Artificial Neural Networks Modelling For AL-Rustumiya Wastwater Treatment Plant in Baghdad

Abstract

In the present research, Artificial Neural Networks (ANNs) were developed for modelling the performance of Al-Rustamiya wastewater treatment plant, Baghdad, Iraq. There were created two models and the outputs were the removal efficiency of BOD and COD parameters. Four main input parameters were selected for modelling, namely Total suspended solids (TSS), Total dissolved solids (TDS), chloride ion (Cl-), and pH. Influent and effluent concentrations of the parameters were collected from Mayoralty of Baghdad for the period from 2011 to 2021. The results of the modelling were in terms of mean square error (MSE) and correlation coefficient (R). The results indicated that the ANNs models were accurately able to predict the removal of the BOD, and COD, and the optimum topology of the ANNs is obtained at 13 neurons in the hidden layer for both with 3.09 MSE, 0.96 and 4.28 MSE, 0.96 R for BOD and COD respectively.