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The investigation of real losses is of paramount importance for water utility willing to control operational costs and environmental and social impacts of water supply. Real losses reduction could be achieved by continuous maintenance actions and occasional pipe substitutions. Maintenance activities in the distribution network represent one of the largest items in a water utility economic balance and, therefore, any approach aimed at maintenance optimization catches the interest of water managers. These strategies are constrained by the amount of funds, which are usually available not in a single instalment but yearly and spread over a time period of several years. Several models have been developed for determining network components’ optimal replacement time and rehabilitation planning by means of a Decision Support System - DSS. DSS actually provides highly valuable indications for infrastructure state and rehabilitation actions to undertake. DSS can also be used to address real losses investigation campaigns in order to focus monitoring efforts in those areas where real losses are probability higher. In the present paper, a procedure for leak detention in a water distribution network is proposed. The procedure is based on network flow and pressure monitoring, joined together with numerical dynamic modelling. The comparison between monitoring and modelling response is used to highlight the network pipes where real losses are most probably present. The decisional outputs are the selection of the pipes which have to be better investigated by means of active search methods. Considering the errors in monitoring data and the simplification in numerical modelling, the approach is coupled with uncertainty analysis in order to provide a probable leakage distribution with a specified level of reliability. The procedure is applied to a laboratory case study and the results obtained show that the proposed procedure has the potential to be a useful tool for rehabilitation scheduling.


Session R58, Water Distribution Networks: Leak Detection II



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