Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model
Uncontrolled 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. Th...
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Institute of Physics
2025
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| author | Mohiyaden H.A. Sidek L.M. Hayder G. |
| author2 | 56780374500 |
| author_facet | 56780374500 Mohiyaden H.A. Sidek L.M. Hayder G. |
| author_sort | Mohiyaden H.A. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Uncontrolled 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. |
| format | Conference paper |
| id | my.uniten.dspace-37121 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Institute of Physics |
| record_format | dspace |
| spelling | my.uniten.dspace-371212025-03-03T15:47:42Z Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model Mohiyaden H.A. Sidek L.M. Hayder G. 56780374500 35070506500 56239664100 Uncontrolled 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. Final 2025-03-03T07:47:42Z 2025-03-03T07:47:42Z 2024 Conference paper 10.1088/1755-1315/1296/1/012015 2-s2.0-85185814120 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185814120&doi=10.1088%2f1755-1315%2f1296%2f1%2f012015&partnerID=40&md5=20a855060d6639c020eee15431da9745 https://irepository.uniten.edu.my/handle/123456789/37121 1296 1 12015 All Open Access; Gold Open Access Institute of Physics Scopus |
| spellingShingle | Mohiyaden H.A. Sidek L.M. Hayder G. Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model |
| title | Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model |
| title_full | Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model |
| title_fullStr | Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model |
| title_full_unstemmed | Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model |
| title_short | Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model |
| title_sort | prediction of river water treatment plant operational performances using optimization approach in artificial neural network model |
| url_provider | http://dspace.uniten.edu.my/ |