Neuro-evolutionary approach applied for optimizing the PEMFC performance

被引：8

作者：

Curteanu, Silvia ^{[1
]}

Piuleac, Ciprian-George ^{[1
]}

Linares, Jose J. ^{[2
]}

Canizares, Pablo ^{[3
]}

Rodrigo, Manuel A. ^{[3
]}

Lobato, Justo

机构：

[1] Gheorghe Asachi Tech Univ Iasi, Fac Chem Engn & Environm Protect, Dept Chem Engn, Iasi 700050, Romania

[2] Univ Brasilia, Inst Chem, Lab Chem Proc, BR-04478 Brasilia, DF, Brazil

[3] Univ Castilla La Mancha, Dept Chem Engn, Ciudad Real 13004, Spain

来源：

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY | 2014年 / 39卷 / 08期

关键词：

High temperature PEMFCs; Stacked neural network; Genetic algorithm; Optimization; FUEL-CELL STACK; CATALYTIC LAYER; NETWORK MODEL; PBI; H3PO4;

D O I：

10.1016/j.ijhydene.2013.07.118

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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%). Copyright (C) 2013, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

引用

页码：4037 / 4043

页数：7

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