Neural network hybrid model of a direct internal reforming solid oxide fuel cell
- Autor
- CHAICHANA, Kattiyapon1 ; PATCHARAVORACHOT, Yaneeporn2 ; CHUTICHAI, Bhawasut1 ; SAEBEA, Dang1 ; ASSABUMRUNGRAT, Suttichai1 ; ARPORNWICHANOP, Amornchai1 3
[1] Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
[2] School of Chemical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand
[3] Computational Process Engineering, Chulalongkorn University, Bangkok 10330, Thailand - Fuente
International journal of hydrogen energy. 2012, Vol 37, Num 3, pp 2498-2508, 11 p ; ref : 35 ref
- CODEN
- IJHEDX
- ISSN
- 0360-3199
- Campo Científico
- General chemistry, physical chemistry; Energy
- Editor
- Elsevier, Kidlington
- País de la publicación
- United Kingdom
- Tipo de documento
- Article
- Idioma
- English
- Palabra clave de autor
- Direct internal reforming Hybrid model Neural network Performance analysis Solid oxide fuel cell
- Palabra clave (in)
- Clasificación
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D06 Energy / 001D06D Energy. Thermal use of fuels / 001D06D03 Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc / 001D06D03E Fuel cells
- Disciplina
- Energy
- Procedencia
- Inist-CNRS
- Base de datos
- PASCAL
- Identificador INIST
- 25466754
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1016/j.ijhydene.2011.10.051 
https://dx.doi.org/10.1016/j.ijhydene.2011.10.051
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