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Deep learning design of functionally graded porous electrode of proton exchange membrane fuel cells

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journal contribution
posted on 2024-01-12, 11:01 authored by Xin TaiXin Tai, Lei Xing, Steven ChristieSteven Christie, Jin Xuan
For the next generation of proton exchange membrane (PEM) fuel cells, the conventional electrode with uniform distribution of functional components is urged to be replaced by functional graded electrode for the prominent performance, efficiency and avoid exacerbated catalyst cost. Due to the complex and non-linear behaviours of PEM fuel cell system, rapid and effective computational model and optimisation algorithm are required to handle such a complex relationship between electrode design parameters and cell performance. In this work, a multi-physics model with multi-directionally graded electrode is developed, in which a deep machine learning approach is embedded, to create a surrogate model for multi-objective optimisation empowered by non-dominated sort genetic algorithm (NSGA-II). A robust prediction deep neural network model with the mean square error lower than 0.01 is obtained from training and then coupling with NSGA-II to evaluate and optimise the fuel cell performances and cost. Remarkably, the Pareto front is successfully defining the trade-off relationship between the objectives where it aids to identify an optimum point where it satisfies the cost effectiveness while maintaining relatively high cell performances. Our work presents a promising strategy to optimise the fuel cell system with underlying interaction and allow rapid and accurate prediction and optimisation.

Funding

Biomanufacturing with carbon capture and utilisation: A Zero Carbon Loss System

Engineering and Physical Sciences Research Council

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Digital Circular Electrochemical Economy (DCEE)

Engineering and Physical Sciences Research Council

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering
  • Science

Department

  • Chemistry
  • Chemical Engineering

Published in

Energy

Volume

283

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2023-07-15

Publication date

2023-07-17

Copyright date

2023

ISSN

0360-5442

eISSN

1873-6785

Language

  • en

Depositor

Prof Steven Christie. Deposit date: 11 January 2024

Article number

128463