Fitting micro-kinetic models to transient kinetics of temporal analysis of product reactors using kinetics-informed neural networks

TL;DR

This paper introduces kinetics-informed neural networks (KINNs) as an efficient method to fit and interpret transient kinetic data from TAP experiments, overcoming computational challenges and noise sensitivity of traditional methods.

Contribution

The work demonstrates that KINNs can accurately fit TAP data, retrieve kinetic parameters, and interpolate unseen behaviors, even with limited information, offering a scalable alternative to existing techniques.

Findings

KINNs effectively fit TAP transient data.

They retrieve kinetic parameters with improved noise tolerance.

They interpolate unseen pulse behaviors accurately.

Abstract

The temporal analysis of products (TAP) technique produces extensive transient kinetic data sets, but it is challenging to translate the large quantity of raw data into physically interpretable kinetic models, largely due to the computational scaling of existing numerical methods for fitting TAP data. In this work, we utilize kinetics-informed neural networks (KINNs), which are artificial feedforward neural networks designed to solve ordinary differential equations constrained by micro-kinetic models, to model the TAP data. We demonstrate that, under the assumption that all concentrations are known in the thin catalyst zone, KINNs can simultaneously fit the transient data, retrieve the kinetic model parameters, and interpolate unseen pulse behavior for multi-pulse experiments. We further demonstrate that, by modifying the loss function, KINNs maintain these capabilities even when precise thin-zone information is unavailable, as would be the case with real experimental TAP data. We also compare the approach to existing optimization techniques, which reveals improved noise tolerance and performance in extracting kinetic parameters. The KINNs approach offers an efficient alternative for TAP analysis and can assist in interpreting transient kinetics in complex systems over long timescales.