TY  - BOOK
AU  - Milewski,Jarosław
AU  - Świrski,Konrad
AU  - Santarelli,Massimo
AU  - Leone,Pierluigi
ED  - SpringerLink (Online service)
TI  - Advanced Methods of Solid Oxide Fuel Cell Modeling
T2  - Green Energy and Technology,
SN  - 9780857292629
U1  - 003.3 23
PY  - 2011///
CY  - London
PB  - Springer London
KW  - MATHEMATICS
KW  - CHEMICAL ENGINEERING
KW  - ARTIFICIAL INTELLIGENCE
KW  - PRODUCTION OF ELECTRIC ENERGY OR POWER
KW  - MATHEMATICAL MODELING AND INDUSTRIAL MATHEMATICS
KW  - INDUSTRIAL CHEMISTRY/CHEMICAL ENGINEERING
KW  - POWER ELECTRONICS, ELECTRICAL MACHINES AND NETWORKS
KW  - ARTIFICIAL INTELLIGENCE (INCL. ROBOTICS)
N1  - 1. Introduction -- 2. Theory -- 3. Advanced Methods in Mathematical Modeling -- 4. Experimental Investigation -- 5. SOFC Modeling
N2  - Fuel cells are widely regarded as the future of the power and transportation industries. Intensive research in this area now requires new methods of fuel cell operation modeling and cell design. Typical mathematical models are based on the physical process description of fuel cells and require a detailed knowledge of the microscopic properties that govern both chemical and electrochemical reactions. Advanced Methods of Solid Oxide Fuel Cell Modeling proposes the alternative methodology of generalized artificial neural networks (ANN) solid oxide fuel cell (SOFC) modeling. Advanced Methods of Solid Oxide Fuel Cell Modeling provides a comprehensive description of modern fuel cell theory and a guide to the mathematical modeling of SOFCs, with particular emphasis on the use of ANNs. Up to now,  most of the equations involved in SOFC models have required the addition of numerous factors that are difficult to determine. The artificial neural network (ANN) can be applied to simulate an object's behavior without an algorithmic solution, merely by utilizing available experimental data. The ANN methodology discussed in Advanced Methods of Solid Oxide Fuel Cell Modeling can be used by both researchers and professionals to optimize SOFC design. Readers will have access to detailed material on universal fuel cell modeling and design process optimization, and will also be able to discover comprehensive information on fuel cells and artificial intelligence theory
UR  - http://dx.doi.org/10.1007/978-0-85729-262-9
ER  -