Activated sludge process identification based on genetic algorithm

This paper investigates the application of an optimization method, which is the genetic algorithm, in order to identify the parameters of an activated sludge process for waste water treatment. The studied model is a hybrid reduced-order one. The genetic algorithm tries to find out the optimal parameters that ensure a minimal mean square error between the measured and simulation outputs. Simulation results show the competence of the proposed approach to seize the true model's parameter values.