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Adaptive on-line simulation of bioreactors: Fermentation monitoring and modeling system
Authors:Dr. Kaj Fagervik  Mikael Rydström  Randolf von Schalien  Björn Saxén  Knut Ringbom  Anette Rothberg  Håkan Gros
Affiliation:(1) Department of Chemical Engineering, Åbo Akademi University, Biskopsgatan 8, FIN-20500 Åbo, Finland;(2) Genencor International, Kyllikinportti 2, PO Box 105, FIN-00241 Helsinki, Finland
Abstract:Summary In order to study and control fermentation processes, indirect on-line measurements and mathematical models can be used. Here an on-line model for fermentation processes is presented. The model is based on atom and partial mass balances as well as on stability equations for the protolytes. The model is given an adaptive form by including transport equations for mass transfer and expressions for the fermentation kinetics. The state of the process can be estimated on-line using the balance component of the model completed with measurement equations for the input and the output flows of the process. Adaptivity is realized by means of on-line estimation of the parameters in the transport and kinetic expressions using recursive regression analysis. On-line estimation of the kinetic and mass transfer parameters makes model-based predictions possible and enables intelligent process control while facilitating testing of the validity of the measurement variables. A practical MS-Windows 3.1 model implementation called FMMS—Fermentation Monitoring and Modeling System is shown. The system makes it easy to configure the operating conditions for a run. It uses Windows dialogs for all set-ups, model configuration parameters, elemental compositions, on-line measurement devices and signal conditioning. Advanced on-line data analysis makes it possible to plot variables against each other for easy comparison. FMMS keeps track of over 100 variables per run. These variables are either measured or estimated by the model. Assay results can also be entered and plotted during fermentation. Thus the model can be verified almost instantly. Historical fermentation runs can be re-analyzed in simulation mode. This makes it possible to examine different signal conditining filters as well as the sensitivity of the model. Combined, the data analysis and the simulation mode make it easy to test and develop model theories and new ideas.
Keywords:Saccharomyces cerevisiae  Process control  State estimation
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