PREDICTION OF GABAL EL-ASFAR WASTEWATER TREATMENT PLANT PERFORMANCE USING ARTIFICIAL NEURAL NETWORKS SYSTEM

Abstract

The traditional methods used for the control of wastewater treatment plants generally depend on examining the final effluent and then applying the suitable control action. These methods are slow in action and, therefore, are inefficient. Better control of the plant can be achieved by modeling the complex processes of wastewater treatment and predicting the plant performance in advance.

New approaches using applications of artificial intelligence (AI) are now widely applied. Artificial neural networks (ANNs), one of the most widely used application of AI, proved to be an efficient tool in modeling dynamic systems with unknown process equations.

The aim of this research is to develop an ANN-based model that can efficiently predict wastewater treatment plant performance (effluent BOD and SS concentrations). The thesis illustrated the usefulness of using ANN-based model for the proper identification and representation of the Gabal El-Asfar WWTP in Cairo.