Journal
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
Volume 16, Issue 1, Pages 397-421Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/19942060.2021.2019126
Keywords
Infiltration rate; treated wastewater; artificial neural network; multilayer perceptron; Elman neural network
Funding
- Institut Pengurusan dan Pemantauan Penyelidikan, Universiti Malaya, Malaysia [RP025A-18SUS]
- United Arab Emirate University (UAEU) within the initiatives of the Asian Universities Alliance (AUA) collaboration [IF059-2021]
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This study developed a model based on the characteristics parameters of treated wastewater to predict the infiltration rate. The optimal model was found to be the first combination of inputs, including all seven parameters, using the MLP model with a 90% data division. The model achieved high accuracy in predicting the infiltration rate.
Predicting the infiltration rate (IR) of treated wastewater (TWW) is essential in controlling clogging problems. Most researchers that predict the IR using neural network models considered the characteristics parameters of soil without considering those of TWW. Therefore, this study aims to develop a model for predicting the IR based on various combinations of TWW characteristics parameters (i.e. total suspended solids (TSS), biological oxygen demand (BOD), electric conductivity (EC), pH, total nitrogen (TN), total phosphorous (TP), and hydraulic loading rate (HLR)) as input parameters. Therefore, two different artificial neural network (ANN) architectures, multilayer perceptron model (MLP) and Elman neural network (ENN), were used to develop optimal model. The optimal model was selected through evaluating three stages: selecting the best division of data, selecting the best model, and deciding the best combination of input parameters based on several performance criteria. The study concluded that the first combination of inputs that include all the seven-parameter using MLP model associated with 90% division of data was the optimal model in predicting the IR depending on TWW characteristics parameters, achieving a promising result of 0.97 for the coefficient of determination, 0.97 for test regression, 0.012 for MSE with 32.4 of max relative percentage error.
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