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Journal of Zhejiang University-SCIENCE A

, Volume 5, Issue 4, pp 378–389 | Cite as

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. 
Bioscience & Biotechnology
  • 468 Downloads

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 words

Multiobjective optimization Genetic algorithms Industrial control Multivariable control systems Fermentation processes 

Document code

CLC number

Q815 TP278 

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References

  1. Andrés Toro, B. De, 1996. Modelización, Optimizacióny Control de Un Proceso Cervecero Industrial. PhD., Univ. Complutense de Madrid, Spain.Google Scholar
  2. Andrés Toro, B. De, Cámara Hurtado, M., Díez Marqués, C., Fernández Conde, C., Girón Sierra, J.M., Torija Isasa, M.T., 1997. Evolución del contenido de azúcares durante la fermentación cervecera industrial.Alimentación, equiposy tecnología,4: 59–71.Google Scholar
  3. Andrés Toro, B. De, Girón-Sierra, J.M., López Orozco, J.A., Peinado, J.M., García Ochoa, F., 1998a. A kinetic model for beer fermentation under industrial operational conditions.Mathematics and Computer Simulation,48:65–74.CrossRefGoogle Scholar
  4. Andrés Toro, B. De, Girón-Sierra, J.M., López Orozco, J.A., Fernández Conde, C., Fernández Blanco, P., 1998b. A Fast Genetic Optimization for Batch Fermentation Processes. Proc. 7th Intl. Conf. on Computer Applications in Biotechnology CAB7, IFAC, Osaka, Japan, p. 61–66.Google Scholar
  5. Andrés Toro, B. De, Girón-Sierra, J.M., Torija Isasa, M.T. and Cámara Hurtado, M., 1999a. Modelizacióny Control de la fermentación industrial de la Cerveza. Estudio experimental.Alimentación, Equiposy Tecnología,4: 93–99.Google Scholar
  6. Andrés Toro, B. De. Girón-Sierra, J.M., López Orozco, J.A., Fernández Blanco, P., 1999b. A Genetic Optimization Method for Dynamic Processes. The 14th World Congress IFAC, Pergamon Ed., Beijing, China.Google Scholar
  7. Bastin, G., Dochain, D., 1986. On-line Estimation of microbial specific growth rates.Automatica,22(6): 705–709.CrossRefMATHGoogle Scholar
  8. Bastin, G., Dochain, D., 1990. On-line Estimation and Adaptative Control of Bioreactors. Elsevier, Amsterdam.Google Scholar
  9. Besada Portas, E., López Orozco, J.A., Andrés Toro, B. De, 2002. A Versatile Toolbox for Solving Industrial Problems with Several Evolutionary Techniques.In: Evolutionary Methods for Design, Optimization and Control, Ed. International Centre for Numerical Methods in Engineering (CIMNE), Barcelona, Spain.Google Scholar
  10. Carrillo, G.E., 1999. Optimal Control of Fermentation Process. PhD, Control Engineering Research Centre, London.Google Scholar
  11. Cheruy, A., 2000. Control du Profil Arôme de la Bière Lors de la Fermentation Alcoolique. PhD, Laboratoire d'Automatique de Grenoble.Google Scholar
  12. Coello, C.A., 2000. An updated survey of GA-based multiobjective optimization techniques.ACM Computing Surveys,32: 109–143.CrossRefGoogle Scholar
  13. Dochain, D., Bastin, G., 1984. Adaptive identification and control algorithms for nonlinear bacterial growth systems.Automatica,20(5): 621–634.CrossRefMATHGoogle Scholar
  14. Engasser, J.M., Marc, I., Moll, M., Duteurtre, B., 1981. Proceedings EBC Congress, p. 579–583.Google Scholar
  15. Fonseca, C.M., Fleming, P.J., 1998. Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithm—Part I: Unified Formulation.In: IEEE Transactions on Systems, Man, and Cybernetics. Part A: Systems and Humans,28(1): 3–18.Google Scholar
  16. Gauthier, J.P., Hammouri, H., Othman, S., 1992. A simple observer for nonlinear systems: applications to Bioreactors.IEEE T. Autom. Control,37(6): 875–880.MathSciNetCrossRefMATHGoogle Scholar
