Modelling and optimization of biotechnological processes : artificial intelligence approaches / Lei Zhi Chen, Sing Kiong Nguang, Xiao Dong Chen
- 作者： Chen, Lei Zhi.
- 其他作者：
- 其他題名：
- Studies in computational intelligence ;
- 出版： Berlin : Springer c2006.
- 叢書名： Studies in computational intelligence ;v. 15
- 主題： Artificial intelligence--Biological applications , Biotechnology--Data processing.
- ISBN： 354030634X (hbk.) :: NT3.298
- 書目註：Includies bibliographical references.
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讀者標籤：
- 系統號： 005117493 | 機讀編目格式
館藏資訊
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.