Publication:
Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model

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dc.citedby2
dc.contributor.authorMohiyaden H.A.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorid56780374500en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid56239664100en_US
dc.date.accessioned2025-03-03T07:47:42Z
dc.date.available2025-03-03T07:47:42Z
dc.date.issued2024
dc.description.abstractUncontrolled development at the upstream of the river catchment have led to detrimental effect to the environment, including degradation of river water quality. River Water Treatment Plant (RWTP) technology was introduced to reduce the contamination loading into the river water system, worldwide. The technology offers the best biological treatment process including simplicity and stable removal efficiency. However, the plant performance plan is difficult task to predict, thus might have influence the operational control. Recently, artificial neural network (ANN) models have been widely applied in environmental engineering area due to the ability to skip the complexity process to assume of the unknown variables compare to conventional physical based model. In this study, the results of 3-yrs performance using ANN of RWTP were developed. Feed-forward back-propagation using Levenberg-Marquardt (trainlm) used as for this predictive approach. The ideal configuration involves utilizing the tangent sigmoid transfer function (Tansig) in the hidden layer and a linear transfer function (Purelin) in the output layer, with 25 neurons. This configuration yields an R2 value of 0.963 and the most least mean square error (MSE) of 30.39. From the comparison between two model (bio-kinetic and ANN), performance indicator for ANN model shows the best and the most optimum model. Ultimately, RWTP optimization using black-box model ANN is more reliable and timesaving as compared to conventional bio-kinetic model. The development of the proposed model can be implemented and used for various water quality improvement facilities and predict the effluent target parameter in RWTP with higher degree of accuracy. ? Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12015
dc.identifier.doi10.1088/1755-1315/1296/1/012015
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85185814120
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185814120&doi=10.1088%2f1755-1315%2f1296%2f1%2f012015&partnerID=40&md5=20a855060d6639c020eee15431da9745
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37121
dc.identifier.volume1296
dc.publisherInstitute of Physicsen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Earth and Environmental Science
dc.titlePrediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Modelen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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