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Journal of Zhejiang University SCIENCE A 2004 Vol.5 No.4 P.378~389

http://doi.org/10.1631/jzus.2004.0378


Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms


Author(s):  ANDRÉ,S-TORO B., GIRÓ,N-SIERRA J.M., FERNÁ,NDEZ-BLANCO P., LÓ,PEZ-OROZCO J.A., BESADA-PORTAS E.

Affiliation(s):  Department of Computer Architecture and System Engineering, Physical Sciences, Complutense University of Madrid, Spain

Corresponding email(s):   deandres@dacya.ucm.es, pfernandez@cesfelipesegundo.com

Key Words:  Multiobjective optimization, Genetic algorithms, Industrial control, Multivariable control systems, Fermentation processes


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ANDRÉS-TORO B., GIRÓN-SIERRA J.M., FERNÁNDEZ-BLANCO P., LÓPEZ-OROZCO J.A., BESADA-PORTAS E.. Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms[J]. Journal of Zhejiang University Science A, 2004, 5(4): 378~389.

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pages="378~389",
year="2004",
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doi="10.1631/jzus.2004.0378"
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Abstract: 
This paper describes empirical research on the model, optimization and supervisory control of beer fermentation. Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results. The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs). Successful finding of optimal ways to drive these processes were reported. Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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