Optimisation of fed-batch bioreactors using genetic algorithms

Abstract-This paper presents a genetic algorithms approach for the optimization of a fed-batch penicillin fermentation process. A customized float-encoding genetic algorithm is developed and implemented to a benchmark fed-batch penicillin fermentation process. Off-line optimization of the initial conditions and set points are carried out in two stages for a single variable and multiple variables. Further investigations with online optimization have been carried out to demonstrate that the yield can be significantly improved with an optimal feed rate profile. The results have shown that the proposed approaches can be successfully applied to optimization problems of fed-batch fermentation to improve the operation of such processes.

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“…The determination of the optimal substrate feed rate also has a problem of singular control because PMP fails to provide a complete solution [2].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary optimization of a fed-batch penicillin fermentation process

Zhang

Lan

2010

2010 International Symposium on Computer, Communication, Control and Automation (3CA)

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“…On the other hand, Miele's technique was utilized to solve an optimization problem with multiple objective functions (Hasegawa et al, 1987;Matsubara et al, 1985). Recently, an evolutionary algorithm, that is a genetic algorithm, with several constraints has frequently been used for rapid calculation of the optimization problem (Ronen et al, 2002;Roubos et al, 1999;Sarkar and Modak, 2003). While there have been many studies of approaches to the optimization problem in fed-batch fermentation, few have dealt with the issue in relation to repeated fedbatch fermentation (Ito et al, 1991;Modak and Lim, 1992;Weigand, 1981).…”

Section: Introductionmentioning

confidence: 99%

Optimization of human serum albumin production in methylotrophic yeast Pichia pastoris by repeated fed‐batch fermentation

Ohya

Ohyama

Kobayashi

2005

Biotech & Bioengineering

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An optimization method for repeated fed-batch fermentation was established with the aim of improving the recombinant human serum albumin (rHSA) production in Pichia pastoris. A simulation model for fed-batch fermentation was formulated and the optimal methanol-feeding policy calculated by dynamic programming method using five different methanol-feeding periods. The necessary state variables were collected from the calculated results and used for further optimization of repeated fed-batch fermentation. The optimal operation policy was investigated using the pre-collected state variables by estimating the overall profit per total methanol-feeding time. The calculated results indicated that the initial cell mass from the 2nd fed-batch fermentation on should be set at 35 or 40 g and methanol-feeding time at 264 h. In repeated fed-batch fermentation using the optimal operation policy, actual culture volume was in good agreement with the values simulated by model equations, but some discrepancy was observed in rHSA production. Minimum experiments were therefore carried out to re-evaluate rHSA production levels, which were then applied in re-calculations to determine the optimal operation policy. The optimal policy for repeated fed-batch fermentation established in the present study (i.e., 4-times-repeated fed-batch fermentation) achieved a 47% increase in annual rHSA production. Optimization of the culture period also brought about a 28% increase in annual rHSA production even in simple (not repeated) fed-batch fermentation.

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“…Several papers have been reported using GA techniques to solve test problems & problems representing engineering operations [18][19][20][21][22][23][24].…”

Section: Mutationmentioning

confidence: 99%