000			04386nam a22003975i 4500
001			280925
003			MX-SnUAN
005			20160429154043.0
007			cr nn 008mamaa
008			150903s2009 xxu| o |||| 0|eng d
020			_a9780817647933 _99780817647933
024	7		_a10.1007/9780817647933 _2doi
035			_avtls000333643
039		9	_a201509030205 _bVLOAD _c201404130505 _dVLOAD _c201404092254 _dVLOAD _y201402041116 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
100	1		_aChristofides, Panagiotis D. _eautor _9305703
245	1	0	_aControl and Optimization of Multiscale Process Systems / _cby Panagiotis D. Christofides, Antonios Armaou, Yiming Lou, Amit Varshney.
264		1	_aBoston : _bBirkhäuser Boston, _c2009.
300			_axx, 212 páginas 100 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aControl Engineering
500			_aSpringer eBooks
505	0		_aMultiscale Process Modeling and Simulation -- Control Using Kinetic Monte Carle Models -- Construction of Stochastic PDEs -- Feedback Control Using Stochastic PDEs -- Optimization of Multiscale Process Systmes -- Dynamic Optimization of Multiscale PDE/kMC Process Systems.
520			_aInterest in the control and optimization of multiscale process systems has been triggered by the need to achieve tight feedback control and optimal operation of complex processes, such as deposition and sputtering of thin films in semiconductor manufacturing, which are characterized by highly coupled macroscopic and microscopic phenomena. Drawing from recent advances in the dynamics and control of distributed parameter processes for which continuum laws are applicable as well as stochastic modeling of phenomena at mesoscopic/microscopic length scales, control and optimization of multiscale process systems has evolved into a very active research area of systems and control engineering. This book—the first of its kind—presents general methods for feedback controller synthesis and optimization of multiscale systems, illustrating their application to thin-film growth, sputtering processes, and catalytic systems of industrial interest. Beginning with an introduction to general issues on control and optimization of multiscale systems and a review of previous work in this area, the book discusses detailed modeling approaches for multiscale processes with emphasis on the theory and implementation of kinetic Monte Carlo simulation, methods for feedback control using kinetic Monte Carlo models, stochastic model construction and parameter estimation, predictive and covariance control using stochastic partial differential equation models, and both steady-state and dynamic optimization algorithms that efficiently address coupled macroscopic and microscopic objectives. Key features of the work: * Demonstrates the advantages of the methods presented for control and optimization through extensive simulations. * Includes new techniques for feedback controller design and optimization of multiscale process systems that are not included in other books. * Illustrates the application of controller design and optimization methods to complex multiscale processes of industrial interest. * Contains a rich collection of new research topics and references to significant recent work. The book requires basic knowledge of differential equations, probability theory, and control theory, and is intended for researchers, graduate students, and process control engineers. Throughout the book, practical implementation issues are addressed to help researchers and engineers understand the development and application of the methods presented in greater depth.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aArmaou, Antonios. _eautor _9306197
700	1		_aLou, Yiming. _eautor _9306198
700	1		_aVarshney, Amit. _eautor _9306199
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780817647926
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-8176-4793-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c280925 _d280925