System behavior prediction by artificial neural network algorithm of a methanol steam reformer for polymer electrolyte fuel cell stack use
(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
- Qi, Yuanxin LU ; Andersson, Martin LU ; Wang, Lei LU and Wang, Jingyu LU
- organization
- publishing date
- 2021-06-01
- 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}}, }