  17. Gee, D.A., Ramírez, F.W., 1988. Optimal temperature control for batch beer fermentation.Biotech. and Bioeng.,31: 224–234.CrossRefGoogle Scholar
  18. Gee, D.A., Ramírez, F.W., 1994. A flavour model for beer fermentation.J. Ins. Brewing,100: 321–329.CrossRefGoogle Scholar
  19. Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Co., Inc. Redwood City, Ca.MATHGoogle Scholar
  20. Hough, J.S., Briggs, D.E., Stevens, R., 1971. Malting and Brewing Science, Chapman & Hall.Google Scholar
  21. Johnson, A., 1987. The control of fed-batch fermentation processes—a survey.Automatica,23(6): 691–705.CrossRefMATHGoogle Scholar
  22. Knowles, J.D., Corne, D.W., 2000. Approximating the nondominated front using the pareto archived evolution strategy.IEEE Evolutionary Computation,8(2): 149–172.CrossRefGoogle Scholar
  23. Meilgaard, M.C., Reid, D.S., Wyborski, K.A., 1982. Reference Standards for beer flavor terminology systems.Journal of American Society of Brewing Chemist,40: 119–128.Google Scholar
  24. Michalewicz, Z., 1999. Genetic Algoritm+Data Structures =Evolution Programs. Springer-Verlag, Berlin.Google Scholar
  25. Miettinen, K.M., 1999. Nonlinear Multiobjective Optimization. Academic Publishers, Kluwer.MATHGoogle Scholar
  26. Moscato, P., 1989. On Evolution, Search, Optimization, Genetic Algorithms and Material Arts: towards Memetic Algorithms.In: Technical Report Computation Program. Californian Institute of Technology, U.S.A.Google Scholar
  27. Park, S., Ramirez, W.F., 1988. Optimal production of secreted protein in fed-batch reactors.AIChE Journal,34: 1550–1558.CrossRefGoogle Scholar
  28. Sonnleitner, B., Kappeli, O., 1986. Growth of saccharomyces cerevisiae is controlled by its limited respiratory capacity: formulation and verification of a hypothesis.Biotech. Bioeng.,28:927–937.CrossRefGoogle Scholar
  29. Steinmeyer, D.E., Shuler, M.L., 1989. Structured model for saccharomyces cerevisiae.Chem. Eng. Sci.,44(9): 2017–2030.CrossRefGoogle Scholar
  30. Steyer, J.P., Queinnec, I., Simoes, D., 1993. Biotech: a real-time application of artificial intelligence for fermentation processes.Control Eng. Practice,1(2): 315–321.CrossRefGoogle Scholar
  31. Tenney, R.I., 1985. Rationale of the brewery fermentation.J. Am. Soc. Brew. Chem.,43: 57–60.Google Scholar
  32. Titica, M., Landau, S., Trelea, I.C., Latrille, E., Corrieu, G., Cheruy, A., 2000. Kinetics of aroma production in beer batch fermentation: Simulation and sensitivity to the operating conditions.J. Am. Soc. Brew. Chem.,58(4): 167–174.Google Scholar
  33. Trelea, I.C., Latrille, E., Landau, S., Corrieu, G., 2001a. Reliable estimation of the key variables.Bioprocess Biosystems Engineering,24: 227–237.CrossRefGoogle Scholar
  34. Trelea, I.C., Titica, M., Landau, S., Latrille, E., Corrieu G., Cheruy, A., 2001b. A predictive modelling of brewing fermentation: from knowledge-based to black-box models.Mathematics and Computers in Simulation,56: 405–424.MathSciNetCrossRefMATHGoogle Scholar
  35. Trelea, I.C., Latrille, E., Landau S., Corrieu, G., 2002. Prediction of confidence limits for diacetyl concentration during beer fermentation.J. Am. Soc. Brew. Chem.,60: 77–87.Google Scholar

Copyright information

© Zhejiang University Press 2004

Authors and Affiliations

  • Andrés-Toro B. 
    • 1
  • Girón-Sierra J. M. 
    • 1
  • Fernández-Blanco P. 
    • 1
  • López-Orozco J. A. 
    • 1
  • Besada-Portas E. 
    • 1
  1. 1.Department of Computer Architecture and System Engineering, Physical SciencesComplutense University of MaddridSpain

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