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Drinking Water Engineering and Science An interactive open-access journal
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Volume 2, issue 1
Drink. Water Eng. Sci., 2, 21-27, 2009
https://doi.org/10.5194/dwes-2-21-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
Drink. Water Eng. Sci., 2, 21-27, 2009
https://doi.org/10.5194/dwes-2-21-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Jun 2009

19 Jun 2009

Verification of filter efficiency of horizontal roughing filter by Weglin's design criteria and Artificial Neural Network

Biswajit Mukhopadhay1, Mrinmoy Majumder2, Rabindra Nath Barman3, Pankaj Kumar Roy2, and Asis Mazumder2 Biswajit Mukhopadhay et al.
  • 1KMW&SA, Kolkata, India
  • 2School of Water Resources Engineering, Jadavpur University, Kolkata, India
  • 3B. P. Poddar Institute of Management and Technology, Kolkata, India

Abstract. 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.

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