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

arXiv:2402.17698v1 Announce Type: cross
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 …

abstract application arxiv carbon carbon dioxide challenge cs.lg cs.na data data-driven domain engineering inference linear math.na modeling process study systems type work