A Proof-of-Concept Study for Hydraulic Model-Based Leakage Detection in Water Pipelines Using Pressure Monitoring Data
A Proof-of-Concept Study for Hydraulic Model-Based Leakage Detection in Water Pipelines Using Pressure Monitoring Data
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DOI：
10.3389/frwa.2021.648622
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Ahmad Momeni;K. Piratla
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文献类型：
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作者：
Ahmad Momeni;K. Piratla
据估计，在美国，大约20%的经处理的饮用水通过分配管道泄漏而损失。管道泄漏检测是全球自来水公司的首要任务，因为泄漏增加了运行能源消耗，如果不加以解决，还可能发展为潜在的灾难性自来水干管破裂。泄漏检测是一项费力的任务，往往受到公用事业公司负担得起的财力和人力资源的限制。许多传统的泄漏检测技术也仅提供泄漏存在的快照指示。此外，在饮用水应用中越来越受欢迎的塑料管道上的许多泄漏检测技术的可靠性也是值得怀疑的。作为智能供水系统框架的一部分，本文提出并验证了一种基于水力模型的技术，用于通过监测整个供水系统(WDS)的压力来检测和评估埋地供水管道泄漏的严重程度。设想的智能水公用事业框架需要能够从有限数量的WDS节点收集用水量数据，并从放置在WDS上的有限数量的压力监测站收集压力数据。一个流行的基准WDS最初是通过增加孔口节点来引起泄漏来进行修改的。通过孔口节点的滴头系数控制泄漏严重程度。随后，从在该改进的配电网中放置压力监测站的位置收集各种需求的WDS压力数据。随后采用进化优化算法预测灌水器系数，从而根据监测到的压力数据对各种节点需求的水力依赖性来确定泄漏严重程度。为了提高计算效率，采用人工神经网络(ANN)来模拟目前流行的水力解算器EPANET2.2。本研究的目的是：(1)验证所提出的用于检测和评估泄漏严重程度的建模方法的概念证明；(2)评估预测精度对收集和使用消耗数据的压力监测站数量和需求节点数量的敏感性。这项研究通过基于水力模型的方法预测泄漏，为确定管道修复的优先顺序提供了新的价值。
It is estimated that about 20% of treated drinking water is lost through distribution pipeline leakages in the United States. Pipeline leakage detection is a top priority for water utilities across the globe as leaks increase operational energy consumption and could also develop into potentially catastrophic water main breaks, if left unaddressed. Leakage detection is a laborious task often limited by the financial and human resources that utilities can afford. Many conventional leak detection techniques also only offer a snapshot indication of leakage presence. Furthermore, the reliability of many leakage detection techniques on plastic pipelines that are increasingly preferred for drinking water applications is questionable. As part of a smart water utility framework, this paper proposes and validates a hydraulic model-based technique for detecting and assessing the severity of leakages in buried water pipelines through monitoring of pressure from across the water distribution system (WDS). The envisioned smart water utility framework entails the capabilities to collect water consumption data from a limited number of WDS nodes and pressure data from a limited number of pressure monitoring stations placed across the WDS. A popular benchmark WDS is initially modified by inducing leakages through addition of orifice nodes. The leakage severity is controlled using emitter coefficients of the orifice nodes. WDS pressure data for various sets of demands is subsequently gathered from locations where pressure monitoring stations are to be placed in that modified distribution network. An evolutionary optimization algorithm is subsequently used to predict the emitter coefficients so as to determine the leakage severities based on the hydraulic dependency of the monitored pressure data on various sets of nodal demands. Artificial neural networks (ANNs) are employed to mimic the popular hydraulic solver EPANET 2.2 for high computational efficiency. The goals of this study are to: (1) validate the proof of concept of the proposed modeling approach for detecting and assessing the severity of leakages and (2) evaluate the sensitivity of the prediction accuracy to number of pressure monitoring stations and number of demand nodes at which consumption data is gathered and used. This study offers new value to prioritize pipes for rehabilitation by predicting leakages through a hydraulic model-based approach.