Modelling and Optimization of Biotechnological Processes

Modelling and Optimization of Biotechnological Processes
Author :
Publisher : Springer
Total Pages : 129
Release :
ISBN-10 : 9783540324935
ISBN-13 : 3540324933
Rating : 4/5 (933 Downloads)

Book Synopsis Modelling and Optimization of Biotechnological Processes by : Lei Zhi Chen

Download or read book Modelling and Optimization of Biotechnological Processes written by Lei Zhi Chen and published by Springer. This book was released on 2007-07-04 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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