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Accelerating kinetic parameter identification by extracting information from transient data : a hydroprocessing study case

(2020) CATALYSTS. 10(4).
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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.
Keywords
kinetic modeling, hydrocarbon, reactor, stabilization, transient data, HYDRODENITROGENATION, HYDROCRACKING, HYDRODESULFURIZATION, CATALYST, DESIGN, MODEL

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Citation

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MLA
Cao, Ngoc-Yen-Phuong, et al. “Accelerating Kinetic Parameter Identification by Extracting Information from Transient Data : A Hydroprocessing Study Case.” CATALYSTS, vol. 10, no. 4, 2020, doi:0.3390/catal10040361.
APA
Cao, N.-Y.-P., Celse, B., Guillaume, D., Guibard, I., & Thybaut, J. (2020). Accelerating kinetic parameter identification by extracting information from transient data : a hydroprocessing study case. CATALYSTS, 10(4). https://doi.org/0.3390/catal10040361
Chicago author-date
Cao, Ngoc-Yen-Phuong, Benoit Celse, Denis Guillaume, Isabelle Guibard, and Joris Thybaut. 2020. “Accelerating Kinetic Parameter Identification by Extracting Information from Transient Data : A Hydroprocessing Study Case.” CATALYSTS 10 (4). https://doi.org/0.3390/catal10040361.
Chicago author-date (all authors)
Cao, Ngoc-Yen-Phuong, Benoit Celse, Denis Guillaume, Isabelle Guibard, and Joris Thybaut. 2020. “Accelerating Kinetic Parameter Identification by Extracting Information from Transient Data : A Hydroprocessing Study Case.” CATALYSTS 10 (4). doi:0.3390/catal10040361.
Vancouver
1.
Cao N-Y-P, Celse B, Guillaume D, Guibard I, Thybaut J. Accelerating kinetic parameter identification by extracting information from transient data : a hydroprocessing study case. CATALYSTS. 2020;10(4).
IEEE
[1]
N.-Y.-P. Cao, B. Celse, D. Guillaume, I. Guibard, and J. Thybaut, “Accelerating kinetic parameter identification by extracting information from transient data : a hydroprocessing study case,” CATALYSTS, vol. 10, no. 4, 2020.
@article{8655176,
  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.},
  articleno    = {361},
  author       = {Cao, Ngoc-Yen-Phuong and Celse, Benoit and Guillaume, Denis and Guibard, Isabelle and Thybaut, Joris},
  issn         = {2073-4344},
  journal      = {CATALYSTS},
  keywords     = {kinetic modeling,hydrocarbon,reactor,stabilization,transient data,HYDRODENITROGENATION,HYDROCRACKING,HYDRODESULFURIZATION,CATALYST,DESIGN,MODEL},
  language     = {eng},
  number       = {4},
  pages        = {19},
  title        = {Accelerating kinetic parameter identification by extracting information from transient data : a hydroprocessing study case},
  url          = {http://dx.doi.org/0.3390/catal10040361},
  volume       = {10},
  year         = {2020},
}

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