Learning reduced-order Quadratic-Linear models in Process Engineering
  using Operator Inference
Ion Victor Gosea Luisa Peterson Pawan Goyal Jens Bremer Kai Sundmacher Peter Benner
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.