Modelling and Optimization of Biotechnological Processes: Artificial Intelligence Approaches

by Lei Zhi Chen, Sing Kiong Nguang, Xiao Dong Chen

Overview

Most industrial biotechnological processes are operated empirically. One of the major difficulties of applying advanced control theories is the highly nonlinear nature of the processes. This book examines approaches based on artificial intelligence methods, in particular, genetic algorithms and neural networks, for monitoring, modelling and optimization of fed-batch fermentation processes. The main aim of a process control is to maximize the final product with minimum development and production costs. This book is interdisciplinary in nature, combining topics from biotechn- ogy, artificial intelligence, system identification, 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 final product is achieved using modified genetic algorithms to determine optimal feed rate profiles. 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-effective methodology for optimizing biotechnological processes.

Product Details

ISBN-13:	9783540306344
Publisher:	Springer Berlin Heidelberg
Publication date:	02/27/2006
Series:	Studies in Computational Intelligence , #15
Edition description:	2006
Pages:	123
Product dimensions:	6.10(w) x 9.25(h) x 0.01(d)

Table of Contents

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.