Genetic Algorithms Parameter Identification Fed-Batch Cultivation of S. Cerevisiae
Issue Date:
2010
Publisher:
Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation:
Serdica Journal of Computing, Vol. 4, No 1, (2010), 11p-18p
Abstract:
Fermentation processes as objects of modelling and high-quality
control are characterized with interdependence and time-varying of process
variables that lead to non-linear models with a very complex structure. This
is why the conventional optimization methods cannot lead to a satisfied
solution. As an alternative, genetic algorithms, like the stochastic global
optimization method, can be applied to overcome these limitations. The
application of genetic algorithms is a precondition for robustness and reaching
of a global minimum that makes them eligible and more workable for
parameter identification of fermentation models. Different types of genetic
algorithms, namely simple, modified and multi-population ones, have been
applied and compared for estimation of nonlinear dynamic model parameters
of fed-batch cultivation of S. cerevisiae.