Artikel
Entropy generation rate minimization for steam methane reforming reactor heated by molten salt
Traditional steam methane reforming facilities for producing hydrogen not only consume a large amount of natural gas, but also emit a lot of greenhouse gas. Considering the utilization of clean energy, the reduction of carbon dioxide emission, and the production of chemicals, this paper establishes a model of steam methane reforming reactor heated by molten salt by utilizing finite-time thermodynamics. In order to reduce the irreversibility of this reactor, the entropy generation rate minimization and some operating parameters are taken as the optimization objective and variables, respectively. The hybrid particle swarm optimization algorithm is proposed for solving this model. Afterwards, the influences of the porosity of the catalyst bed and catalyst activity on the optimal performance are analyzed. The results indicate that the total entropy generation rate of the reactor is reduced by 22.08% after optimizing the inlet parameters of the molten salt and reaction mixture. Within the scope of this study, the total entropy generation rate of the optimal reactor decreases with the increasing porosity of catalyst bed. When the catalyst activity decreases, the hydrogen production rate can remain constant by adjusting the operating parameters. The obtained results are favorable to guide the optimal design of the practical reactors.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 6 ; Year: 2020 ; Pages: 685-697 ; Amsterdam: Elsevier
- Klassifikation
-
Wirtschaft
- Thema
-
Steam methane reforming
Molten salt
Minimum entropy generation rate
Finite-time thermodynamics
Hybrid particle swarm optimization
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Li, Penglei
Chen, Lingen
Xia, Shaojun
Zhang, Lei
Kong, Rui
Ge, Yanlin
Feng, Huijun
- Ereignis
-
Veröffentlichung
- (wer)
-
Elsevier
- (wo)
-
Amsterdam
- (wann)
-
2020
- DOI
-
doi:10.1016/j.egyr.2020.03.011
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Li, Penglei
- Chen, Lingen
- Xia, Shaojun
- Zhang, Lei
- Kong, Rui
- Ge, Yanlin
- Feng, Huijun
- Elsevier
Entstanden
- 2020
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