ACO and GA for Parameter Settings of E. coli Fed-Batch Cultivation Model

E. coli

plays significant role in modern biological engineering and industrial microbiology. In this paper the Ant Colony Optimization algorithm and Genetic algorithm are proposed for parameter identification of an

E. coli

fed-batch cultivation process model. A system of nonlinear ordinary differential equations is used to model the biomass growth and the substrate utilization. We use real experimental data set from an

E. coli

MC4110 fed-batch cultivation process for performing parameter optimization. The objective function was formulated as a distance between the model predicted and the experimental data. Two different distances were used and compared – the Least Square Regression and the Hausdorff Distance. The Hausdorff Distance was used for the first time to solve the considered parameter optimization problem. The results showed that better results concerning model accuracy are obtained using the objective function with a Hausdorff Distance between the modeled and the measured data. Although the Hausdorff Distance is more time consuming than the Least Square Distance, this metric is more realistic for the considered problem.