Prediction of Performance Efficiency for Wastewater Treatment Plant's Effluent Biochemical Oxygen Demand Using Artificial Neural Network

This study investigated the application of an artificial neural network (ANN) to predict the performance efficiency of the Abuja-based Wupa WWTP, Nigeria using effluent 5-day biochemical oxygen demand (BOD5) as a performance indicator. Daily data of influent BOD5, pH, total dissolved solids, total suspended solids, chemical oxygen demand, total coliform, Escherichia coliform, and fecal coliform; and effluent BOD5 over a period of five years (2013 to 2017) for the Wupa WWTP was utilized for the plant’s performance efficiency. The four most reliable multilayer perceptron ANN (MLPANN) algorithms namely, Levenberg-Marquardt (LM) backpropagation resilient backpropagation, QuasiNewton backpropagation, and Fletcher-Reeves conjugate gradient backpropagation were adopted; and the most appropriate model was selected following training, validation and testing by altering the number of neurons and activation functions in both the hidden and output layers. The model efficiency was determined using mean square error (MSE) and correlation coefficient (R2 ). The ML algorithm with Logsig-Tansig activation pairing and architecture [8-1270-1] performed the best in terms of convergence time and prediction error, with MSE and R2 values of 1.522 and 0.922, respectively. Also, it revealed that the selected ANN model predicted the effluent BOD5 with an overall correlation coefficient of 0.962; thus, demonstrating the efficacy of ANN models for accurate prediction of the Wupa WWTP performance. The novelty of this research is in evaluating the efficiency of the plant over the periods and determining the most precise ANN model for Wupa WWTP, Abuja, Nigerians a study which has never been carried out before now