Real-coded genetic algorithms and nonlinear parameter identification

In this study, real-coded genetic algorithms are used in the parameter identification of the macroscopic Chemostat model. The Chemostat model utilized in this work is nonlinear having two distinct operating areas. Thus, the model is identified separately for both operating areas. The process simulator is used to generate data for the parameter identification. The optimizations with genetic algorithms are repeated with 200 different initial populations to guarantee the validity of the results. The parameter identification with genetic algorithms performs well giving accurate results.