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Development of a Fast Operation Algorithm of a Small-Scale Fuel Cell System With Solar Reforming / Shin’ya Obara in Transactions of the ASME . Journal of dynamic systems, measurement, and control, Vol. 131 N° 3 (Mai 2009) 

Public ISBD [article]  in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 131 N° 3 (Mai 2009) . - 12 p. Titre : Development of a Fast Operation Algorithm of a Small-Scale Fuel Cell System With Solar Reforming Type de document : texte imprimé Auteurs : Shin’ya Obara, Auteur ; Itaru Tanno, Auteur Année de publication : 2009 Article en page(s) : 12 p. Note générale : dynamic systems Langues : Anglais (eng) Mots-clés : small-scale  bioethanol  steam  reforming  system;  fuel  cell;  genetic  algorithm Résumé : The small-scale bioethanol steam reforming system (FBSR), using sunlight applied to a heat source, is a very clean method, which can supply fuel to a fuel cell. However, it is difficult to analyze the operation planning of this system with high precision. If such an analytical algorithm is developed, the optimum operation of this system will be realized by the command of the control device. However, the difficulty of weather forecasts, such as solar radiation and outside-air-temperature, to date has made it difficult to achieve rapid and highly precise results and to analyze the system operation. In this paper, an algorithm, which analyzes the operation planning of the FBSR on arbitrary days, is developed using the neural network. The weather pattern for the past 1 year is input into this algorithm, and the operation planning of the FBSR, based on the same weather pattern, is given as a training signal. In this paper, the operation results of the system obtained via genetic algorithm (GA) were used as the training signal for the neural network. Operation planning (the amount of hydrogen production and the amount of exhaust heat storage) of the system on arbitrary days could be obtained rapidly by ensuring that input data (the weather and energy-demand patterns) are channeled into the learned neural network following this study. Moreover, in order to investigate the accuracy of the operational analysis via the proposed algorithm, it is compared with the analysis result of operation planning using the GA. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://dynamicsystems.asmedigitalcollection.asme.org/issue.aspx?journalid=117&is [...] [article] Development of a Fast Operation Algorithm of a Small-Scale Fuel Cell System With Solar Reforming [texte imprimé] / Shin’ya Obara, Auteur ; Itaru Tanno, Auteur . - 2009 . - 12 p. dynamic systems Langues : Anglais (eng) in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 131 N° 3 (Mai 2009) . - 12 p. Mots-clés : small-scale  bioethanol  steam  reforming  system;  fuel  cell;  genetic  algorithm Résumé : The small-scale bioethanol steam reforming system (FBSR), using sunlight applied to a heat source, is a very clean method, which can supply fuel to a fuel cell. However, it is difficult to analyze the operation planning of this system with high precision. If such an analytical algorithm is developed, the optimum operation of this system will be realized by the command of the control device. However, the difficulty of weather forecasts, such as solar radiation and outside-air-temperature, to date has made it difficult to achieve rapid and highly precise results and to analyze the system operation. In this paper, an algorithm, which analyzes the operation planning of the FBSR on arbitrary days, is developed using the neural network. The weather pattern for the past 1 year is input into this algorithm, and the operation planning of the FBSR, based on the same weather pattern, is given as a training signal. In this paper, the operation results of the system obtained via genetic algorithm (GA) were used as the training signal for the neural network. Operation planning (the amount of hydrogen production and the amount of exhaust heat storage) of the system on arbitrary days could be obtained rapidly by ensuring that input data (the weather and energy-demand patterns) are channeled into the learned neural network following this study. Moreover, in order to investigate the accuracy of the operational analysis via the proposed algorithm, it is compared with the analysis result of operation planning using the GA. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://dynamicsystems.asmedigitalcollection.asme.org/issue.aspx?journalid=117&is [...]