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Mamunu Mustapha
Kano University of Science & Technology

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Estimation of Turbidity in Water Treatment Plant using Hammerstein-Wiener and Neural Network Technique M. S Gaya; L. A. Yusuf; Mamunu Mustapha; Bashir Muhammad; Ashiru Sani; Aminu Tijjani Aminu Tijjani; N. A. Wahab; M. T.M. Khairi
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 3: March 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i3.pp666-672

Abstract

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.
Co-Authors Aminu Tijjani Aminu Tijjani Ashiru Sani Bashir Muhammad L. A. Yusuf M. S Gaya M. T.M. Khairi N. A. Wahab