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

Qi, Yuanxin LU ; Andersson, Martin LU ; Wang, Lei LU and Wang, Jingyu LU (2021) In Fuel Cells 21(3). p.279-289
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... (More)

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.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
backpropagation neural network algorithm, membrane reactor, methanol steam reforming, polymer electrolyte fuel cell
in
Fuel Cells
volume
21
issue
3
pages
11 pages
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:85106744564
ISSN
1615-6846
DOI
10.1002/fuce.202100006
language
English
LU publication?
yes
additional info
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.
id
5d91319d-8670-4d0b-8f98-277fe2c2000d
date added to LUP
2021-06-07 07:14:39
date last changed
2022-04-27 02:14:32
@article{5d91319d-8670-4d0b-8f98-277fe2c2000d,
  abstract     = {{<p>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.</p>}},
  author       = {{Qi, Yuanxin and Andersson, Martin and Wang, Lei and Wang, Jingyu}},
  issn         = {{1615-6846}},
  keywords     = {{backpropagation neural network algorithm; membrane reactor; methanol steam reforming; polymer electrolyte fuel cell}},
  language     = {{eng}},
  month        = {{06}},
  number       = {{3}},
  pages        = {{279--289}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Fuel Cells}},
  title        = {{System behavior prediction by artificial neural network algorithm of a methanol steam reformer for polymer electrolyte fuel cell stack use}},
  url          = {{http://dx.doi.org/10.1002/fuce.202100006}},
  doi          = {{10.1002/fuce.202100006}},
  volume       = {{21}},
  year         = {{2021}},
}