Parameter identification of a reduced nonlinear model for an activated sludge process based on cuckoo search algorithm

Abstract

Parameter identification plays a key role in systems’ modeling and control. This paper deals with a parameter identification problem for an activated sludge process used in wastewater treatment. The considered model is a nonlinear one inspired from the well-known ASM1. Nature-inspired algorithms have gained significant attention over the last years as useful means to solve parameter identification problem. The proposed approach in this paper is the cuckoo search algorithm based on both the fascinating brood parasitic behavior and the lévy flights. The advantages of this method are its simplicity and robustness, but it requires a good tuning of its parameters to have the best results. The comparison of the simulation results with the Nelder-Mead method, genetic algorithm, and particle swarm optimization proves the capability of this method to identify the model’s parameters with high precision.