Optimization of Molasses and Air Feeding Profiles in Fed-Batch Baker's Yeast Fermentation

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dc.authoridYuceer, Mehmet/0000-0002-2648-3931
dc.authorwosidYuceer, Mehmet/E-5110-2012
dc.contributor.authorAtasoy, Ilknur
dc.contributor.authorYuceer, Mehmet
dc.contributor.authorBerber, Ridvan
dc.date.accessioned2024-08-04T21:00:08Z
dc.date.available2024-08-04T21:00:08Z
dc.date.issued2009
dc.departmentİnönü Üniversitesien_US
dc.description19th European Symposium on Computer Aided Process Engineering -- JUN 14-17, 2009 -- Cracow, POLANDen_US
dc.description.abstractThis 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.,en_US
dc.description.sponsorshipScientific Research Projects Fund of Ankara University [2001-07-05-056]en_US
dc.description.sponsorshipThe support provided by the Scientific Research Projects Fund of Ankara University through grant no 2001-07-05-056 for this work is gratefully acknowledged. The authors are thankful to Dr. Mustafa TURKER for his support and provision of industrial data.en_US
dc.identifier.endpage628en_US
dc.identifier.isbn978-0-444-53433-0
dc.identifier.issn1570-7946
dc.identifier.startpage623en_US
dc.identifier.urihttps://hdl.handle.net/11616/103809
dc.identifier.volume26en_US
dc.identifier.wosWOS:000287727900099en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartof19th European Symposium on Computer Aided Process Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBaker's yeasten_US
dc.subjectKalman filteren_US
dc.subjectneural networken_US
dc.subjectdynamic optimizationen_US
dc.titleOptimization of Molasses and Air Feeding Profiles in Fed-Batch Baker's Yeast Fermentationen_US
dc.typeConference Objecten_US

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