Micro-kinetics analysis based on partial reaction networks to compare catalysts performances for methane dry reforming reaction

Journal Article (2023)
Author(s)

Shambhawi (University of Cambridge)

J.M. Weber (TU Delft - Pattern Recognition and Bioinformatics)

Alexei A. Lapkin (Cambridge Centre for Advanced Research and Education in Singapore)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2023 Shambhawi, J.M. Weber, Alexei A. Lapkin
DOI related publication
https://doi.org/10.1016/j.cej.2023.143212
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Shambhawi, J.M. Weber, Alexei A. Lapkin
Research Group
Pattern Recognition and Bioinformatics
Volume number
466
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Abstract

Designing a simple, yet representative reaction network for subsequent micro-kinetic analysis is important for limiting the cost of evaluation and ensuring model solvability. This is currently achieved by employing sensitivity analysis over a comprehensive reaction network (CRN) to screen reaction species. However, as a reaction network is being simplified for a particular catalyst composition, it loses its transferability to other compositions. Therefore, in this study, a two-way approach is presented to circumvent this problem. Firstly, a generalizable model outcome is identified, i.e. minimum reactant conversions (xR), based on a mass-flow analysis. Then, a stepwise workflow is developed for constructing a partial reaction network (PRN) to insure transferability of min (xR) for a range of varying catalyst energetics, in the absence of experimental data for validation. Lastly, the transferability of this approach is demonstrated for CH4 dry reforming by developing a PRN using Ni(1 1 1) as the initial catalyst and testing it over Ru(0 0 1).