Goal‐Oriented Two‐Layered Kernel Models as Automated Surrogates for Surface Kinetics in Reactor Simulations

Abstract

Multi‐scale modeling allows the description of real reactive systems under industrially relevant conditions. However, its application to rational catalyst and reactor design is hindered by the prohibitively high computational cost associated with the chemical kinetics on the catalyst scale. Here, the computational cost is drastically reduced by introducing goal‐oriented kernel models that serve as surrogates for the chemical kinetics. This special model type allows for automated training set design and reliable results, even outside the training region. Therefore, it can be envisioned as a plug‐and‐play solution for accelerating reactive flow simulations with guaranteed accuracy.

Robust Mechanism Discovery with Atom Conserving Chemical Reaction Neural Networks

“…The proposed key species approach can be applied intuitively to small reaction systems without explicitly using the matrix B * . This is a common way to achieve atom conservation in surrogate models of catalytic systems [29][30][31][32][33]. Our element balance layer formalizes this approach, allowing its application to more complex systems.…”