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Journal Articles Catalysts Year : 2020

Accelerating Kinetic Parameter Identification by Extracting Information from Transient Data: A Hydroprocessing Study Case

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Abstract

Hydroprocessing reactions require several days to reach steady-state, leading to long experimentation times for collecting sufficient data for kinetic modeling purposes. The information contained in the transient data during the evolution toward the steady-state is, at present, not used for kinetic modeling since the stabilization behavior is not well understood. The present work aims at accelerating kinetic model construction by employing these transient data, provided that the stabilization can be adequately accounted for. A comparison between the model obtained against the steady-state data and the one after accounting for the transient information was carried out. It was demonstrated that by accounting for the stabilization, combined with an experimental design algorithm, a more robust and faster manner was obtained to identify kinetic parameters, which saves time and cost. An application was presented in hydrodenitrogenation, but the proposed methodology can be extended to any hydroprocessing reaction.
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Dates and versions

hal-02571855 , version 1 (13-05-2020)

Licence

Attribution - CC BY 4.0

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Ngoc-Yen-Phuong Cao, Benoît Celse, Denis Guillaume, Isabelle Guibard, Joris W Thybaut. Accelerating Kinetic Parameter Identification by Extracting Information from Transient Data: A Hydroprocessing Study Case. Catalysts, 2020, 10 (4), pp.361. ⟨10.3390/catal10040361⟩. ⟨hal-02571855⟩

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