Issue 1, 2024

LearnCK: mass conserving neural network reduction of chemistry and species of microkinetic models

Abstract

Reduction of chemical reaction mechanisms has been long studied to minimize the computational cost of reacting flows or the number of parameters of catalytic reaction models for estimation from experimental data. Conventional reduction techniques encompass either a tabulation of the reaction rates of elementary reactions or a reduction of elementary reactions. We introduce a Python-TensorFlow tool to learn chemical kinetics (LearnCK) systematically and automatically from microkinetic models using artificial neural networks (NNs). The approach constructs overall reactions among stable species only and interconversion rates and dramatically reduces the number of species and, thus, of the differential equations (the most expensive aspect in reacting flows). Doing this also removes the stiffness and nearly eliminates the complexity and cost of estimating the entire thermochemistry and kinetic rate expressions for computing reaction rates. Python programming automates training data generation, extracts metadata for fitting the NNs, and deploys the NN model. Since NNs are black boxes, we propose an approach to conserve mass. We demonstrate the method for the ammonia synthesis on Ru and the methane non-oxidative coupling over a single-atom Fe/SiO2 catalyst. The latter model includes over 500 gas and surface species and a combined 9300 gas and surface reactions. We demonstrate a nearly 1000-fold computational speedup and exceptional predictive accuracy using up to 8 overall reactions. The NN model is embedded in macroscopic reactor flow models to estimate uncertainty.

Graphical abstract: LearnCK: mass conserving neural network reduction of chemistry and species of microkinetic models

Supplementary files

Article information

Article type
Paper
Submitted
17 may 2023
Accepted
08 sen 2023
First published
11 sen 2023
This article is Open Access
Creative Commons BY license

React. Chem. Eng., 2024,9, 119-131

LearnCK: mass conserving neural network reduction of chemistry and species of microkinetic models

S. Kasiraju and D. G. Vlachos, React. Chem. Eng., 2024, 9, 119 DOI: 10.1039/D3RE00279A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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