Neuro-evolutionary approach applied for optimizing the PEMFC performance |
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| Authors: | Silvia Curteanu Ciprian-George Piuleac Jose J Linares Pablo Cañizares Manuel A Rodrigo Justo Lobato |
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| Affiliation: | 1. “Gheorghe Asachi” Technical University Iasi, Faculty of Chemical Engineering and Environmental Protection, Department of Chemical Engineering, Bd. Prof. dr. doc. D. Mangeron, No. 73, 700050 Iasi, Romania;2. Laboratory of Chemical Processes, Institute of Chemistry, University of Brasilia Campus Universitário Darcy Ribeiro, CP 04478 Brasilia, Brazil;3. Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004 Ciudad Real, Spain |
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| Abstract: | A multi-objective optimization strategy, based on stacked neural network–genetic algorithm (SNN–GA) hybrid approach, was applied to study the C/PBI content on a high temperature PEMFC performance. The operating conditions of PEMFC were correlated with power density and electrochemical active surface area for electrodes. The structure of the stack was determined in an optimal form related to the contribution of individual neural networks, after applying an interpolation based procedure. Multi-objective optimization using SNN as model and GA as solving procedure provides optimal working conditions which lead to a high PEMFC performance. Simulation results were in agreement with experimental data, both for model validation and system optimization (the C/PBI content in the range of 17–21%). |
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| Keywords: | High temperature PEMFCs Stacked neural network Genetic algorithm Optimization |
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