A review of hydrogen production optimization from the reforming of C1 and C2 alcohols via artificial neural networks

Chen, WH; Biswas, PP; Ubando, AT; Kwon, EE; Lin, KYA; Ong, HC

A review of hydrogen production optimization from the reforming of C1 and C2 alcohols via artificial neural networks

Chen, WH Biswas, PP Ubando, AT Kwon, EE Lin, KYA Ong, HC

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Fuel, 2023, 345
Issue Date:: 2023-08-01

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Field	Value	Language
dc.contributor.author	Chen, WH
dc.contributor.author	Biswas, PP
dc.contributor.author	Ubando, AT
dc.contributor.author	Kwon, EE
dc.contributor.author	Lin, KYA
dc.contributor.author	Ong, HC
dc.date.accessioned	2024-03-21T04:54:59Z
dc.date.available	2024-03-21T04:54:59Z
dc.date.issued	2023-08-01
dc.identifier.citation	Fuel, 2023, 345
dc.identifier.issn	0016-2361
dc.identifier.issn	1873-7153
dc.identifier.uri	http://hdl.handle.net/10453/176972
dc.description.abstract	Hydrogen production from different fuels has received extensive study interest owing to its environmental sustainability, renewability, and lack of carbon emission. This research aims to investigate how artificial neural networks (ANNs) are employed to optimize operating parameters for the catalytic thermochemical conversion of methanol and ethanol and their impact on hydrogen production. According to the ANN model, peak methanol conversion (99%) occurs at lower temperatures of 300 °C with a maximum hydrogen yield of 2.905 mol, whereas peak ethanol conversion (85%) occurs at 500 °C owing to dehydrogenation and the C-C bond-breaking process. A steam-to-carbon (S/C) ratio of (3.5) was advantageous for methanol steam reforming (MSR), and a high ethanol concentration of 10–15 vol% was favorable for ethanol steam reforming (ESR). Ni (10 wt%), and Co (10 wt%) were the optimum metal combinations in the catalyst for ethanol reformation at a reforming temperature of 450 °C. The optimum metal catalysts for producing hydrogen and converting ethanol were those synthesized through co-precipitation. The peak hydrogen yield was attained at the sintering temperature of 560–570 °C. ANN technique is cost-effective, quick, and precise, with vast potential to produce hydrogen energy, and may give significant benefits for industrial applications.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Fuel
dc.relation.isbasedon	10.1016/j.fuel.2023.128243
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0306 Physical Chemistry (incl. Structural), 0904 Chemical Engineering, 0913 Mechanical Engineering
dc.subject.classification	Energy
dc.subject.classification	4004 Chemical engineering
dc.subject.classification	4012 Fluid mechanics and thermal engineering
dc.subject.classification	4019 Resources engineering and extractive metallurgy
dc.title	A review of hydrogen production optimization from the reforming of C1 and C2 alcohols via artificial neural networks
dc.type	Journal Article
utslib.citation.volume	345
utslib.for	0306 Physical Chemistry (incl. Structural)
utslib.for	0904 Chemical Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-21T04:54:56Z
pubs.publication-status	Published
pubs.volume	345

Abstract:

Hydrogen production from different fuels has received extensive study interest owing to its environmental sustainability, renewability, and lack of carbon emission. This research aims to investigate how artificial neural networks (ANNs) are employed to optimize operating parameters for the catalytic thermochemical conversion of methanol and ethanol and their impact on hydrogen production. According to the ANN model, peak methanol conversion (99%) occurs at lower temperatures of 300 °C with a maximum hydrogen yield of 2.905 mol, whereas peak ethanol conversion (85%) occurs at 500 °C owing to dehydrogenation and the C-C bond-breaking process. A steam-to-carbon (S/C) ratio of (3.5) was advantageous for methanol steam reforming (MSR), and a high ethanol concentration of 10–15 vol% was favorable for ethanol steam reforming (ESR). Ni (10 wt%), and Co (10 wt%) were the optimum metal combinations in the catalyst for ethanol reformation at a reforming temperature of 450 °C. The optimum metal catalysts for producing hydrogen and converting ethanol were those synthesized through co-precipitation. The peak hydrogen yield was attained at the sintering temperature of 560–570 °C. ANN technique is cost-effective, quick, and precise, with vast potential to produce hydrogen energy, and may give significant benefits for industrial applications.

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http://hdl.handle.net/10453/176972