Analysis of the effect of pressure control on leakages in distribution systems by FAVAD equation and field applications

Abstract

Pressure has an important effect on the occurrence of failures/leaks in water distribution systems (WDSs) or the change of leakage in existing leakages. For this reason, monitoring the pressure is important especially for analyzing the changes in the day and night, determining the fluctuations and applying pressure management (PM) to ensure normal operating conditions. In this study, the effect of pressure on water losses and minimum night flow (MNF) was carried out according to the Fixed and Varied Area Discharge (FAVAD) approach which allows the amount of leakage to be calculated based on the change in pressure and field tests. The minimum flow rate and potential leakage were determined under the network operating conditions before the pressure control in region. Then, considering the features of the region, pressure was reduced with the pressure control system and MNFs and leaks were monitored. By reducing the pressure from 9.10 bar to 3.2 bar in the region, the MNF rate was reduced from 6.95 l/s to 3.29 l/s. The daily water savings in the system inlet volume is 78.44 m3/day and the annual saving is 28,624 m3 /year. The results obtained are very important for practitioners in terms of implementing PM in the field.

Modeling and optimization of pressure and water age for evaluation of urban water distribution systems performance

“…This causes increasing leakage and burst of pipe. For this reason, in order to reduce the existing leakage in the region active leakage control method should be applied so as to get the whole reported data for further investigation ( Özdemir et al., 2021 ). The existing system is excessively imbalanced and inefficient in delivering water to meet demand in all sections of the system, as shown in Figure 4 ( Awe et al., 2020 ).…”

Model-Based Approach for Leak Detection and Localization in Water Distribution Networks: A Literature Survey

“…These investigations show that leak areas are not stable but instead change with system pressure, which is the main reason for this discrepancy. Since the geometry, size, or area of the opening may change over time, further research studies [8,[28][29][30][31][32] have discovered that the leak flow via a pipe opening will also depend on these factors. For instance, in Table 1, the N1 exponent for different pipe materials and leak geometry is illustrated.…”

Impact of active night population and leakage exponent on leakage estimation in developing countries

“…Another parameter under investigation is the HDF sometimes called night day factor (NDF) is a dimensionless parameter that presupposes that leakage and pressure are related, thus, by controlling pressure, water losses can be reduced (García et al 2006;Cheung et al 2010;AL-Washali et al 2019;Özdemir et al 2021). It is known from the Morrison et al (2007) study that, gravity-fed networks and low-pressure gravity systems usually have HDF less than or equal to 24 hours per day due to high frictional head losses.…”

“…The determination of N1 is specific to any distribution network and is obtained from field studies by gradually reducing flow into a section of the network and recording changes in pressure over a period of time (Lambert 2001;Tabesh & Asadiani Yekta 2005;García et al 2006;Thornton et al 2008;Cheung et al 2010). N1 values have been found to typically range from 0.5 to 2.5 (García et al 2006;Tabesh et al 2009;Cheung et al 2010;Negharchi & Shafaghat 2020;Özdemir et al 2021) depending on pipe material and level of leakage. The more rigid the pipe material, the closer the N1 is to 0.5.…”

“…Studies by Knobloch et al (2014) and Farley et al (2011) indicate that the steps in addressing the leakage problem are fourfold; identify how much is lost, where the losses occur, why they occur and how they can be reduced. To determine how much is lost, three methods namelywater balance (Farley et al 2011;Knobloch et al 2014), minimum night flow (MNF) (García et al 2006;Tabesh et al 2009;Xin et al 2014) and component analysis also known as the Burst and Background Estimates (BABE) (McKenzie 2003;Fanner & Thornton 2005;Özdemir et al 2021) have been identified and have been applied to the case study network in Amoatey et al (2018).…”