Comparison of State-of-The-Art Machine Learning Algorithms and Data-Driven Optimization Methods for Mitigating Nitrogen Crossover in Pem Fuel Cells-Reference-Cited by-同舟云学术

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Comparison of State-of-The-Art Machine Learning Algorithms and Data-Driven Optimization Methods for Mitigating Nitrogen Crossover in Pem Fuel Cells

Published:2021 Issue: Volume: Page:
ISSN:1556-5068
Container-title:SSRN Electronic Journal
language:en
Short-container-title:SSRN Journal

Author:

Liu Jianguo,wang jiankang,ding rui,cao feng,li jia,Dong Hui,shi tao,xing lei

Publisher

Elsevier BV

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference42 articles.

1. A simple, analytic model of polymer electrolyte membrane fuel cell anode recirculation at operating power including nitrogen crossover;K Promislow;Journal of Power Sources,2011

2. Local potential evolutions during proton exchange membrane fuel cell operation with dead-ended anode -Part II: Aging mitigation strategies based on water management and nitrogen crossover;S Abbou;Journal of Power Sources,2017

3. The use of on-line hydrogen sensor for studying inert gas effects and nitrogen crossover in PEMFC system;H Karim�ki;International Journal of Hydrogen Energy,2011

4. Measurement of gas permeability in SPE membranes for use in fuel cells;D Bessarabov;Membrane Technology,2007

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