Case Study Regarding the Pre-Localisation of Water Losses within Water Distribution Networks with the Help of Noise Loggers

Alin Anchidin, Ilie Vlaicu and Katalin Bodor

Romaqua, 2015, vol. 103, issue 5, 40-46

Abstract: Noise loggers are modern devices that register and analyse noises from water distribution networks. The pre-localisation of water losses with the help of noise loggers has the advantage that they can register noises during a period of time without the assistance of the operator, as with classical methods. Noise loggers were used to pre-localise water losses within a ring network of 645km, made up of mains and secondary pipes with diameters between 80-1000 mm. The pipes are made of grey cast iron, asbestos cement, pre-compressed concrete 44% with ages between 50-100 years, steel 24% with ages between 20-50 years and PVC, polyethylene, polyester reinforced with glass fibre, cast iron not older than 30 years. There are 24,329 connections with a length of 198 km, 65% of which are made of polyethylene and PVC, 35% made of lead, Zn-coated steel and grey cast iron. The connections are 100% metered and the pressure within the network is between 2.0-2.3 atm. The Infrastructure Leakage Index (ILI) for the network of this case study is 30 with a real annual loss of 30,503m3/day. This paper presents the results obtained after the pre-localisation of water losses with the help of noise loggers in comparison to the classical pre-localisation methods. The use of noise loggers lead to the reduction of the time needed to verify the distribution network by 60%. The balance of the identified defects on the pipes increases in comparison to the classical pre-localisation methods as the identified defects are hidden within areas with high traffic, areas in which the classical pre-localisation methods did not work efficiently. The use of noise loggers for the pre-localization of water losses within the distribution network lead to a 4% decrease of water loss in two years.

Keywords: water; distribution; networks; water; losses; noise; loggers; case; study (search for similar items in EconPapers)
Date: 2015
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