  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.
