The use of lignocellulosic biomass in the chemical industry can significantly contribute to respect the various international agreements on climate change. One of the most promising platform molecules issued from the lignocellulosic biomass hydrolysis is γ-valerolactone (GVL). GVL can be upgraded to valuable chemicals and produced by the hydrogenation of alkyl levulinates. Although these reactions are widely studied, seldom research focused on the solvent effect. To fill this gap, the effect of three different reaction mixtures with an excess of butyl levulinate (BL), of butanol and GVL was studied on the kinetics of BL hydrogenation to GVL over Ru/C. PC-SAFT (Perturbed-Chain Statistical Associating Fluid Theory) shows that the solubility of hydrogen is not constant during the reaction progress, and it was taken into account. To allow a fair comparison, kinetic models were developed using Bayesian statistics for each reaction mixture. The best performances were obtained when the reaction mixture has an excess of GVL.

Solvent effect on the kinetics of the hydrogenation of n-butyl levulinate to γ-valerolactone

Casson Moreno V.;
2021-01-01

Abstract

The use of lignocellulosic biomass in the chemical industry can significantly contribute to respect the various international agreements on climate change. One of the most promising platform molecules issued from the lignocellulosic biomass hydrolysis is γ-valerolactone (GVL). GVL can be upgraded to valuable chemicals and produced by the hydrogenation of alkyl levulinates. Although these reactions are widely studied, seldom research focused on the solvent effect. To fill this gap, the effect of three different reaction mixtures with an excess of butyl levulinate (BL), of butanol and GVL was studied on the kinetics of BL hydrogenation to GVL over Ru/C. PC-SAFT (Perturbed-Chain Statistical Associating Fluid Theory) shows that the solubility of hydrogen is not constant during the reaction progress, and it was taken into account. To allow a fair comparison, kinetic models were developed using Bayesian statistics for each reaction mixture. The best performances were obtained when the reaction mixture has an excess of GVL.
2021
Capecci, S.; Wang, Y.; Casson Moreno, V.; Held, C.; Leveneur, S.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1159375
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 25
  • ???jsp.display-item.citation.isi??? 24
social impact