scholarly journals Effectively Positioning Water Loss Event in Smart Water Networks

2015 ◽  
Author(s):  
Weiren Yu ◽  
Julie McCann
Keyword(s):  
Water Loss ◽  
Water Networks ◽  
Smart Water ◽  
Loss Event

scholarly journals Theoretical Estimation of Energy Balance Components in Water Networks for Top-Down Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1011
Author(s):  
Surachai Lipiwattanakarn ◽  
Suparak Kaewsang ◽  
Natchapol Charuwimolkul ◽  
Jiramate Changklom ◽  
Adichai Pornprommin
Keyword(s):  
Energy Balance ◽  
Water Loss ◽  
Distribution Systems ◽  
Minimum Energy ◽  
Head Loss ◽  
Water Networks ◽  
Top Down ◽  
Pressurized Water ◽  
Simple Method ◽  
Energy Balance Approach

The energy balance calculation for pressurized water networks is an important step in assessing the energy efficiency of water distribution systems. However, the calculation generally requires mathematical modelling of the water networks to estimate three important energy components: outgoing energy through water loss (El), friction energy loss (Ef) and energy associated with water loss (EWL). Based on a theoretical energy balance analysis of simplified pipe networks, a simple method is proposed to estimate El, Ef and EWL with minimum data requirements: input energy, water loss (WL) and head loss between the source and the minimum energy point (ΔH). By inclusion of the head loss in water networks into the estimation, the percentages of El and EWL are lower and higher, respectively, than using only the percentage of WL. The percentage of Ef can be a function of the percentage of ΔH. By demonstrating our analysis with the simulation results from the mathematical models of 20 real water networks, the proposed method can be used to effectively estimate El, Ef and EWL as a top-down energy balance approach.

scholarly journals Cyber-Physical Systems for Smart Water Networks: A Review

2021 ◽  
pp. 1-1
Author(s):  
Jyotirmoy Bhardwaj ◽  
Joshin P. Krishnan ◽  
Diego F. Larios Marin ◽  
Baltasar B. Lozano ◽  
Linga R. Cenkeramaddi ◽  
...  
Keyword(s):  
Cyber Physical Systems ◽  
Water Networks ◽  
Physical Systems ◽  
Smart Water

scholarly journals Adaptive Compressive Sensing in Smart Water Networks

2015 ◽  
Author(s):  
Sokratis Kartakis ◽  
George Tzagkarakis ◽  
Julie McCann
Keyword(s):  
Compressive Sensing ◽  
Water Networks ◽  
Smart Water

The power of smart water networks

2013 ◽  
Vol 105 (3) ◽  
pp. 72-77 ◽  
Author(s):  
Colin Walsby
Keyword(s):  
Water Networks ◽  
Smart Water

Pipe crack early warning for burst prevention by permanent acoustic noise level monitoring in smart water networks

2020 ◽  
Vol 17 (9) ◽  
pp. 827-837
Author(s):  
Chi Zhang ◽  
Martin F. Lambert ◽  
Mark L. Stephens ◽  
Jinzhe Gong ◽  
Benjamin S. Cazzolato
Keyword(s):  
Early Warning ◽  
Noise Level ◽  
Acoustic Noise ◽  
Water Networks ◽  
Smart Water ◽  
Level Monitoring

scholarly journals Decision Support System for Cyber Attack Diagnosis in Smart Water Networks

2019 ◽  
Vol 51 (34) ◽  
pp. 329-334
Author(s):  
Marcos Quiñones-Grueiro ◽  
Alberto Prieto-Moreno ◽  
Cristina Verde ◽  
Orestes Llanes-Santiago
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Cyber Attack ◽  
Water Networks ◽  
Smart Water

scholarly journals Divide and Conquer Partitioning Techniques for Smart Water Networks

2014 ◽  
Vol 89 ◽  
pp. 1176-1183 ◽  
Author(s):  
A. Di Nardo ◽  
M. Di Natale ◽  
G.F. Santonastaso ◽  
V. Tzatchkov ◽  
V.H. Alcocer Yamanaka
Keyword(s):  
Divide And Conquer ◽  
Water Networks ◽  
Smart Water

scholarly journals On-line Measuring Sensors for Smart Water Network Monitoring

10.29007/4fcr ◽  
2018 ◽  
Author(s):  
Armando Di Nardo ◽  
David Baquero Gonzalez ◽  
Tom Baur ◽  
Romeo Bernini ◽  
Sergio Bodini ◽  
...  
Keyword(s):  
Smart Cities ◽  
Information And Communications Technology ◽  
Divide And Conquer ◽  
Water Networks ◽  
Water Sector ◽  
Water Network ◽  
Network Efficiency ◽  
Smart Meters ◽  
Smart Water ◽  
On Line

Smart cities are getting essential to drive economic growth, increase social prospects and improve high-quality lifestyle for citizens. To meet the goal of smart cities, Information and Communications Technology (ICT) have a key role. The application of smart solutions will allow the cities to use ICT and big data to improve infrastructure and services (i.e. network efficiency, protection from contamination, etc.). In the water sector, the integration of smart meters and sensors coupled with cloud computing and the paradigm of “divide and conquer” introduces a novel and smart management of the water network allowing an efficient online monitoring and transforming the traditional water networks into modern Smart WAter Networks (SWAN). The Ctrl+SWAN (Cloud Technologies & ReaL time monitoring+Smart WAter Network) Action Group (AG) was created within the European Innovation Partnership on Water, in order to promote innovation in the water sector by advancing existing smart solutions. The paper presents an update of a previous work on the state of the art on the best On-line Measuring Sensors (OMS) already available on the market and innovative technologies in the Research and Development (R&D) phases.

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

2021 ◽  
Author(s):  
Peace Korshiwor Amoatey ◽  
Abena Agyeiwaa Obiri-Yeboah ◽  
Maxwell Akosah-Kusi
Keyword(s):  
Developing Countries ◽  
Water Balance ◽  
Relative Error ◽  
Water Loss ◽  
Performance Indicators ◽  
Water Utilities ◽  
Loss Reduction ◽  
Water Networks ◽  
Minimum Night Flow ◽  
Leakage Exponent

Abstract Methods for network leakage estimation include water balance, component analysis and minimum night flow (MNF) methods the latter of which involves subtracting the customer night use (QCNU) from night leakage and multiplying by the hour day factor (HDF). QCNU and HDF respectively depend on Active Night Population (ANP) and leakage exponent (N1). In most developing countries, these parameters are assumed in the MNF method thus introducing errors which makes setting realistic leakage reduction targets and key performance indicators (KPI) problematic. In this study, QCNU and HDF were evaluated by determining the relative error associated with ANP and N1 to establish localized rates for accurately estimating leakage in water networks. Between 7 and 11% relative error was associated with every 1% higher or lower ANP while up to 4% relative error was observed for every step considered. A linear relationship exists between the relative error associated with both and ANP although that of ANP is twice as high as This has technical implications on setting water loss reduction targets and investing in the water infrastructure. It is recommended that water utilities must establish localized ANP and values for accurate leakage estimation in water networks.

Sign in / Sign up

Export Citation Format

Share Document