ADAPTIVE ONLINE SIMULATION OF BIOREACTORS - FERMENTATION MONITORING AND MODELING SYSTEM

Citation
K. Fagervik et al., ADAPTIVE ONLINE SIMULATION OF BIOREACTORS - FERMENTATION MONITORING AND MODELING SYSTEM, Journal of industrial microbiology, 14(5), 1995, pp. 403-411
Citations number
13
Categorie Soggetti
Biothechnology & Applied Migrobiology
ISSN journal
01694146
Volume
14
Issue
5
Year of publication
1995
Pages
403 - 411
Database
ISI
SICI code
0169-4146(1995)14:5<403:AOSOB->2.0.ZU;2-X
Abstract
In order to study and control fermentation processes, indirect on-line measurements and mathematical models can be used. Here an on-line mod el for fermentation processes is presented. The model is based on atom and partial mass balances as well as on stability equations for the p rotolytes. The model is given an adaptive form by including transport equations for mass transfer and expressions for the fermentation kinet ics. The state of the process can be estimated on-line using the balan ce component of the model completed with measurement equations for the input and the output hows of the process. Adaptivity is realized by m eans of on-line estimation of the parameters in the transport and kine tic expressions using recursive regression analysis. On-line estimatio n of the kinetic and mass transfer parameters makes model-based predic tions possible and enables intelligent process control while facilitat ing testing of the validity of the measurement variables. A practical MS-Windows 3.1 model implementation called FMMS-Fermentation Monitorin g and Modeling System is shown. The system makes it easy to configure the operating conditions for a run. It uses Windows dialogs for all se t-ups, model configuration parameters, elemental compositions, on-line measurement devices and signal conditioning. Advanced on-line data an alysis makes it possible to plot variables against each other for easy comparison. FMMS keeps track of over 100 variables per run. These var iables are either measured or estimated by the model. Assay results ca n also be entered and plotted during fermentation. Thus the model can be verified almost instantly. Historical fermentation runs can be re-a nalyzed in simulation mode. This makes it possible to examine differen t signal conditioning filters as well as the sensitivity of the model. Combined, the data analysis and the simulation mode make it easy to l est and develop model theories and new ideas.