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Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

preprint
submitted on 06.04.2021, 14:16 and posted on 08.04.2021, 09:35 by Bjarne Kreitz, C. Franklin Goldsmith, Richard West, Emily Mazeau, Katrin Blondal, Gregor Wehinger, Thomas Turek, Khachik Sargsyan
Automatic mechanism generation is used to determine mechanisms for the CO2 hydrogenation on Ni(111) in a two-stage process, while considering the uncertainty in energetic parameters systematically. In a coarse stage, all the possible chemistry is explored with gas-phase products down to the ppb level, while a refined stage discovers the core methanation submechanism. 5,000 unique mechanisms were generated, which contain minor perturbations in all parameters. Global uncertainty assessment, global sensitivity analysis, and degree of rate control analysis are performed to study the effect 1 of this parametric uncertainty on the microkinetic model predictions. Comparison of the model predictions with experimental data on a Ni/SiO2 catalyst find a feasible set of microkinetic mechanisms that are in quantitative agreement with the measured data, without relying on explicit parameter optimization. Global uncertainty and sensitivity analyses provide tools to determine the pathways and key factors that control the methanation activity within the parameter space. Together, these methods reveal that the degree of rate control approach can be misleading if parametric uncertainty is not considered. The procedure of considering uncertainties in the automated mechanism generation is not unique to CO2 methanation and can be easily extended to other challenging heterogeneously catalyzed reactions

History

Email Address of Submitting Author

franklin_goldsmith@brown.edu

Institution

Brown University

Country

USA

ORCID For Submitting Author

0000-0002-2212-0172

Declaration of Conflict of Interest

no conflict of interest

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ChemRxiv

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