Authors :
Samson Balogun; Toochukwu Chibueze Ogwueleka
Volume/Issue :
Volume 8 - 2023, Issue 1 - January
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/40QFPIn
DOI :
https://doi.org/10.5281/zenodo.7631299
Abstract :
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
Keywords :
Artificial Neural Network (ANN); Wastewater Treatment Plant (WWTP); 5-Day Biochemical Oxygen Demand (BOD5); Wupa WWTP; Multilayer Perceptron (MLP)
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
Keywords :
Artificial Neural Network (ANN); Wastewater Treatment Plant (WWTP); 5-Day Biochemical Oxygen Demand (BOD5); Wupa WWTP; Multilayer Perceptron (MLP)
