Leizhi Chen, Sing Kiong Nguang, X. D. Chen

Modelling and Optimization of Biotechnological Processes

Artificial Intelligence Approaches

• Gebundenes Buch

Produktbeschreibung

- Introduction

- Optimization of Fed-batch Culture

- On-line Identi¯cation and Optimization

- On-line Softsensor Development

- Optimization based on Neural Models

- Experimental Validation of Neural Models

- Designing and Implementing Optimal Control.

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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,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 andfeed 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.

Produktdetails

• Studies in Computational Intelligence 15
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 11550952, 978-3-540-30634-4
• 2006
• Seitenzahl: 132
• Erscheinungstermin: 12. Januar 2006
• Englisch
• Abmessung: 241mm x 160mm x 12mm
• Gewicht: 322g
• ISBN-13: 9783540306344
• ISBN-10: 354030634X
• Artikelnr.: 20743167

Autorenporträt

Lei Zhi Chen, University of Auckland, New Zealand / Sing Kiong Nguang, University of Auckland, New Zealand / Xiao Dong Chen, University of Auckland, New Zealand

Inhaltsangabe

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.