Parallel differential algorithms for fermentation process

Author

Manyri, Laurent ; Doncescu, Andrei ; Roux, Gilles ; Dahhou, Boutaieb

Author_Institution

Lab. d´´Autom. et d´´Anal. des Syst., CNRS, Toulouse, France

fYear

2002

fDate

2002

Firstpage

414

Lastpage

418

Abstract

In biotechnology the estimation of the kinetic parameters needs a lot of approximation due to the non-linearity of the system and to the important number of model parameters. Therefore, the computation time increases with the complexity of the problem. We present the performances of the DE (differential evolution), which is a part of EA (evolutionary algorithms) based on GA (genetic algorithms) applied to estimate the parameters model of the fermentation bioprocess. The master-slave scheme ameliorates the time computation allowing us to know the physiological states of the yeast.

Keywords

biotechnology; fermentation; genetic algorithms; parallel algorithms; parameter estimation; state estimation; biotechnology; differential evolution; evolutionary algorithms; fermentation process; genetic algorithms; kinetic parameters estimation; master-slave scheme; parallel differential algorithms; physiological states; yeast; Biological system modeling; Change detection algorithms; Evolution (biology); Evolutionary computation; Fungi; Master-slave; Optimization methods; Parameter estimation; Robustness; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel Processing Workshops, 2002. Proceedings. International Conference on

ISSN

1530-2016

Print_ISBN

0-7695-1680-7

Type

conf

DOI

10.1109/ICPPW.2002.1039759

Filename

1039759