Learning reduced-order models for dynamic CO2 methanation using operator inference - Peterson, Luisa; Goyal, Pawan; Gosea, Ion Victor; Bremer, Jens; Benner, Peter; Sundmacher, Kai (Computer Aided Chemical Engineering, 2024)
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.