Application of Novel MCDM for Location Selection of Surface Water Treatment Plant

Author(s):  
Sudipa Choudhury ◽  
Prasenjit Howladar ◽  
Mrinmoy Majumder ◽  
Apu Kumar Saha
2019 ◽  
Vol 8 (3) ◽  
pp. 20-42
Author(s):  
Sudipa Choudhury ◽  
Apu Kumar Saha

Water treatment plants (WTPs) are responsible for ensuring supply of healthy water to urban and rural consumers for drinking and other related purposes. But the arbitrary selection of a location for installation or relocation of WTPs often fails the purpose of the plant. Presently studies in location selection for water treatment plant are rare. Multi-criteria decision making (MCDM) methods and bagged polynomial neural networks (PNN) were found to be exemplary and easy to use tools for prediction, simulation and optimization of decision-making objectives. The present study tries to apply the advantages of MCDM and bagged PNNs in the identification of an ideal location for a surface water treatment plant. The most significant parameter is found to be WQI which represents the overall quality of water suitable for domestic use. The PNN models were developed with all the selected eight alternatives as input and output. The algorithms like GMDH, SFS, SMS, and QC were used to estimate the weight of connections in between the input and hidden; and hidden and output layers separately for each segment. The application of these two soft computation tools provides an opportunity to the decision maker in the selection of optimal location with the help of an objective and cognitive method.


2014 ◽  
Vol 71 (4) ◽  
pp. 638-644 ◽  
Author(s):  
Alina Pruss

A technological investigation was carried out over a period of 2 years to evaluate surface water treatment technology. The study was performed in Poland, in three stages. From November 2011 to July 2012, for the first stage, flow tests with a capacity of 0.1–1.5 m3/h were performed simultaneously in three types of technical installations differing by coagulation modules. The outcome of the first stage was the choice of the technology for further investigation. The second stage was performed between September 2012 and March 2013 on a full-scale water treatment plant. Three large technical installations, operated in parallel, were analysed: coagulation with sludge flotation, micro-sand ballasted coagulation with sedimentation, coagulation with sedimentation and sludge recirculation. The capacity of the installations ranged from 10 to 40 m3/h. The third stage was also performed in a full-scale water treatment plant and was aimed at optimising the selected technology. This article presents the results of the second stage of the full-scale investigation. The critical treatment process, for the analysed water, was the coagulation in an acidic environment (6.5 < pH < 7.0) carried out in a system with rapid mixing, a flocculation chamber, preliminary separation of coagulation products, and removal of residual suspended solids through filtration.


2006 ◽  
Vol 54 (3) ◽  
pp. 23-28 ◽  
Author(s):  
J. Rapala ◽  
M. Niemelä ◽  
K.A. Berg ◽  
L. Lepistö ◽  
K. Lahti

The removal of cyanobacteria, hepatotoxins produced by them (microcystins), phytoplankton, heterotrophic bacteria and endotoxins were monitored at a surface water treatment plant with coagulation, clarification, sand filtration, ozonation, slow sand filtration and chlorination as the treatment process. Coagulation–sand filtration reduced microcystins by 1.2–2.4, and endotoxins by 0.72–2.0 log10 units. Ozonation effectively removed the residual microcystins. The treatment process reduced phytoplankton biomass by 2.2–4.6 and heterotrophic bacteria by 2.0–5.0 log10 units. In treated water, the concentration of microcystins never exceeded the WHO guide value (1 μg/L), but picoplankton and monad cells were often detected in high numbers. The heterotrophic bacterial isolates from the treated waters belonged to genera Sphingomonas, Pseudomonas, Bacillus, Herbaspirillum and Bosea.


Desalination ◽  
1992 ◽  
Vol 88 (1-3) ◽  
pp. 3-31 ◽  
Author(s):  
JamesC. Lozier ◽  
Gary Smith ◽  
JerryW. Chapman ◽  
DavidE. Gattis

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