posted on 2021-08-11, 17:34authored byGerhard
R. Wittreich, Geun Ho Gu, Daniel J. Robinson, Markos A. Katsoulakis, Dionisios G. Vlachos
Accounting
for parametric uncertainty in models is essential for
quantifying the models’ predictive ability. Recently, approaches
have been introduced to estimate parametric uncertainty in kinetic
models while accounting for correlations among energy parameters.
However, correlations have been estimated indirectly and correlations
in entropies have not been accounted for. For surface-catalyzed microkinetic
models of >C2 (more than two carbon-containing) molecules, which
consist
of thousands of reaction steps and intermediate surface species, first-principles
density functional theory (DFT) is costly, and thus, estimation of
thermochemistry and reaction barriers requires surrogate methods of
DFT, such as group additivity and Brønsted–Evans–Polanyi
relationships, respectively. For such parametrization, model uncertainty
is unclear. This work develops a framework to overcome these gaps
using group additivity and a single DFT functional. We estimate correlations
in parameters of kinetic models and quantify uncertainty for thermochemistry,
reaction barriers, reaction paths, and ultimately reaction rates,
accounting also for the contribution of entropic uncertainty. The
approach is illustrated on propane combustion and ethane oxidative
dehydrogenation reactions.