Prediction and analysis of the cathode catalyst layer performance of proton exchange membrane fuel cells using artificial neural network and statistical methods

Journal of Power Sources

Volumn 196, Issue 8, 2011, Pages 3750-3756

  Khajeh Hosseini Dalasm, N ^a   Ahadian, S ^a,b   Fushinobu, K ^a   Okazaki, K ^a   Kawazoe, Y ^b  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^b TOHOKU UNIVERSITY   (Japan)

Author keywords

Agglomerate model; Analysis of means; Analysis of variance; Artificial neural network; Catalyst layer; Proton exchange membrane fuel cell

Indexed keywords

ACTIVATION OVERPOTENTIAL; AGGLOMERATE MODEL; ANALYSIS OF MEANS; ARTIFICIAL NEURAL NETWORK; CARBON LOADINGS; CATALYST LAYER; CATHODE CATALYST LAYERS; GAS DIFFUSION LAYERS; PREDICTION AND ANALYSIS; SENSITIVITY FACTORS; STATISTICAL ANALYSIS; STRUCTURAL PARAMETER; TRAINED NEURAL NETWORKS;

ACTIVATION ANALYSIS; CATALYSTS; DIFFUSION IN GASES; MATHEMATICAL MODELS; MEMBRANES; NETWORK LAYERS; NEURAL NETWORKS; PLATINUM; PROTON EXCHANGE MEMBRANE FUEL CELLS (PEMFC); PROTONS; REGRESSION ANALYSIS; STATISTICS;

ANALYSIS OF VARIANCE (ANOVA);

EID: 79751534610     PISSN: 03787753     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jpowsour.2010.12.061     Document Type: Article

References (21)

1. J. Xhang PEM Fuel Cell Electrocatalysts and Catalyst Layers: Fundamentals and Applications 2008 Springer London
2. W.Y. Lee, G.G. Park, T.H. Yang, Y.G. Yoon, and C.S. Kim Int. J. Hydrogen Energy 29 2004 961 966
3. S.J. Wu, S.W. Shiah, and W.L. Yu Renew. Energy 34 2009 134 144
4. J. Lobato, P. Canizares, M.A. Rodrigo, J.J. Linares, C.G. Piuleac, and S. Curteanu J. Power Sources 192 2009 190 192
5. C. Marr, and X. Li J. Power Sources 77 1999 17 27
6. W. Sun, B.A. Peppley, and K. Karan Electrochim. Acta 50 2005 3359 3374
7. N.S. Sisworahardjo, T. Yalcinoz, M.Y. El-Sharkh, and M.S. Alam Int. J. Hydrogen Energy 35 2010 9104 9109
8. N. Khajeh-Hosseini-Dalasm, M.J. Kermani, D. Ghadiri Moghaddam, and J.M. Stockie Int. J. Hydrogen Energy 35 2010 2417 2427
9. W. McCulloch, and W. Pitts Bull. Math. Biophys. 5 1943 115 133
10. T. Hastie, R. Tibishrani, and J. Freidman The Elements of Statistical Learning: Data Mining, Inference and Prediction 2001 Springer New York
11. M.T. Hagan, H.B. Demuth, and M. Beale Neural Network Design 1996 PWS Boston
12. K. Hornik, M. Stinchcombe, and H. White Neural Networks 2 1989 359 366
13. N.R. Draper, and H. Smith Applied Regression Analysis 1998 Wiley NewYork
14. S. Haykin Neural Networks: A Comprehensive Foundation 1999 Prentice Hall New Jersey
15. D.C. Montgomery, E.A. Peck, and G.G. Vining Introduction to Linear Regression Analysis 2006 Wiley-Interscience Hoboken
16. S. Ahadian, H. Mizuseki, and Y. Kawazoe Microfluid. Nanofluid. 9 2010 319 328
17. M. Secandel, K. Karan, A. Suleman, and N. Djilali Electrochim. Acta 52 2007 6318 6337
18. S. Kamarajugadda, and S. Mazumder J. Power Sources 183 2008 629 642
19. J.M. Song, S.Y. Cha, and W.M. Lee J. Power Sources 94 2001 78 84
20. K.M. Yin J. Electrochem. Soc. 153 2005 A583 A593
21. D. Song, Q. Wang, Z.S. Liu, and C. Huang J. Power Sources 159 2006 928 942