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In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference - a non-intrusive, data-driven method for understanding 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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Learning Reduced-Order Quadratic-Linear Models in Process Engineering Using Operator Inference

  • 会议论文

  • 发表日期：

    2025年

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

摘要

In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference - a non-intrusive, data-driven method for understanding 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.