The chlorine concentration variation in Baghdad water networks was studied. The
chlorine data were collected from Mayoralty of Baghdad and Ministry of Environment
(MOE) for the networks for both sides of the city Karkh and Rasafa for (2008-2009). The
study of these data indicates that there are no systematic testing program .Classified GIS
maps showed that the areas far from the treatment plants have almost always low
chlorine concentration .This indicates that the problem of the low chlorine concentration
in the far areas is due to cracks of pipe along the conveyance path ,as expected. The area's
most frequently have low concentration are Al-sadir,Al-Kadhimya, and Al-Amiria . It
was found also that the chlorine concentrations were lowest in summer months than those
in winter months.The Amiria area district (636) was selected as a case study to test the
ability of using the quantitative- qualitative model in the EPANET software, to find the
required onsite chlorine injection point number, locations and dose, so as to raise the
chlorine concentration to the acceptable limits in the other nodes of the network. The
bulk decay coefficient was found to be (-2.212)1/day and the wall coefficients were
found to be between (-0.001)to(-0.9)1/day The main conclusion of this study is that the
onsite injection can improve the chlorine concentration in Baghdad water supply
networks. The EPANET model can be used effectively to obtain the required injection
program for this purpose.
English
Loanwords are the words transferred from one language to another, which become essential part of the borrowing language. The loanwords have come from the source language to the recipient language because of many reasons. Detecting these loanwords is complicated task due to that there are no standard specifications for transferring words between languages and hence low accuracy. This work tries to enhance this accuracy of detecting loanwords between Turkish and Arabic language as a case study. In this paper, the proposed system contributes to find all possible loanwords using any set of characters either alphabetically or randomly arranged. Then, it processes the distortion in the pronunciation, and solves the problem of the missing lette
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