Overview
- Authors:
-
-
Lei Zhi Chen
-
Diagnostics and Control Research Centre, Engineering Research Institute, Auckland University of Technology, Auckland, New Zealand
-
Xiao Dong Chen
-
Department of Chemical and Materials Engineering, The University of Auckland, Auckland, New Zealand
-
Sing Kiong Nguang
-
Department of Electrical and Computer Engineering, The University of Auckland, Auckland, New Zealand
Access this book
Other ways to access
Table of contents (8 chapters)
-
Front Matter
Pages I-VIII
-
-
-
-
-
-
-
-
-
Back Matter
Pages 111-123
About this book
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.
Authors and Affiliations
-
Diagnostics and Control Research Centre, Engineering Research Institute, Auckland University of Technology, Auckland, New Zealand
Lei Zhi Chen
-
Department of Chemical and Materials Engineering, The University of Auckland, Auckland, New Zealand
Xiao Dong Chen
-
Department of Electrical and Computer Engineering, The University of Auckland, Auckland, New Zealand
Sing Kiong Nguang
