Drinking Water Engineering and Science Discussions www.drink-water-eng-sci-discuss.net 1996-9473 1996-9481 1 2 2008 10.5194/dwesd-1-117-2008 http://www.drink-water-eng-sci-discuss.net/1/117/2008/ http://www.drink-water-eng-sci-discuss.net/1/117/2008/dwesd-1-117-2008.html http://www.drink-water-eng-sci-discuss.net/1/117/2008/dwesd-1-117-2008.pdf 117 133 2008-07-08 Verification of filter efficiency of horizontal roughing filter by Weglin's design criteria and Artificial Neural Network B. Mukhopadhay biswajitmukherjee23@rediff.com M. Majumder R. Nath Barman P. Kumar Roy A. 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 motive is to reduce the Slow Sand load in the raw water by using HRF as the pretreatment unit, but the main objective is to verify the Weglin's design criteria for HRF with respect to raw water condition. A model was also built with the help of neural network which tries to predict the filter efficiency of the HRF. Three results achieved from the three different models were compared to find whether the experimental HRF output conforms to the other two models. According to the results the results from experimental setup is coherent with the neural model but incoherent with the results from Weglin's formula. 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 Resour. 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 J. Hydrol. Eng., 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, retrieved from: 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. Hassoun, M.: Fundamental of Artificial Neural Networks, Massachusetts Institute of Technology, 1, 1995. Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models, J. Hydrol., 10, 282–290, 1970. 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.