Using genetic algorithms for dynamic optimization: an industrial fermentation case

Author

De Andres-Toro, B. ; Giron-Sierra, J.M. ; Lopez-Orozco, J.A. ; Fernandez-Conde, C.

Author_Institution

Dept. de Inf. y Autom., Univ. Complutense de Madrid, Spain

Volume

1

fYear

1997

fDate

10-12 Dec 1997

Firstpage

828

Abstract

The research deals with the dynamic optimization of the beer batch fermentation, obtaining a temperature profile to drive the process along an optimal trajectory. We discretize the temperature profiles in a form of chromosomes, and genetic algorithms are applied to search for the best solution. An objective function is established, and employed as fitting function along the evolutionary optimization. Satisfactory results are obtained requiring not much computation effort, and a fine discretization of the solution achieved

Keywords

batch processing (industrial); control system analysis computing; fermentation; genetic algorithms; process control; search problems; temperature control; batch fermentation; beer making; dynamic optimization; fitting function; genetic algorithms; objective function; search problem; temperature profiles; Acceleration; Biological cells; Computer aided software engineering; Drives; Electronic mail; Ethanol; Genetic algorithms; Production; Sugar industry; Temperature dependence;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on

Conference_Location

San Diego, CA

ISSN

0191-2216

Print_ISBN

0-7803-4187-2

Type

conf

DOI

10.1109/CDC.1997.650742

Filename

650742