Estimation of turbidity in water treatment plant using hammerstein-wiener and neural network technique
Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. Thi...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Advanced Engineering and Science
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/74899/1/NorhalizaAbdulWahab_EstimationofTurbidityinWaterTtreatment.pdf http://eprints.utm.my/id/eprint/74899/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016993323&doi=10.11591%2fijeecs.v5.i3.pp666-672&partnerID=40&md5=8975efeb191788f844d07ec31dab5dbf |
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| Summary: | Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. This paper presents a comparison of Hammerstein Wiener and neural network technique for estimating of turbidity in water treatment plant. The models were validated using an experimental data from Tamburawa water treatment plant in Kano, Nigeria. Simulation results demonstrated that the neural network model outperformed the Hammerstein-Wiener model in estimating the turbidity. The neural network model may serve as a valuable tool for predicting the turbidity in the plant. |
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