Bayesian Statistics to Elucidate the Kinetics of γ-Valerolactone from n-Butyl Levulinate Hydrogenation over Ru/C

Sarah Capecci, Yanjun Wang, Jose Delgado, Valeria Casson Moreno, Mélanie Mignot, Henrik Grénman, Dmitry Yu Murzin, Sébastien Leveneur*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The synthesis of γ-valerolactone (GVL), a platform molecule that can be produced from lignocellulosic biomass, was performed in this work by hydrogenation of an alkyl levulinate over Ru/C. Kinetic models reported in the literature are typically not compared with rival alternatives, even if a discrimination study is needed to find the optimum operating conditions. Different surface reaction kinetic models were thus considered in this work, specifically addressing hydrogenation of butyl levulinate to GVL, where the latter was used as a solvent to minimize potential solvent interference with the reaction, including its evaporation. The Bayesian approach was applied to evaluate the probability of each model. It was found that non-competitive Langmuir-Hinshelwood with no dissociation of the hydrogen model has the highest posterior probability.

Original languageEnglish
Pages (from-to)11725-11736
Number of pages12
JournalIndustrial and Engineering Chemistry Research
Volume60
Issue number31
DOIs
Publication statusPublished - 11 Aug 2021
MoE publication typeA1 Journal article-refereed

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