The Special Issue on Advances in Water Distribution Networks (WDNs) explores four important topics of research in the framework of WDNs, namely simulation and optimization modelling, topology and ...partitioning, water quality, and service effectiveness. With regard to the first topic, the following aspects are addressed: pressure-driven formulations, algorithms for the optimal location of control valves to minimize leakage, the benefits of water discharge prediction for the remote real time control of valves, and transients generated by pumps operating as turbines. In the context of the second topic, a topological taxonomy of WDNs is presented, and partitioning methods for the creation of district metered areas are compared. In relation to the third topic, the vulnerability to trihalomethane is assessed, and a statistical optimization model to minimize heavy metal releases is presented. Finally, the fourth topic focusses on the estimation of non-revenue water, including leakage and unauthorized consumption, and on the assessment of service under intermittent supply conditions.
AbstractThis paper proposes a system-characteristics and graph theory–based water distribution system (WDS) model classification scheme that is based on system function and topology. Various ...parameters are examined to determine the most adequate parameter(s) for describing a WDS. The classification scheme is applied to a single hypothetical and 25 real systems. The primary indicator to classify a WDS function (transmission or distribution networks) is the length-weighted average pipe diameter. The average nodal demand and histogram of total length of each pipe diameter are applied as secondary measures. A new parameter, defined as the branch index (BI), is used to further classify a branched network by estimating the degree of branching within a WDS. The degree of looping and a second level of classification are based on the meshedness coefficient (MC), but only after the system is reduced to eliminate nonessential nodes. BI values are compared with other system-structure metrics in the literature including link density (LD), average node degree (AND), MC, and clustering coefficient (CC). To that end, Pearson correlation coefficients are computed across the set of other metrics for 26 systems. The correlation analysis reveals that several graph-theory system-structure metrics are highly correlated.
Water utilities are vulnerable to a wide variety of human-caused and natural disasters. The Water Network Tool for Resilience (WNTR) is a new open source Python™ package designed to help water ...utilities investigate resilience of water distribution systems to hazards and evaluate resilience-enhancing actions. In this paper, the WNTR modeling framework is presented and a case study is described that uses WNTR to simulate the effects of an earthquake on a water distribution system. The case study illustrates that the severity of damage is not only a function of system integrity and earthquake magnitude, but also of the available resources and repair strategies used to return the system to normal operating conditions. While earthquakes are particularly concerning since buried water distribution pipelines are highly susceptible to damage, the software framework can be applied to other types of hazards, including power outages and contamination incidents.
•Water Network Tool for Resilience (WNTR) is a new open source Python package.•WNTR can model a wide range of disruptive incidents and repair strategies.•An earthquake case study is used to demonstrate capabilities in WNTR.•Resilience metrics include water service availability and population impacted.
AbstractThis paper presents an analytical algorithm for simultaneous least-cost design and operation of looped water distribution systems (WDSs). This method could be used to replace evolutionary ...methods (which are typically used to solve the design-operation problem), or it can be used in conjunction with evolutionary algorithms to enhance their performance (i.e., a hybrid approach). Unlike previous studies that propose analytical methods for split-pipe or continuous diameter design, the developed method addresses a more realistic case in which the pipe design is restricted to commercially available discrete diameters. The analytical approach consists of three stages. In the first stage, a reformulated linear programming (LP) method is used to find the least-cost design of a WDS for a given set of flow distribution while allowing a pipe-split in the solution. In the second stage, the equivalent pipe diameters of the split-pipe design are calculated and modified to discrete pipe diameters by applying a rounding-up strategy to the next commercially available pipe diameter. In the third stage, a nonlinear programming (NLP) method is used to find a new flow distribution that reduces the cost of the WDS operation given the design of the second stage. It is shown in this study that the results produced by the analytical method outperform the results of evolutionary methods when compared to previously published studies. Moreover, when a hybrid approach is adapted, the analytical method can be used to initialize the evolutionary algorithm to gain enhanced performance. The results of the hybrid approach fine-tune those obtained from the analytical method and demonstrate a substantial improvement when compared to a standard evolutionary algorithm initialized with a randomly generated initial population.
AbstractWater loss reduction is important in sustainable water resource management. As one of the main water loss control methods, early detection of hydraulic accidents in district metering areas ...(DMAs) has emerged as a research focus. This study presents a data-driven method for burst detection which consists of three stages: prediction, classification and correction. A prediction stage is used to improve accuracy of flow prediction, a classification stage utilizes multiple thresholds to make the method robust to time variation, and an outlier feedback correction stage allows consecutive detection of outliers. The proposed method was capable of triggering burst alarms with 99.80% detection accuracy (DA), 85.71% true-positive rate (TPR), and 0.14% false-positive rate (FPR) in simulated experiments, and 99.77% DA, 94.82% TPR and 0.21% FPR in synthetic experiments over a 10-min detection time in a real-life DMA. The identifiable minimum burst rate was as low as 2.79% of average DMA inflow. The proposed method outperformed the single threshold-based method, window size–based method, and clustering-based method. It provides a sensitive and effective solution for burst detection in water distribution systems.
This study focuses on data-driven approaches for burst detection and classifies them into three categories: classification method, prediction-classification method and statistical method. The ...performance of these methods is discussed. By analysing uncertainty in burst detection, this paper revealed that non-stationary monitoring data and limitations present in these methods challenge the reliability of detection results. Data pre-processing and probabilistic solutions to deal with the uncertainty are summarised. From these findings and discussions, this paper concludes and recommends that: a) data-driven approaches are promising in real-life burst detection and reducing false alarms is an important issue; b) more comprehensive performance evaluation might be necessary, in particular regarding detectable burst size; c) further research on new methods employing multivariate analysis and a new category based on clustering analysis would be beneficial to tackle uncertainty; d) more focus on the use of pressure data might facilitate burst location and reduce investment in burst detection.
•The idea of characterizing the dynamics of WDNs via multilayer networks is introduced.•Demand variations and operational status of sources, pumps & valves are considered.•Correlations between the ...operational status of sources & pumps and the general dynamics of WDNs unearthed.•Vulnerability indexes can now be tied with different temporal windows.•Routine maintenance can now be optimally scheduled.
Nodal demands vary throughout the day, as such any vulnerability analysis based on static networks, which considers daily average demands cannot realistically represent the criticality of nodes in the network. This study presents a systematic framework, which couples multilayer networks, structural reducibility and a Demand Adjusted Vulnerability Measure for dynamic nodal vulnerability assessment of water distribution networks (WDNs) under extended period simulation. Within this framework, we present the novel idea of characterizing the dynamics of WDNs with multi-slice networks, which captures the state of the network within a predefined temporal window taking into consideration the directional flow in pipes and the operational status of pumps, valves etc. Using a benchmark WDN, Net 3, as a case study we have demonstrated the importance of demand variations and operational status of various components, no matter how minuscule their operational time, on nodal vulnerability assessment in WDNs. The results indicated that the framework evaluates the criticality of all types of nodes, even intermediary nodes with zero base demand, within any temporal window much more realistically than conventional vulnerability analysis methods based on single (static) networks. Structural reducibility unearthed correlations between the operational status of source nodes and pumps on the general dynamics of the distribution system. The multilayer framework opens a new frontier in vulnerability analysis of WDNs and could serve as a tool for stakeholders in accessing node criticality, impact of various failure scenarios and optimal scheduling of maintenance routines.
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