Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms

Angelova, Maria, Tzonkov, Stoyan, and Pencheva, Tania. "Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms." Serdica Journal of Computing 4.1 (2010): 11-18. <http://eudml.org/doc/11371>.

@article{Angelova2010,
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.* This work is partly supported by the National Science Fund Project MI – 1505/2005.},
author = {Angelova, Maria, Tzonkov, Stoyan, Pencheva, Tania},
journal = {Serdica Journal of Computing},
keywords = {Genetic Algorithms; Parameter Identification; Fed-Batch Cultivation of S. Cerevisiae; genetic algorithms; parameter identification; fed-batch cultivation of s. cerevisiae},
language = {eng},
number = {1},
pages = {11-18},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms},
url = {http://eudml.org/doc/11371},
volume = {4},
year = {2010},
}

TY - JOUR
AU - Angelova, Maria
AU - Tzonkov, Stoyan
AU - Pencheva, Tania
TI - Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms
JO - Serdica Journal of Computing
PY - 2010
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 4
IS - 1
SP - 11
EP - 18
AB - 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.* This work is partly supported by the National Science Fund Project MI – 1505/2005.
LA - eng
KW - Genetic Algorithms; Parameter Identification; Fed-Batch Cultivation of S. Cerevisiae; genetic algorithms; parameter identification; fed-batch cultivation of s. cerevisiae
UR - http://eudml.org/doc/11371
ER -