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Prediction of Water Quality and Alum Dose using Artificial Neural Network-Case Study of Surat

H. Jariwala

Abstract


This study outlines the application of artificial neural network to improve prediction capability by investigating the effect of data sampling, network type and configuration as well as the inclusion of past data at the neural network input. To improve drinking water quality while reducing operating costs, many drinking water utilities are investing in advanced process control and automation technologies. The use of artificial intelligence technologies, specifically artificial neural networks, is increasing in the drinking water treatment industry as they allow for the development of robust nonlinear models of complex unit processes. This paper highlights the utility of artificial neural networks in water quality modeling as well as drinking water treatment process modeling and control through the presentation of a large-scale water treatment plants in Surat, Gujarat. The detailed objectives of this paper is to develop ANN models that are capable of predicting treated water quality parameters given raw water quality parameters and alum dose and to develop an ANN model that is capable of predicting the optimal alum dose given raw and treated water quality.

Keywords


Artificial Neural Network, Coagulation Control, Neurosolutions, Regression Analysis, Statistical Analysis. Water Treatment.

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References


Amari, Murata, Muller, Finke, Yang, 1997. Asymptotic statistical theory of overtraining and cross-validation, IEEE Transactions on Neural Networks 8 (5), 985–996.

B. Raduly, 2008 “Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study”, Seminar for Master of Science from university of Malasiya.

C.W. Baxter, S.J. Stanley, Q. Zhang, D.W. Smith; 2007; “Developing Artificial Neural Network Process Models: A Guide for Drinking Water Utilities”. 6th Environmental Engineering Specialty Conference of the CSCE & 2nd Spring Conference of the Geoenvironmentla, Division of the Canadian Geotechnical Society.

Chen, W.C., Chang, N.B., Shieh, W.K., 2001. Advanced hybrid fuzzy-neural controller for industrial wastewater treatment. Journal of Environmental Engineering 127 (11), 1048–1059.

Fernández, E. and Gálvis, A., “Artificial Neural Networks Model Used for Clear Water Treatment Plant”, seminar submitted to University of Colombia.

Gagnon, C., Grandjean, B.P.A., Thibault, J., 1997. Modelling of coagulant dosage in a water treatment plant. Artificial Intelligence in Engineering 11, 401–404.

Guan-De Wu, Shang-Lien Lo; 2008, “Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system”, Journal of Engineering Applications of Artificial Intelligence 21 (1189–1195).

Gulay TEZEL, Esra YEL and R.Kaan SINAN, 2010, “Artificial Neural Network (Ann) Model for Domestic Wastewater Treatment Plant Control”, BALWOIS - Ohrid, Republic of Macedonia – 25.

Holger R. Maier, Nicolas Morgan & Christopher W.K. Chow, 2003, “Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters”, Journal of Environmental Modeling & software Environmental Modelling & Software 19 (485–494).

Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366.

Maged M. Hamed, Mona G. Khalafallah & Ezzat A. Hassanien; 2004; “Prediction of wastewater treatment plant performance using artificial neural networks”; Journal of Environmental Modeling & software 19 (919–928).

N. Valentin, 1994, “Modelling of coagulant dosage in a water treatment plant”, (56-57).

National Instruments, 1999. Labview 5.1, National Instruments, Austin, Texas, USA.

Serodes, J.B., Rodriguez, M.J., Ponton, A., 2001. Chlorcast: a methodology for developing decision-making tools for chlorine disinfection control. Environmental Modelling and Software 16, 53–62.

Weigend, A.S., Rumelhart, D.E., Huberman, B.A., 1990. Predicting the future: A connectionist approach. International Journal of Neural Systems 1 (3), 193–209.

White, H., 1989. Learning in artificial neural networks: A statistical perspective. Neural Computation 1, 425–464.

Zhang, Q., Stanley, S.J., 1999. Real-time water treatment process control with artificial neural networks. Journal of Environmental Engineering 125 (2), 153–160.


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