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Artificial Neural Network Modelling of Shyamala Water Works, Bhopal MP, India: A Green Approach towards the Optimization of Water Treatment Process

Author Affiliations

  • 1 Trinity Institute of Technology and Research, Bhopal, MP, INDIA
  • 2 Sarojini Naidu Govt. Girls P.G. College, Bhopal MP, INDIA

Res. J. Recent Sci., Volume 2, Issue (ISC-2012), Pages 26-28, February,2 (2013)


The water industry is striving hard to produce higher quality water at a lower cost due to increased regulatory standards. Municipal Water Treatment Plants can be considered as the industries producing potable water. They also produce huge amount of sludge after coagulation sedimentation in the clarri- flocculator unit which is a type of waste effluent containing large amount of aluminium and organic contaminants. Commonly it is discharged into surface water without proper treatment and hence causes water pollution. Aluminium salts extensively used for coagulation has been implicated in dialysis dementia, Parkinson and Alzheimer’s disease in Humans and also known to cause structural and functional problems in fishes, birds and animals. The present research work emphasizes to develop a green eco-friendly, clean and cost effective water treatment process to avoid the water pollution by non- judicious use of coagulant. Artificial Neural Network (ANN) technique is applied to the prediction of optimum coagulant dosing in Shyamala Water Treatment Plant, Bhopal. The alum sludge generated can be recycled and reused for waste water treatment.


  1. Valentin N., Fotoohi F. and Denoeux T., Modeling of coagulant dosing in a water treatment plant, Proc. of EANN’99, Warsaw, 165-170 (1999)
  2. Baxter C.W., Stanley S.J., Zhang Q. and Smith D.W., Developing artificial neural network models of water treatment processes: a guide for drinking water utilities, J. Environ. Eng. Sci., 1, 201-211(2002)
  3. Baxter C.W., Zhang Q., Stanley S.J., Shariff R., Tupas R.R.T. and Stark H.L., Drinking water quality and treatment, the use of artificial neural networks, Can. J. Civil Engg., 28 (1), 26–35 (2001)
  4. Maier H.R., Morgan N. and Chow W.K.C., Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters, Envir. Mod. & Soft., 19, 485-494 (2004)
  5. Fletcher D. and Goss E., Forecasting with Neural Networks: an application using bankruptcy data, Inform. Manage, 24, 159-167 (1993)
  6. Ying Z., Jun N., Fuyi C., Liang G., Water Quality forecasting through application of BP neural network at Yuquio Reservoir, J. Zhejiang Univ. Sci., A (8), 1482-1487 (2007)
  7. Vyas M., Modhera B., Vyas V. and Sharma A.K., Performance forecasting of common effluent treatment plant parameters by artificial neural network, ARPN Jour. Engg. Applied Sci., 6(1), 38-42, (2011)
  8. Parihar S.S., Kumar Ajit, Kumar Ajay, Gupta R.N., Pathak Manoj, Shrivastav Archana and Pandey A.C., Physico-Chemical and Microbiological Analysis of Underground Water in and Around Gwalior City, MP, India, Res .J. Recent Sci., 1(6), 62-65(2012)
  9. Kushwah Ram Kumar, Malik Suman and Singh Archana, Water Quality Assessment of Raw Sewage and Final Treated Water with Special Reference to Waste Water Treatment Plant Bhopal, MP, India, Res. J. Recent Sci., 1(ISC-2011) , 185-190 (2012)
  10. Safari D., Mulongo G., Byarugaba D. and Tumwesigye W., Impact of Human Activities on the Quality of Water in Nyaruzinga Wetland of Bushenyi District Uganda, I. Res. J. Environment Sci., 1(4), 1-6(2012)