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Journal of Zhejiang University SCIENCE A 2004 Vol.5 No.12 P.1597-1603

http://doi.org/10.1631/jzus.2004.1597


Ant colony system algorithm for the optimization of beer fermentation control


Author(s):  XIAO Jie, ZHOU Ze-kui, ZHANG Guang-xin

Affiliation(s):  National Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   seanxiaojie@zju.edu.cn

Key Words:  Beer fermentation, Kinetic model, ACS algorithm, Optimization, Optimal temperature profile


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XIAO Jie, ZHOU Ze-kui, ZHANG Guang-xin. Ant colony system algorithm for the optimization of beer fermentation control[J]. Journal of Zhejiang University Science A, 2004, 5(12): 1597-1603.

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Abstract: 
beer fermentation is a dynamic process that must be guided along a temperature profile to obtain the desired results. Ant colony system algorithm was applied to optimize the kinetic model of this process. During a fixed period of fermentation time, a series of different temperature profiles of the mixture were constructed. An optimal one was chosen at last. optimal temperature profile maximized the final ethanol production and minimized the byproducts concentration and spoilage risk. The satisfactory results obtained did not require much computation effort.

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Reference

[1] Andres-Toro, B., Giron-Sierra, J.M., Lopez-Orozco, J.A., Fernandez-Conde, C., 1997a. Application of Genetic Algorithms and Simulations for the Optimization of Batch Fermentation Control. Proc. IEEE International Conference on System, Man, and Cybernetics, ‘Computational Cybernetics and Simulation’, p.392-397.

[2] Andres-Toro, B., Giron-Sierra, J.M., Lopez-Orozco, J.A., Fernandez-Conde, C., 1997b. Using Genetic Algorithms for Dynamic Optimization: An Industrial Fermentation Case. Proceedings of the 36th Conference on Decision & Control. San Diego, California, USA, p.828-829.

[3] Andres-Toro, B., Giron-Sierra, J.M., Lopez-Orozco, J.A., Fernandez-Conde, C., Peinado, J.M., Garcia-Ochoa, F., 1998. A kinetic model for beer production under industrial operational conditions. Mathematics and Computers in Simulation, 48:65-74.

[4] Carrillo-Ureta, G.E., Roberts, P.D., Becerra, V.M., 2001. Genetic Algorithms for Optimal Control of Beer Fermentation. Proceedings of the 2001 IEEE International Symposium on Intelligent Control, Mexico City, Mexico, p.391-396.

[5] Dorigo, M., Gambardella, L.M., 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1):53-66.

[6] Dorigo, M., Maniezzo, V., Colorni, A., 1996. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and CyberneticsPart B: Cybernetics, 26(1):29-41.

[7] Dorigo, M., Gambardella, L.M., Middendorf, M., Stutzle, T., 2002. Guest editorial special section on ant colony optimization. IEEE Transactions on Evolutionary Computation, 6(4):317-319.

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