Precise and imprecise Bayesianism applied to gas-solid reactions

Gas–solid reactions play a crucial role in sustainability, yet very few studies have focused on the uncertainty of their chemical kinetic parameters and its propagation. In this pioneering work, based on a numerically generated synthetic dataset of conversion profiles, we address the uncertainty arising from variations in powder particle size between any two small powder samples, which impacts experimental conversion profiles. This variation is assumed to follow a log-normal distribution and is propagated into the uncertainty of the activation energy, which subsequently affects the uncertainty of the delay time at which the chemical conversion reaches a desired value under other conditions. Both precise and imprecise Bayesian approaches were compared. The results indicate that precise Bayesian methods struggle to differentiate effectively between varying levels of knowledge. In contrast, the imprecise Bayesian method based on a set of truncated normal distributions proved efficient and significantly more useful than the one based on uniform priors for this purpose. Finally, we provide suggestions on how to apply this methodology to more realistic settings.

Authors

• Marc Fischer
• Lo\"\ic Favergeon

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