Artikel

Improved grass fibrous root algorithm for exergy optimization of a high-temperature PEMFC

This paper presents an exergy assessment for a proposed power generation system that is used the organic Rankine cycle to recycle the waste heat from a high-temperature proton exchange membrane fuel cell (HT-PEMFC). To do so, mathematical model for the studied PEMFC along with the water management system have been introduced. Parametric analysis has been directed to study the impact of different economic and thermodynamic parameters, like the fuel cell irreversibility, exergy efficiency, and its work. For optimal designing the PEMFC, its parameters have been optimized by considering three objective functions, i.e. irreversibility, exergy efficiency, and its work. The optimization process has been performed based on a new model of fibrous root optimization algorithm improved. Simulation results of the presented algorithm have been compared with empirical results, genetic algorithm, and the basic of fibrous root optimization algorithm. The optimized values of irreversibility, exergy efficiency, work for the proposed algorithm are achieved 0.012, -0.439, and -0.4993, respectively which has the best values compared with the other analyzed algorithms.

Sprache
Englisch

Erschienen in
Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 6 ; Year: 2020 ; Pages: 1328-1337 ; Amsterdam: Elsevier

Klassifikation
Wirtschaft
Thema
Improved
Exergy analysis
Proton exchange membrane fuel cell
Grass fibrous root algorithm
Irreversibility

Ereignis
Geistige Schöpfung
(wer)
Lu, Xiaohui
Ren, Jianglin
Guo, Lin
Wang, Peifang
Yousefi, Nasser
Ereignis
Veröffentlichung
(wer)
Elsevier
(wo)
Amsterdam
(wann)
2020

DOI
doi:10.1016/j.egyr.2020.05.011
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Lu, Xiaohui
  • Ren, Jianglin
  • Guo, Lin
  • Wang, Peifang
  • Yousefi, Nasser
  • Elsevier

Entstanden

  • 2020

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