Please wait a minute...
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2004, Vol. 5 Issue (4): 378-389    DOI: 10.1631/jzus.2004.0378
Bioscience & Biotechnology     
Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms
ANDRÉS-TORO B., GIRÓN-SIERRA J.M., FERNÁNDEZ-BLANCO P., LÓPEZ-OROZCO J.A., BESADA-PORTAS E.
Department of Computer Architecture and System Engineering, Physical Sciences, Complutense University of Madrid, Spain
Download:     PDF (0 KB)     
Export: BibTeX | EndNote (RIS)      

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.

Key wordsMultiobjective optimization      Genetic algorithms      Industrial control      Multivariable control systems      Fermentation processes     
Received: 30 October 2003     
CLC:  Q815  
  TP278  
Cite this article:

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. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2004, 5(4): 378-389.

URL:

http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2004.0378     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2004/V5/I4/378

[1] Jin Cheng, Gui-fang Duan, Zhen-yu Liu, Xiao-gang Li, Yi-xiong Feng, Xiao-hai Chen. Interval multiobjective optimization of structures based on radial basis function, interval analysis, and NSGA-II[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2014, 15(10): 774-788.
[2] Xiao-lei Dong, Sui-qing Liu, Tao Tao, Shu-ping Li, Kun-lun Xin. A comparative study of differential evolution and genetic algorithms for optimizing the design of water distribution systems[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2012, 13(9): 674-686.
[3] Jun-hai SHI, Zhi-dan ZHONG, Xin-jian ZHU, Guang-yi CAO. Robust design and optimization for autonomous PV-wind hybrid power systems[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2008, 9(3): 401-409.
[4] Yi LIU, Ying LI, Yi-jia CAO, Chuang-xin GUO. Forward and backward models for fault diagnosis based on parallel genetic algorithms[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2008, 9(10): 1420-1425.
[5] Giuseppe Carlo MARANO. Reliability based multiobjective optimization for design of structures subject to random vibrations[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2008, 9(1): 15-25.
[6] CHEN Min-rong, LU Yong-zai, YANG Gen-ke. Multiobjective extremal optimization with applications to engineering design[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2007, 8(12): 1905-1911.
[7] LIU Xiang, CHEN Lin, SUN You-xian. A new digital approach to design multivariable robust optimal control systems*[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2005, 6( 7): 16-.
[8] LIU Bin, SU Hong-ye, CHU Jian. New predictive control algorithms based on least squares Support Vector Machines[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2005, 6( 5): 14-.
[9] LIU Ping, CHENG Yi-yu. AN IMPROVED GENETIC ALGORITHM FOR TRAINING LAYERED FEEDFORWARD NEURAL NETWORKS[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2000, 1(3): 322-326.