Atasoy, IlknurYuceer, MehmetBerber, Ridvan2024-08-042024-08-042009978-0-444-53433-01570-7946https://hdl.handle.net/11616/10380919th European Symposium on Computer Aided Process Engineering -- JUN 14-17, 2009 -- Cracow, POLANDThis work focuses on maximization of the amount of biomass in the production of baker's yeast in fed-batch fermenters while minimizing the undesirable alcohol formation, by regulating the molasses and air feed rates. An optimization mechanism coupled with a state estimation algorithm and an Artificial Neural Network model based on original industrial data has been designed. As substrate and biomass concentrations required within this structure can not be measured on-line, these variables were predicted by an artificial neural network model using other measurable variables. Non-measurable substrate concentrations were successfully estimated by Kalman filtering using industrial data and thus, obtained new data sets were used for training the neural network model. Subsequently, through an SQP based optimization algorithm feeding profiles yielding maximum biomass and minimum alcohol formation were obtained. When computed results were compared to the industrial data, it was seen that molasses feeding profiles were compatible whereas aeration profiles were considerably different. The reason of this discrepancy was due to the agitation of the industrial fermenter with air at high air flow rates in order to provide better mixing in the reactor. Since the aeration profile obtained is associated with only the reproduction of microorganisms, it is postulated that the suggested optimization strategy may be industrially applicable for the maximization of biomass where enough agitation is provided by other means.,eninfo:eu-repo/semantics/closedAccessBaker's yeastKalman filterneural networkdynamic optimizationConference Object26623628WOS:000287727900099N/A