Learning Reduced-Order Models for Dynamic CO2 Methanation using Operator 
Inference

Peterson, Luisa; Bremer, Jens; Goyal, Pawan Kumar; Gosea, Ion Victor; Benner, Peter; Sundmacher, Kai

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Learning Reduced-Order Models for Dynamic CO2 Methanation using Operator Inference

Peterson, L., Bremer, J., Goyal, P. K., Gosea, I. V., Benner, P., & Sundmacher, K. (in press). Learning Reduced-Order Models for Dynamic CO2 Methanation using Operator Inference. In Proceedings of the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering (ESCAPE34/PSE24).

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Item Permalink: https://hdl.handle.net/21.11116/0000-000E-3309-F Version Permalink: https://hdl.handle.net/21.11116/0000-000E-330A-E

Genre: Conference Paper

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Creators:
Peterson, Luisa¹, Author
Bremer, Jens², Author
Goyal, Pawan Kumar³, Author
Gosea, Ion Victor³, Author
Benner, Peter³, Author
Sundmacher, Kai^{1, 4}, Author

Affiliations:
1Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738151
2TU Clausthal, ou_persistent22
3Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738141
4Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156

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Language(s): eng - English

Dates: Accepted: 2024

Publication Status: Accepted / In Press

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Rev. Type: Peer

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Title: ESCAPE34/PSE24

Place of Event: Florence, Italy

Start-/End Date: 2024-06-02 - 2024-06-06

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Title: Proceedings of the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering (ESCAPE34/PSE24)

Source Genre: Proceedings

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