Estimation of Turbidity in Water Treatment Plant using Hammerstein-Wiener and Neural Network Technique

M. S Gaya (Kano University of Science & Technology)
L. A. Yusuf (Bayero University Kano)
Mamunu Mustapha (Kano University of Science & Technology)
Bashir Muhammad (Kano University of Science & Technology)
Ashiru Sani (Kano University of Science & Technology)
Aminu Tijjani Aminu Tijjani (Kano University of Science & Technology)
N. A. Wahab (Universiti Teknologi Malaysia)
M. T.M. Khairi (Universiti Teknologi Malaysia)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 5, No 3: March 2017

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.