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Modelling and Optimization of Biotechnological Processes: Artificial Intelligence Approaches
Contributor(s): Chen, Lei Zhi (Author), Nguang, Sing Kiong (Author), Chen, Xiao Dong (Author)

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 ISBN: 3642067921     ISBN-13: 9783642067921
Publisher: Springer
OUR PRICE: $104.49  

Binding Type: Paperback - See All Available Formats & Editions
Published: November 2010
Qty:

Click for more in this series: Studies in Computational Intelligence
Additional Information
BISAC Categories:
- Mathematics | Applied
- Computers | Intelligence (ai) & Semantics
- Science | Life Sciences - Biology
Dewey: 660.6
Series: Studies in Computational Intelligence
Physical Information: 0.28" H x 6.14" W x 9.21" L (0.43 lbs) 123 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
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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