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

doi:10.1021/acs.iecr.1c02107

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 journal › Article › Scientific › peer-review

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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 language	English
Pages (from-to)	11725-11736
Number of pages	12
Journal	Industrial and Engineering Chemistry Research
Volume	60
Issue number	31
Early online date	29 Jul 2021
DOIs	https://doi.org/10.1021/acs.iecr.1c02107
Publication status	Published - 11 Aug 2021
MoE publication type	A1 Journal article-refereed

Funding

This study has been done in the framework of Task 2: “Green process: 2nd generation of biomass” of the AMED project. The authors thank the AMED project. The AMED project has been funded with the support from the European Union with the European Regional Development Fund (ERDF) and from the Regional Council of Normandie. For the analytical part (GC-FID), the authors thank the European Regional Development Fund (GreenChem) and the Laboratoire d’Excellence (LabEx) SynOrg (ANR-11-LABX-0029). The authors thank the Maîtrise des Risques et Environementaux department and the Erasmus program that made the research project of S.C. possible.

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https://urn.fi/URN:NBN:fi-fe2022120168454

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

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. ",

author = "Sarah Capecci and Yanjun Wang and Jose Delgado and \{Casson Moreno\}, Valeria and M{\'e}lanie Mignot and Henrik Gr{\'e}nman and Murzin, \{Dmitry Yu\} and S{\'e}bastien Leveneur",

note = "Funding Information: This study has been done in the framework of Task 2: “Green process: 2nd generation of biomass” of the AMED project. The authors thank the AMED project. The AMED project has been funded with the support from the European Union with the European Regional Development Fund (ERDF) and from the Regional Council of Normandie. For the analytical part (GC-FID), the authors thank the European Regional Development Fund (GreenChem) and the Laboratoire d{\textquoteright}Excellence (LabEx) SynOrg (ANR-11-LABX-0029). The authors thank the Ma{\^i}trise des Risques et Environementaux department and the Erasmus program that made the research project of S.C. possible. Publisher Copyright: {\textcopyright} 2021 American Chemical Society.",

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doi = "10.1021/acs.iecr.1c02107",

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AU - Capecci, Sarah

AU - Wang, Yanjun

AU - Delgado, Jose

AU - Casson Moreno, Valeria

AU - Mignot, Mélanie

AU - Grénman, Henrik

AU - Murzin, Dmitry Yu

AU - Leveneur, Sébastien

N1 - Funding Information: This study has been done in the framework of Task 2: “Green process: 2nd generation of biomass” of the AMED project. The authors thank the AMED project. The AMED project has been funded with the support from the European Union with the European Regional Development Fund (ERDF) and from the Regional Council of Normandie. For the analytical part (GC-FID), the authors thank the European Regional Development Fund (GreenChem) and the Laboratoire d’Excellence (LabEx) SynOrg (ANR-11-LABX-0029). The authors thank the Maîtrise des Risques et Environementaux department and the Erasmus program that made the research project of S.C. possible. Publisher Copyright: © 2021 American Chemical Society.

PY - 2021/8/11

Y1 - 2021/8/11

N2 - 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.

AB - 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.

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JF - Industrial and Engineering Chemistry Research

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ER -

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

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