In this paper, an on-line identification and optimization method based on genetic algorithms (GAs) has been used to optimize the productivity of a seventh-order nonlinear model of fed-batch culture of hybridoma cells. The parameters of the seventh-order nonlinear model are assumed to be unknown. The intention of this paper is to use GAs for (1) identifying the parameters of a seventh-order nonlinear model of fed-batch culture of hybridoma cells, and (2) determining the best feed rate control profiles for glucose and glutamine. The final level of monoclonal antibodies obtained by this method is then compared with the case where all the parameters are assumed to be known. It is found that the final level of monoclonal antibodies obtained by the on-line identification and optimization method is only about 3% less than the final level of monoclonal antibodies obtained by the case where all the parameters are assumed to be known. GAs proved to be a good alternative method for solving on-line identification and optimization problems.
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