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.

Interactions Evaluation between the Jouamaa Hakama Groundwater and Ouljat Echatt River in the North of Morocco, Using Hydrochemical Modeling, Multivariate Statistics and GIS

“…For that purpose, to characterize the quality of SW, several researchers have used pertinent methods and techniques, such as WQI, which is regarded as one of the most efficient techniques for assessing water quality [18][19][20]. Moreover, fuzzy logic [21,22], machine learning [23,24], and the projection pursuit approach [11,25,26] have been used to forecast dam water quality [23], river water quality, and GW quality [11,25,26], and neural networks have been utilized to analyze water quality [27,28]. Multivariate statistical approaches can be used to assess large freshwater quality datasets with a minimum loss of information [29,30], which is valuable for quickly characterization of the contamination [31,32].…”