000			03662nam a22005535i 4500
001			978-3-540-32493-5
003			DE-He213
005			20161121231117.0
007			cr nn 008mamaa
008			100301s2006 gw | s |||| 0|eng d
020			_a9783540324935 _9978-3-540-32493-5
024	7		_a10.1007/978-3-540-32493-5 _2doi
050		4	_aTA329-348
050		4	_aTA640-643
072		7	_aTBJ _2bicssc
072		7	_aMAT003000 _2bisacsh
082	0	4	_a519 _223
100	1		_aChen, Lei Zhi. _eauthor.
245	1	0	_aModelling and Optimization of Biotechnological Processes _h[electronic resource] : _bArtificial Intelligence Approaches / _cby Lei Zhi Chen, Xiao Dong Chen, Sing Kiong Nguang.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2006.
300			_aVIII, 123 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Computational Intelligence, _x1860-949X ; _v15
505	0		_aOptimization of Fed-batch Culture of Hybridoma Cells using Genetic Algorithms -- On-line Identification and Optimization of Feed Rate Profiles for Fed-batch Culture of Hybridoma Cells -- On-line Softsensor Development for Biomass Measurements using Dynamic Neural Networks -- Optimization of Fed-batch Fermentation Processes using Genetic Algorithms based on Cascade Dynamic Neural Network Models -- Experimental Validation of Cascade Recurrent Neural Network Models -- Designing and Implementing Optimal Control of Fed-batch Fermentation Processes -- Conclusions.
520			_aThis book presents logical approaches to monitoring, modelling and optimization of fed-batch fermentation processes based on artificial intelligence methods, in particular, neural networks and genetic algorithms. Both computer simulation and experimental validation are demonstrated in this book. The approaches proposed in this book can be readily adopted for different processes and control schemes to achieve maximum productivity with minimum development and production costs. These approaches can eliminate the difficulties of having to specify completely the structures and parameters of highly nonlinear bioprocess models. The book begins with a historical introduction to the field of bioprocess control based on artificial intelligence approaches, followed by two chapters covering the optimization of fed-batch culture using genetic algorithms. Online biomass soft-sensors are constructed in Chapter 4 using recurrent neural networks. The bioprocess is then modelled in Chapter 5 by cascading two soft-sensor neural networks. Optimization and validation of the final product are detailed in Chapters 6 and 7. The general conclusions are drawn in Chapter 8.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aBioinformatics.
650		0	_aApplied mathematics.
650		0	_aEngineering mathematics.
650		0	_aBiomedical engineering.
650	1	4	_aEngineering.
650	2	4	_aAppl.Mathematics/Computational Methods of Engineering.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aBiomedical Engineering.
650	2	4	_aBioinformatics.
700	1		_aChen, Xiao Dong. _eauthor.
700	1		_aNguang, Sing Kiong. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783540306344
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v15
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-540-32493-5
912			_aZDB-2-ENG
950			_aEngineering (Springer-11647)
999			_c508745 _d508745