Advanced Methods of Solid Oxide Fuel Cell Modeling [electronic resource] / by Jarosław Milewski, Konrad Świrski, Massimo Santarelli, Pierluigi Leone.

By: Milewski, Jarosław [author.]Contributor(s): Świrski, Konrad [author.] | Santarelli, Massimo [author.] | Leone, Pierluigi [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Green Energy and Technology: Publisher: London : Springer London, 2011Description: XIV, 218 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780857292629Subject(s): Mathematics | Chemical engineering | Artificial intelligence | Production of electric energy or power | Mathematics | Mathematical Modeling and Industrial Mathematics | Industrial Chemistry/Chemical Engineering | Power Electronics, Electrical Machines and Networks | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 003.3 LOC classification: TA342-343Online resources: Click here to access online
Contents:
1. Introduction -- 2. Theory -- 3. Advanced Methods in Mathematical Modeling -- 4. Experimental Investigation -- 5. SOFC Modeling.
In: Springer eBooksSummary: 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.
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1. Introduction -- 2. Theory -- 3. Advanced Methods in Mathematical Modeling -- 4. Experimental Investigation -- 5. SOFC Modeling.

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.

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