner, Peter Numerical Analysis Machine Learning 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. |
| title | Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference |
| topic | Numerical Analysis Machine Learning |
| url | https://arxiv.org/abs/2402.17698 |