000			03481nam a22005175i 4500
001			978-0-85729-262-9
003			DE-He213
005			20251006084443.0
007			cr nn 008mamaa
008			110303s2011 xxk| s |||| 0|eng d
020			_a9780857292629
020			_a99780857292629
024	7		_a10.1007/978-0-85729-262-9 _2doi
082	0	4	_a003.3 _223
100	1		_aMilewski, Jarosław. _eauthor.
245	1	0	_aAdvanced Methods of Solid Oxide Fuel Cell Modeling _h[electronic resource] / _cby Jarosław Milewski, Konrad Świrski, Massimo Santarelli, Pierluigi Leone.
264		1	_aLondon : _bSpringer London, _c2011.
300			_aXIV, 218 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aGreen Energy and Technology, _x1865-3529
505	0		_a1. Introduction -- 2. Theory -- 3. Advanced Methods in Mathematical Modeling -- 4. Experimental Investigation -- 5. SOFC Modeling.
520			_aFuel 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.
650		0	_aMATHEMATICS.
650		0	_aCHEMICAL ENGINEERING.
650		0	_aARTIFICIAL INTELLIGENCE.
650		0	_aPRODUCTION OF ELECTRIC ENERGY OR POWER.
650	1	4	_aMATHEMATICS.
650	2	4	_aMATHEMATICAL MODELING AND INDUSTRIAL MATHEMATICS.
650	2	4	_aINDUSTRIAL CHEMISTRY/CHEMICAL ENGINEERING.
650	2	4	_aPOWER ELECTRONICS, ELECTRICAL MACHINES AND NETWORKS.
650	2	4	_aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
700	1		_aŚwirski, Konrad. _eauthor.
700	1		_aSantarelli, Massimo. _eauthor.
700	1		_aLeone, Pierluigi. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780857292612
830		0	_aGreen Energy and Technology, _x1865-3529
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-85729-262-9 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-ENG
942			_2ddc _cER
999			_c59997 _d59997