Drinking Water Engineering and Science www.drink-water-eng-sci.net 1996-9457 1996-9465 2 1 2009 10.5194/dwes-2-21-2009 http://www.drink-water-eng-sci.net/2/21/2009/ http://www.drink-water-eng-sci.net/2/21/2009/dwes-2-21-2009.html http://www.drink-water-eng-sci.net/2/21/2009/dwes-2-21-2009.pdf 21 27 2009-06-19 Verification of filter efficiency of horizontal roughing filter by Weglin's design criteria and Artificial Neural Network Biswajit Mukhopadhay Mrinmoy Majumder Rabindra Nath Barman Pankaj Kumar Roy Asis Mazumder KMW&SA, Kolkata, India School of Water Resources Engineering, Jadavpur University, Kolkata, India B. P. Poddar Institute of Management and Technology, Kolkata, India The general objective of this study is to estimate the performance of the Horizontal Roughing Filter (HRF) by using Weglin's design criteria based on 1/3–2/3 filter theory. The main objective of the present study is to validate HRF developed in the laboratory with Slow Sand Filter (SSF) as a pretreatment unit with the help of Weglin's design criteria for HRF with respect to raw water condition and neuro-genetic model developed based on the filter dataset. The results achieved from the three different models were compared to find whether the performance of the experimental HRF with SSF output conforms to the other two models which will verify the validity of the former. According to the results, the experimental setup was coherent with the neural model but incoherent with the results from Weglin's formula as lowest mean square error was observed in case of the neuro-genetic model while comparing with the values found from the experimental SSF-HRF unit. As neural models are known to learn a problem with utmost efficiency, the model verification result was taken as positive. Ahmed, J. A. and Sarma, A. K.: Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir, J. Water Res. Manage., 19, 145–161, 2005. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology: Artificial neural networks in Hydrology I: Preliminary concepts, ASCE Journal of Hydrologic Engineering, 5(2), 115–123, 2000. Barman, R. N., Mukhopadhaya, B., Majumder, M., Roy, P. K., and Mazumdar, A.: Estimation And Calculationof A Relationship Between Dispersion Number, Reynolds Number, Porosity And Hydraulic Gradient in Horizontal Roughing Filter, Journal of Agricultural, Food, and Environmental Sciences, 2, 1, http://www.scientificjournals.org/journals2008/j_of_agriculture1_2008.htm, 2008. Bhatt, V. K., Bhattacharya, P., and Tiwari, A. K.: Application of artificial neural network in estimation of rainfall erosivity, Hydrol. J., 1–2, 30–39, 2007. Clair, T. A. and Ehrman, J. M.: Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers, Water Resour. Res., 34(3), 447–455, 1998. Coulibaly, P., Anctil, F., and Bobee, B.: Daily reservoir inflow forecasting using artificial neural networks with stopped training approach, Hydrology, 230(3–4), 244–257, 2000. El-Taweel, G. E. and Ali, G. H.: Evaluation of Roughing and Slow Sand Filters for Water Treatment, Water Air Soil Poll., 120(1/2), 21–28, 2004. Hassoun, M.: Fundamental of Artificial Neural Networks, Massachusetts Institute of Technology, 1, 1995. Imrie, C. E., Durucan, S., and Korre, A.: River flow prediction using neural networks: Generalization beyond the calibration range, Hydrology, 233(3–4), 138–154, 2000. Jain, S. K., Das, A., and Srivastava, D. K.: Application of ANN for reservoir inflow prediction and operation, J. Water Res. Pl.-ASCE, 125(5), 263–271, 1999. Maier, H. R. and Dandy, G. C.: Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting, Water Resour. Res., 35(8), 2591–2596, 1999. Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models, J. Hydrol., 10, 282–290, 1970. Ray, C. and Klindworth, K. K.: Neural networks for agrichemical vulnerability assessment of rural private wells, Hydrol. Eng., 5(2), 162–171, 2000. Tokar, A. S. and Johnson, P. A.: Rainfall-runoff modeling using artificial neural networks, Hydrol. Eng., 4(3), 232–239, 1999. Yitian, L. and Gu, R. R.: Modeling Flow and Sediment Transport in a River System Using an Artificial Neural Network, J. Environ. Manage., 31, 1, 122–134, 2003. Wang, Q. J.: The genetic algorithm and its application to calibrating conceptual rainfall-runoff models, Water Resour. Res., 27(9), 2467–2471, 1991. Wardlaw, R. and Sharif, M.: Evaluation of genetic algorithms for optimal reservoir system operation, J. Water Res. Pl.-ASCE, 125(1), 25–33, 1999. Zhang, Q. and Stanley, S. J.: Real-time treatment process control with artificial neural networks, J. Environ. Eng., 125(2), 153–160, 1999.