Bioprocess and Biosystems Engineering

, Volume 26, Issue 6, pp 393–400 | Cite as

Hybrid process models for process optimisation, monitoring and control

  • V. Galvanauskas
  • R. Simutis
  • A. LübbertEmail author
Original papers


Hybrid models aim to describe different components of a process in different ways. This makes sense when the corresponding knowledge to be represented is different as well. In this way, the most efficient representations can be chosen and, thus, the model performance can be increased significantly. From the various possible variants of hybrid model, three are selected which were applied for important biotechnical processes, two of them from existing production processes. The examples show that hybrid models are powerful tools for process optimisation, monitoring and control.


Membership Function Hybrid Model Fuzzy Controller Fuzzy Variable Oxygen Uptake Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Alexander von Humboldt Stiftung, Deutscher Akademischer Austausch Dienst (DAAD) and Deutsches Bundesministerium für Bildung und Forschung (BMBF) for their generous support.


  1. 1.
    Bonissone PP (1995) Discussion: fuzzy logic control technology: a personal perspective. Technometrics 37:262–266Google Scholar
  2. 2.
    Cybenko G (1989) Approximations by superpositions of a sigmoidal function. Math Control Signal Syst 2:303–314Google Scholar
  3. 3.
    Dors M, Simutis R, Lübbert A (1995) Hybrid process modeling for advanced process state estimation, prediction, and control exemplified in a production-scale mammalian cell culture. ACS Symp Ser 613:144–154Google Scholar
  4. 4.
    Galvanauskas V, Lübbert A (2002) Monitoring recombinant protein concentrations in production processes (submitted)Google Scholar
  5. 5.
    Horiuchi JI, Hiraga K (1999) Industrial application of fuzzy control to large-scale recombinant vitamin B2 production. J Biosci Bioeng 87:365–371CrossRefGoogle Scholar
  6. 6.
    Horiuchi JI, Kishimoto M (2002) Application of fuzzy control to industrial processes in Japan. Fuzzy Sets Syst 128:117–124CrossRefGoogle Scholar
  7. 7.
    Matlab (2002) Matlab 6. The Math Works Inc., Natick, MassachusettsGoogle Scholar
  8. 8.
    Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313Google Scholar
  9. 9.
    Park TY, Froment GF (1998) A hybrid genetic algorithm for the estimation of parameters in detailed kinetic models. Comput Chem Eng 22:S103–S110CrossRefGoogle Scholar
  10. 10.
    Preusting H, Noordover J, Simutis R, Lübbert A (1996) The use of hybrid modeling for the optimization of the penicillin fermentation process. Chimia 50:416–417Google Scholar
  11. 11.
    Roubos H (2002) Bioprocess modelling and optimisation. PhD thesis, Delft University of Technology, The NetherlandsGoogle Scholar
  12. 12.
    Roubos JA, Babuska R, Krabben P, Heijnen JJ (2000) Hybrid modeling of fed-batch bioprocesses; combination of physical equations with metabolic networks and black-box kinetics. Journal A, Benelux Q J Automatic Control 41:17–23Google Scholar
  13. 13.
    Schubert J, Simutis R, Dors M Havlik I, Lübbert A (1994) Bioprocess optimization and control: application of hybrid modelling. J Biotechnol 35:51–68CrossRefGoogle Scholar
  14. 14.
    Shioya S, Shimizu K, Yoshida T (1999) Knowledge-based design and operation of bioprocess systems. J Biosci Bioeng 87:261–266CrossRefGoogle Scholar
  15. 15.
    Simutis R, Havlik I, Lübbert A (1993) Fuzzy aided neural network for real time state estimation and process prediction in a production scale beer fermentation. J Biotechnol 27:203–215CrossRefGoogle Scholar
  16. 16.
    Sjöberg J, Zhang Q, Ljung L, Benveniste A, Delyon B, Glorennec PY, Hjalmarsson H, Juditsky A (1995) Nonlinear black-box modeling in system identification: a unified overview. Automatica 31:1691–1724CrossRefGoogle Scholar
  17. 17.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353Google Scholar
  18. 18.
    Zimmermann HJ (1983) Fuzzy mathematical programming. Comput Oper Res 10:291–298CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  1. 1.Institut für BioengineeringMartin-Luther-UniversitätHalle-WittenbergGermany

Personalised recommendations