Performance analysis and artificial intelligence modeling for enhanced hydrogen production by catalytic bio-alcohol reforming in a membrane-assisted reactor

Ali Bakhtyari; Roghayeh Bardool; Mohammad Reza Rahimpour; Masoud Mofarahi; Chang Ha Lee

doi:10.1016/j.ces.2022.118432

Performance analysis and artificial intelligence modeling for enhanced hydrogen production by catalytic bio-alcohol reforming in a membrane-assisted reactor

Ali Bakhtyari, Roghayeh Bardool, Mohammad Reza Rahimpour, Masoud Mofarahi, Chang Ha Lee

Department of Chemical and Biomolecular Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Due to the significance of green processing and artificial intelligence (AI) modeling, herein, we proposed a mathematical and an AI model to enhance green hydrogen production from bio-alcohols in a membrane-assisted reactor. A sensitivity analysis for the effective variables was conducted in terms of conversion, thermal behavior, pressure drop, and hydrogen distribution. A single-layer perceptron network with tansig + trainlm functions and eight neurons yielded a 0.72 % error (mean squared error (MSE) = 2.69, R² = 0.99994) in the bio-methanol reformer, while the same functions with nine neurons presented a 0.22 % error (MSE = 0.32, R² = ∼1.00000) in the bio-ethanol reformer. Lastly, a multi-objective optimization was performed to determine the optimum operating conditions, which enhanced the hydrogen production in the bio-methanol (y_MeOH = 0.4 and 0.204 mol/h at 517 K and 6 bar) and bio-ethanol (y_EtOH = 0.3 and 1.8 mol/h at 823 K and 6 bar) reforming.

Original language	English
Article number	118432
Journal	Chemical Engineering Science
Volume	268
DOIs	https://doi.org/10.1016/j.ces.2022.118432
Publication status	Published - 2023 Mar 15

Bibliographical note

Funding Information:
A. Bakhtyari and M. Mofarahi are sincerely grateful for the sponsorships from Iran's National Elites Foundation and Iran National Science Foundation : INSF (Project No.: 4003871 ). The authors are also sincerely grateful for supporting this research by Persian Gulf University and Yonsei University . This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT ( 2019K1A4A7A03113187 ).

Publisher Copyright:
© 2022

All Science Journal Classification (ASJC) codes

Chemistry(all)
Chemical Engineering(all)
Industrial and Manufacturing Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.ces.2022.118432

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title = "Performance analysis and artificial intelligence modeling for enhanced hydrogen production by catalytic bio-alcohol reforming in a membrane-assisted reactor",

abstract = "Due to the significance of green processing and artificial intelligence (AI) modeling, herein, we proposed a mathematical and an AI model to enhance green hydrogen production from bio-alcohols in a membrane-assisted reactor. A sensitivity analysis for the effective variables was conducted in terms of conversion, thermal behavior, pressure drop, and hydrogen distribution. A single-layer perceptron network with tansig + trainlm functions and eight neurons yielded a 0.72 % error (mean squared error (MSE) = 2.69, R2 = 0.99994) in the bio-methanol reformer, while the same functions with nine neurons presented a 0.22 % error (MSE = 0.32, R2 = ∼1.00000) in the bio-ethanol reformer. Lastly, a multi-objective optimization was performed to determine the optimum operating conditions, which enhanced the hydrogen production in the bio-methanol (yMeOH = 0.4 and 0.204 mol/h at 517 K and 6 bar) and bio-ethanol (yEtOH = 0.3 and 1.8 mol/h at 823 K and 6 bar) reforming.",

author = "Ali Bakhtyari and Roghayeh Bardool and Rahimpour, {Mohammad Reza} and Masoud Mofarahi and Lee, {Chang Ha}",

note = "Funding Information: A. Bakhtyari and M. Mofarahi are sincerely grateful for the sponsorships from Iran's National Elites Foundation and Iran National Science Foundation : INSF (Project No.: 4003871 ). The authors are also sincerely grateful for supporting this research by Persian Gulf University and Yonsei University . This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT ( 2019K1A4A7A03113187 ). Publisher Copyright: {\textcopyright} 2022",

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AU - Mofarahi, Masoud

AU - Lee, Chang Ha

N1 - Funding Information: A. Bakhtyari and M. Mofarahi are sincerely grateful for the sponsorships from Iran's National Elites Foundation and Iran National Science Foundation : INSF (Project No.: 4003871 ). The authors are also sincerely grateful for supporting this research by Persian Gulf University and Yonsei University . This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT ( 2019K1A4A7A03113187 ). Publisher Copyright: © 2022

PY - 2023/3/15

Y1 - 2023/3/15

N2 - Due to the significance of green processing and artificial intelligence (AI) modeling, herein, we proposed a mathematical and an AI model to enhance green hydrogen production from bio-alcohols in a membrane-assisted reactor. A sensitivity analysis for the effective variables was conducted in terms of conversion, thermal behavior, pressure drop, and hydrogen distribution. A single-layer perceptron network with tansig + trainlm functions and eight neurons yielded a 0.72 % error (mean squared error (MSE) = 2.69, R2 = 0.99994) in the bio-methanol reformer, while the same functions with nine neurons presented a 0.22 % error (MSE = 0.32, R2 = ∼1.00000) in the bio-ethanol reformer. Lastly, a multi-objective optimization was performed to determine the optimum operating conditions, which enhanced the hydrogen production in the bio-methanol (yMeOH = 0.4 and 0.204 mol/h at 517 K and 6 bar) and bio-ethanol (yEtOH = 0.3 and 1.8 mol/h at 823 K and 6 bar) reforming.

AB - Due to the significance of green processing and artificial intelligence (AI) modeling, herein, we proposed a mathematical and an AI model to enhance green hydrogen production from bio-alcohols in a membrane-assisted reactor. A sensitivity analysis for the effective variables was conducted in terms of conversion, thermal behavior, pressure drop, and hydrogen distribution. A single-layer perceptron network with tansig + trainlm functions and eight neurons yielded a 0.72 % error (mean squared error (MSE) = 2.69, R2 = 0.99994) in the bio-methanol reformer, while the same functions with nine neurons presented a 0.22 % error (MSE = 0.32, R2 = ∼1.00000) in the bio-ethanol reformer. Lastly, a multi-objective optimization was performed to determine the optimum operating conditions, which enhanced the hydrogen production in the bio-methanol (yMeOH = 0.4 and 0.204 mol/h at 517 K and 6 bar) and bio-ethanol (yEtOH = 0.3 and 1.8 mol/h at 823 K and 6 bar) reforming.

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Performance analysis and artificial intelligence modeling for enhanced hydrogen production by catalytic bio-alcohol reforming in a membrane-assisted reactor

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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