Identification of leakages by calibration of WDS models

Open Access journalCopyright © 2013 The Authors. Published by Elsevier Ltd.12th International Conference on Computing and Control for the Water Industry, CCWI2013Leakage detection is critical for the proper management of water distribution systems (WDS). This paper proposes a leak detection approach based on a Bayesian calibration method. The methodology uses a newly formulated index, μ, which takes into account the variation of roughness in pipes between the calibrated models with and without leaks. Case studies, which use literature networks, are presented to demonstrate how the approach can be used in identifying pipes with losses. The approach starts with a calibration method followed by the analysis of sensitivity matrices. The approach proved to be effective in finding leaks, but the results depend crucially on the number and quality of the observed data.European CommissionEuropean Social FundRegion of Calabri

Emergency Management of Water Distribution Systems: The Nodal Demand Control

Abstract During emergency situations (e.g., due to pipe bursts or other network failures), appropriate management of Water Distribution Systems (WDS) is required. Critical events often cause service failures, because the pressure head in some nodes of the network become inadequate to deliver required demand. In this paper, a new methodology is developed based on the nodal demand control. with the aim to increase the pressure head, and hence the flow rate actually delivered at critical nodes (i.e., hospitals, vulnerable customers, etc.). This is done to avoid or minimize service interruptions between the failure and the repair times. Furthermore, a pipe burst can cause isolation of a portion of the network such that the flow along pipes changes and this causes the reduction of head in some nodes. The proposed methodology is manages the delivered flow rate using a Pressure Driven Analysis (PDA) approach. This is based on operating control of valves and by identifying the nodes where the pressure control should be implemented. Those control nodes are chosen by the analysis of sensitivity matrices and the Max-Sum Method (Bush and Uber, 1998; Fiorini Morosini et al., 2014). The methodology is demonstrated on a case study for a real network of Cosenza, a town in the South of Italy