Embedding Physics into Neural ODEs to learn Kinetics from Integral Reactors

While the digitalization of chemical research and industry is vastly increasing the amount of data for developing kinetic models, model parametrization is not keeping up. To take advantage of the full potential of this data, machine learning tools are required that autonomously learn kinetics from reactor data. Previously, we introduced a neural network with embedded stoichiometry and the thermodynamics for data efficient kinetic modelling. When trained as a neural ODE, this physics embedded neural network discovered kinetics from integral reactor measurements of an equilibrium limited steam reforming reactor whereas conventional neural ODEs failed. We now extend the application to another industrially relevant case of reactors operating at full conversion. Using the preferential oxidation of CO in H2 rich streams as an example, we show that the physics embedded neural network discovers kinetics from stiff systems containing cases of both full conversion and equilibrium limitation using integral reactor data.