In this chapter, an on-line identification and optimization method, based on a series of real-valued GA, is studied for a seventh-order nonlinear model of fed-batch culture of hybridoma cells. The parameters of the model are assumed to be unknown. The on-line procedure is divided into three stages: Firstly, the GA is used for identifying the unknown parameters of the model. Secondly, the best feed rate control profiles of glucose and glutamine are found by the GA based on the estimated parameters. Finally, the bioreactor is driven under the control of the optimal feed flow rates. The final MAb concentration of 193.1 mg/L and a final volume of 2L are reached at the end of the fermentation. This result is only 2% less than the best result (196.27 mg/L) obtained for the case wherein all the parameters are assumed to be known (i.e., no online identification). The real-valued GA have proved to be effective tools for solving on-line identification and optimization problems.
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