System behavior prediction by artificial neural network algorithm of a methanol steam reformer for polymer electrolyte fuel cell stack use

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Abstract

In this paper, a novel membrane reactor (MR) for methanol steam reforming is modeled to produce fuel cell grade hydrogen, which can be used as the inlet fuel for a later developed 500-W horizon polymer electrolyte fuel cell (PEFC) stack. The backpropagation (BP) neural network algorithm is employed to develop the mapping relation model between the MR's prime operational parameters and fuel cell output performance for future integration system design and control application. Simulation results showed that the MR model performs well for hydrogen production and the developed PEFC system presents good agreement with experimental results. Finally, the BP method captures an accurate mapping relation model between the MR inputs and PEFC output, for example, predicts the system's behavior well.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • Xi'an University of Science and Technology
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Energiteknik

Nyckelord

Originalspråkengelska
Sidor (från-till)279-289
Antal sidor11
TidskriftFuel Cells
Volym21
Utgåva nummer3
Tidigt onlinedatum2021
StatusPublished - 2021 jun 1
PublikationskategoriForskning
Peer review utfördJa