NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring Data

[EN] Operations of water distribution networks (WDNs) are monitored daily via installed data loggers, where the collated hydraulic data can be leveraged to improve the system’s operations over time, and to minimize total economic losses due to non-revenue water (NRW). In collaboration with Public Ut...

Descripción completa

Detalles Bibliográficos
Autores: Chew, Alvin, Wu, Zheng, Kalfarisi, Rony, Xue, Meng, Pok, Jocelyn, Jianping, Cai, Lai, Kah, Hew, Sock, Wong, Jia
Tipo de recurso: capítulo de libro
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/206039
Acceso en línea:https://riunet.upv.es/handle/10251/206039
Access Level:acceso abierto
Palabra clave:Water distribution networks
Water losses estimation
Anomaly localization
Demand calibration
Hydraulic model calibration
Non-revenue water
id ES_650bac2d1417c644d052802edbba6dbd
oai_identifier_str oai:riunet.upv.es:10251/206039
network_acronym_str ES
network_name_str España
repository_id_str
spelling NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring DataChew, AlvinWu, ZhengKalfarisi, RonyXue, MengPok, JocelynJianping, CaiLai, KahHew, SockWong, JiaWater distribution networksWater losses estimationAnomaly localizationDemand calibrationHydraulic model calibrationNon-revenue water[EN] Operations of water distribution networks (WDNs) are monitored daily via installed data loggers, where the collated hydraulic data can be leveraged to improve the system’s operations over time, and to minimize total economic losses due to non-revenue water (NRW). In collaboration with Public Utility Board (PUB), Singapore’s National Water Agency, a practically novel model calibration approach using 24/7 monitoring flow and pressure data has been developed to facilitate PUB’s Smart Water Grid (SWG). The approach is developed as a generic integrated solution process to conduct a series of systematic analyses for daily WDN model calibration, namely: (1) estimating the system’s daily NRW contributions; (2) performing flow calibration that involves net demand consumption calibration, adjusting pumps operational configurations and localizing NRW sources when the system’s daily estimated NRW volume exceeds its assumed background volume; (3) performing energy calibration by rectifying possible drifting in monitored pressure head data and calibrating other physical properties which include, but not limited to, pipe roughness and valve settings, especially during peak-demand hours. The effectiveness of our proposed approach is subsequently tested on three WDN zones in Singapore, having a total pipe length of >100km, that comprises of atypical water usage patterns. The results of model calibration for one of three zones is presented in this paper. The key outcomes derived from the study are: (a) localized a reported leakage event by PUB to less than 100m; (b) calibrated the system’s flow balance, to less than 1% average mean absolute percentage error (MAPE), by first identifying and addressing the system’s billing data uncertainties, followed by localizing anomaly events that account for the total NRW volume estimated; and (c) calibrated the system’s pipe roughness values to improve the total energy balance by achieving an average daily MAPE of 4.0%.Editorial Universitat Politècnica de ValènciaNational Research Foundation SingaporeSingapore's National Water AgencyRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-03-06book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/206039reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2060392026-06-13T07:49:27Z
dc.title.none.fl_str_mv NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring Data
title NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring Data
spellingShingle NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring Data
Chew, Alvin
Water distribution networks
Water losses estimation
Anomaly localization
Demand calibration
Hydraulic model calibration
Non-revenue water
title_short NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring Data
title_full NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring Data
title_fullStr NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring Data
title_full_unstemmed NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring Data
title_sort NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring Data
dc.creator.none.fl_str_mv Chew, Alvin
Wu, Zheng
Kalfarisi, Rony
Xue, Meng
Pok, Jocelyn
Jianping, Cai
Lai, Kah
Hew, Sock
Wong, Jia
author Chew, Alvin
author_facet Chew, Alvin
Wu, Zheng
Kalfarisi, Rony
Xue, Meng
Pok, Jocelyn
Jianping, Cai
Lai, Kah
Hew, Sock
Wong, Jia
author_role author
author2 Wu, Zheng
Kalfarisi, Rony
Xue, Meng
Pok, Jocelyn
Jianping, Cai
Lai, Kah
Hew, Sock
Wong, Jia
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv National Research Foundation Singapore
Singapore's National Water Agency
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Water distribution networks
Water losses estimation
Anomaly localization
Demand calibration
Hydraulic model calibration
Non-revenue water
topic Water distribution networks
Water losses estimation
Anomaly localization
Demand calibration
Hydraulic model calibration
Non-revenue water
description [EN] Operations of water distribution networks (WDNs) are monitored daily via installed data loggers, where the collated hydraulic data can be leveraged to improve the system’s operations over time, and to minimize total economic losses due to non-revenue water (NRW). In collaboration with Public Utility Board (PUB), Singapore’s National Water Agency, a practically novel model calibration approach using 24/7 monitoring flow and pressure data has been developed to facilitate PUB’s Smart Water Grid (SWG). The approach is developed as a generic integrated solution process to conduct a series of systematic analyses for daily WDN model calibration, namely: (1) estimating the system’s daily NRW contributions; (2) performing flow calibration that involves net demand consumption calibration, adjusting pumps operational configurations and localizing NRW sources when the system’s daily estimated NRW volume exceeds its assumed background volume; (3) performing energy calibration by rectifying possible drifting in monitored pressure head data and calibrating other physical properties which include, but not limited to, pipe roughness and valve settings, especially during peak-demand hours. The effectiveness of our proposed approach is subsequently tested on three WDN zones in Singapore, having a total pipe length of >100km, that comprises of atypical water usage patterns. The results of model calibration for one of three zones is presented in this paper. The key outcomes derived from the study are: (a) localized a reported leakage event by PUB to less than 100m; (b) calibrated the system’s flow balance, to less than 1% average mean absolute percentage error (MAPE), by first identifying and addressing the system’s billing data uncertainties, followed by localizing anomaly events that account for the total NRW volume estimated; and (c) calibrated the system’s pipe roughness values to improve the total energy balance by achieving an average daily MAPE of 4.0%.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-03-06
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/206039
url https://riunet.upv.es/handle/10251/206039
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869409713285758976
score 15.812429

Similares en LA Referencia