Issue 6, 2023

High-throughput experimentation based kinetic modeling of selective hydrodesulfurization of gasoline model molecules catalyzed by CoMoS/Al2O3

Abstract

The selective hydrodesulfurization (HDS) of fluidized catalytic cracking gasoline still represents a challenging step to minimize hydrogen overconsumption and maintain high octane numbers. To better understand the competition between desulfurization and hydrogenation reactions, a Langmuir–Hinshelwood kinetic model is established, based on high-throughput HDS experiments of a model feedstock of 3-methyl-thiophene (3MT) and 2,3-dimethyl-but-2-ene over CoMoS/Al2O3 catalysts. To reduce the model's dimensionality, some key enthalpies of adsorption are determined by density functional theory (DFT) calculations. The model takes into account 16 different reactions (hydrogenation, hydrodesulfurization, isomerization) for which rate constants and adsorption constants are determined to reproduce adequately the experimental product distribution. The model is finally used to predict and discuss the impact of operating conditions (partial pressures of key reactants and temperature) on the selectivity. The selectivity is most affected by the conversion levels of the reactants, with an optimum desulfurization selectivity at approximately 30–50% 3MT conversion. Operating at low temperature (170 °C) is also favorable for the HDS selectivity.

Graphical abstract: High-throughput experimentation based kinetic modeling of selective hydrodesulfurization of gasoline model molecules catalyzed by CoMoS/Al2O3

Supplementary files

Article information

Article type
Paper
Submitted
09 Dec 2022
Accepted
19 Jan 2023
First published
20 Jan 2023
This article is Open Access
Creative Commons BY-NC license

Catal. Sci. Technol., 2023,13, 1777-1787

High-throughput experimentation based kinetic modeling of selective hydrodesulfurization of gasoline model molecules catalyzed by CoMoS/Al2O3

E. Galand, F. Caron, E. Girard, A. Daudin, M. Rivallan, P. Raybaud, J. Schweitzer and Y. Schuurman, Catal. Sci. Technol., 2023, 13, 1777 DOI: 10.1039/D2CY02093A

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