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

Yuanxin Qi, Martin Andersson, Lei Wang, Jingyu Wang

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

Sammanfattning

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.

Originalspråkengelska
Sidor (från-till)279-289
Antal sidor11
TidskriftFuel Cells
Volym21
Nummer3
Tidigt onlinedatum2021
DOI
StatusPublished - 2021 juni 1

Bibliografisk information

Funding Information:
The authors would like to acknowledge the support from the Chinese Scholarship Council (201706080005) and the Åforsk Project (2017‐331).

Publisher Copyright:
© 2021 Wiley-VCH GmbH

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Ämnesklassifikation (UKÄ)

  • Energiteknik

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