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|a 9783540324935
|9 978-3-540-32493-5
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|a 10.1007/978-3-540-32493-5
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|a TA329-348
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|a 620
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|a Chen, Lei Zhi.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Modelling and Optimization of Biotechnological Processes
|h [electronic resource] :
|b Artificial Intelligence Approaches /
|c by Lei Zhi Chen, Sing Kiong Nguang, Xiao Dong Chen.
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|a 1st ed. 2006.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2006.
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|a VIII, 123 p.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
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|a online resource
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|a text file
|b PDF
|2 rda
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|a Studies in Computational Intelligence,
|x 1860-9503 ;
|v 15
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|a Optimization 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.
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|a This 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.
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|a Engineering mathematics.
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|a Engineering-Data processing.
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|a Artificial intelligence.
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|a Biomedical engineering.
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|a Bioinformatics.
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|a Mathematical and Computational Engineering Applications.
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|a Artificial Intelligence.
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|a Biomedical Engineering and Bioengineering.
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|a Bioinformatics.
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|a Nguang, Sing Kiong.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Chen, Xiao Dong.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9783642067921
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|i Printed edition:
|z 9783540818281
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|i Printed edition:
|z 9783540306344
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|a Studies in Computational Intelligence,
|x 1860-9503 ;
|v 15
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|u https://doi.uam.elogim.com/10.1007/978-3-540-32493-5
|z Texto Completo
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|a ZDB-2-ENG
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912 |
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|a ZDB-2-SXE
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|a Engineering (SpringerNature-11647)
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950 |
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|a Engineering (R0) (SpringerNature-43712)
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