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

Sarah Capecci (1, 2) , Yanjun Wang (1) , Jose Delgado (1, 3) , Valeria Casson Moreno (2) , Mélanie Mignot (4) , Henrik Grénman (3) , Dmitry Yu. Murzin (3) , Sébastien Leveneur (1)

1 LSPC - Laboratoire de Sécurité des Procédés Chimiques
2 UNIBO - Alma Mater Studiorum Università di Bologna = University of Bologna
3 Åbo Akademi University [Turku]
4 COBRA - Chimie Organique et Bioorganique : Réactivité et Analyse

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

Mots clés: Bayesian statistics; γ-valerolactone; model discrimination; kinetic modeling; hydrogenation

Domaines: Chimie organique; Catalyse; Génie chimique

Sarah Capecci, Yanjun Wang, Jose Delgado, Valeria Casson Moreno, Mélanie Mignot, et al.. Bayesian Statistics to Elucidate the Kinetics of γ-Valerolactone from n -Butyl Levulinate Hydrogenation over Ru/C. Industrial and engineering chemistry research, 2021, 60 (31), pp.11725-11736. ⟨10.1021/acs.iecr.1c02107⟩. ⟨hal-03498097⟩