Abstract
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.
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References
Bonissone PP (1995) Discussion: fuzzy logic control technology: a personal perspective. Technometrics 37:262–266
Cybenko G (1989) Approximations by superpositions of a sigmoidal function. Math Control Signal Syst 2:303–314
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 613:144–154
Galvanauskas V, Lübbert A (2002) Monitoring recombinant protein concentrations in production processes (submitted)
Horiuchi JI, Hiraga K (1999) Industrial application of fuzzy control to large-scale recombinant vitamin B2 production. J Biosci Bioeng 87:365–371
Horiuchi JI, Kishimoto M (2002) Application of fuzzy control to industrial processes in Japan. Fuzzy Sets Syst 128:117–124
Matlab (2002) Matlab 6. The Math Works Inc., Natick, Massachusetts
Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313
Park TY, Froment GF (1998) A hybrid genetic algorithm for the estimation of parameters in detailed kinetic models. Comput Chem Eng 22:S103–S110
Preusting H, Noordover J, Simutis R, Lübbert A (1996) The use of hybrid modeling for the optimization of the penicillin fermentation process. Chimia 50:416–417
Roubos H (2002) Bioprocess modelling and optimisation. PhD thesis, Delft University of Technology, The Netherlands
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 41:17–23
Schubert J, Simutis R, Dors M Havlik I, Lübbert A (1994) Bioprocess optimization and control: application of hybrid modelling. J Biotechnol 35:51–68
Shioya S, Shimizu K, Yoshida T (1999) Knowledge-based design and operation of bioprocess systems. J Biosci Bioeng 87:261–266
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 27:203–215
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 31:1691–1724
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Zimmermann HJ (1983) Fuzzy mathematical programming. Comput Oper Res 10:291–298
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We thank Alexander von Humboldt Stiftung, Deutscher Akademischer Austausch Dienst (DAAD) and Deutsches Bundesministerium für Bildung und Forschung (BMBF) for their generous support.
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Galvanauskas, V., Simutis, R. & Lübbert, A. Hybrid process models for process optimisation, monitoring and control. Bioprocess Biosyst Eng 26, 393–400 (2004). https://doi.org/10.1007/s00449-004-0385-x
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DOI: https://doi.org/10.1007/s00449-004-0385-x