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In this paper, Firefly algorithms (FA) and Genetic algorithms (GA) are applied to parameter identification problem of a non-linear mathematical model of the E. coli cultivation process. A system of ordinary differential equations is proposed to model the growth of the bacteria, substrate utilization and acetate formation. Parameter optimization is performed using a real experimental data set from an E. coli MC4110 fed-batch cultivation process. In the considered non-linear mathematical model, the parameters that should be estimated are maximum specific growth rate, two saturation constants and two yield coefficients. Parameters of both meta-heuristics are tuned on the basis of several pre-tests according to the optimization problem considered here. Based on the numerical and simulation result, it is shown that the model obtained by the FA is more accurate and adequate than the one obtained using the GA. Presented results prove FA superiority and powerfulness in solving non-linear dynamic model of cultivation processes.

Genetic Algorithms and Firefly Algorithms for Non-linear Bioprocess Model Parameters Identification