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Modelling and Optimization of Biotechnological Processes

Artificial Intelligence Approaches. 'Studies in Computational Intelligence'. Auflage 2006. Book. Sprache:…
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Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe major di?culties of applying advanced control theories is the highly nonlinear nature of the processes. This book examines approaches based on arti?cial intelligencemethods,inpart... weiterlesen
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Produktdetails
Titel: Modelling and Optimization of Biotechnological Processes
Autor/en: Lei Zhi Chen, Xiao Dong Chen, Sing Kiong Nguang

ISBN: 354030634X
EAN: 9783540306344
Artificial Intelligence Approaches.
'Studies in Computational Intelligence'.
Auflage 2006.
Book.
Sprache: Englisch.
Springer Berlin Heidelberg

12. Januar 2006 - gebunden - 132 Seiten

Beschreibung

Mostindustrialbiotechnologicalprocessesareoperatedempirically.Oneofthe major di?culties of applying advanced control theories is the highly nonlinear nature of the processes. This book examines approaches based on arti?cial intelligencemethods,inparticular,geneticalgorithmsandneuralnetworks,for monitoring, modelling and optimization of fed-batch fermentation processes. The main aim of a process control is to maximize the ?nal product with minimum development and production costs. This book is interdisciplinary in nature, combining topics from biotechn- ogy, arti?cial intelligence, system identi?cation, process monitoring, process modelling and optimal control. Both simulation and experimental validation are performed in this study to demonstrate the suitability and feasibility of proposed methodologies. An online biomass sensor is constructed using a - current neural network for predicting the biomass concentration online with only three measurements (dissolved oxygen, volume and feed rate). Results show that the proposed sensor is comparable or even superior to other sensors proposed in the literature that use more than three measurements. Biote- nological processes are modelled by cascading two recurrent neural networks. It is found that neural models are able to describe the processes with high accuracy. Optimization of the ?nal product is achieved using modi?ed genetic algorithms to determine optimal feed rate pro?les. Experimental results of the corresponding production yields demonstrate that genetic algorithms are powerful tools for optimization of highly nonlinear systems. Moreover, a c- bination of recurrentneural networks and genetic algorithms provides a useful and cost-e?ective methodology for optimizing biotechnological processes.

Inhaltsverzeichnis

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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