On-line evolutionary optimization of an industrial fed-batch yeast fermentation process.
This paper presents two genetic algorithms based on optimization methods to maximize biomass concentration, and to minimize ethanol formation. The objective function is maximized according to the values of feed flow rate, using genetic search approaches. Five case studies were carried out for different initial conditions, which strongly influence the optimal profiles of feed flow rate for the fermentation process. The ethanol and glucose disturbance effects were examined to stress the effectiveness of proposed approaches. The proposed genetic approaches were implemented for an industrial scale baker's yeast fermentor which produces Saccharomyces cerevisiae known as baker's yeast. The results show that optimal feed flow rate was obtained in a satisfactory and successful way for fed-batch fermentation process.
Department of Electronic and Telecommunication Engineering, Kocaeli University, 41040, Kocaeli, Turkey. uyuzgec@kocaeli.edu.tr
Uğur
U
Yüzgeç
Mustafa
M
Türker
Akif
A
Hocalar
D000465:Algorithms; D019149:Bioreactors / Q000382:microbiology*; D049109:Cell Proliferation; D003198:Computer Simulation; D000431:Ethanol / Q000378:metabolism*; D005246:Feedback / Q000502:physiology; D005947:Glucose / Q000378:metabolism*; D007218:Industrial Microbiology / Q000379:methods; D008954:Models, Biological; D010101:Oxygen Consumption / Q000502:physiology*; D011786:Quality Control; D012441:Saccharomyces cerevisiae / Q000378:metabolism* / Q000502:physiology*
D000431:Ethanol; D005947:Glucose