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 process
