Water Distribution Network Models Research Articles

Reshaping water distribution topology from the carbon emission reduction perspective: An exploratory analysis of water distribution reliability

As urban economies continue to evolve, the water distribution networks (WDNs) are expanding in scale and becoming more interconnected, leading to increased carbon emissions from operations and maintenance. Consequently, enhancing the stability and safety of WDNs while saving energy has emerged as a primary research focus. This study abandoned the original use of high economic costs for post-maintenance of WDNs. Instead, it reshaped the traditional water distribution topology to form a dynamic, storable, energy-efficient "WDN self-help" model. Drawing inspiration from the "deep tunnel" project in drainage systems, the proposal was to leverage underground spaces to create a deep aqueduct (DA) complementing the traditional WDN, forming a three-dimensional (3D) WDN. Hydraulic and water quality analyses of varying scales of the 3D WDN model demonstrated its superior ability to equalize node pressures, reduce pipeline head losses, and maintain water quality for end-users. Reliability assessments of the 3D WDN revealed enhanced system robustness for medium-to large-scale distributions, while energy consumption analyses indicated a significant increase in water supply energy utilization and significant long-term reductions in carbon footprint. A practical case study was presented to validate the effectiveness of the 3D WDN concept, confirming its ability to reliably distribute water even in the event of a failure. Finally, an estimate of the retrofit cost and the static payback period of the 3D WDN was conducted. This study aims to provide a theoretical reference for the renovation of water supply projects or the optimal design of new WDNs in the context of carbon neutrality.

A deep-level decomposed model to accelerate hydraulic simulations in large water distribution networks

As the size of water distribution network (WDN) models continues to grow, developing and applying real-time models or digital twins to simulate hydraulic behaviors in large-scale WDNs is becoming increasingly challenging. The long response time incurred when performing multiple hydraulic simulations in large-scale WDNs can no longer meet the current requirements for the efficient and real-time application of WDN models. To address this issue, there is a rising interest in accelerating hydraulic calculations in WDN models by integrating new model structures with abundant computational resources and mature parallel computing frameworks. This paper presents a novel and efficient framework for steady-state hydraulic calculations, comprising a joint topology-calculation decomposition method that decomposes the hydraulic calculation process and a high-performance decomposed gradient algorithm that integrates with parallel computation. Tests in four WDNs of different sizes with 8 to 85,118 nodes demonstrate that the framework maintains high calculation accuracy consistent with EPANET and can reduce calculation time by up to 51.93 % compared to EPANET in the largest WDN model. Further investigation found that factors affecting the acceleration include the decomposition level, consistency of sub-model sizes and sub-model structures. The framework aims to help develop rapid-responding models for large-scale WDNs and improve their efficiency in integrating multiple application algorithms, thereby supporting the water supply industry in achieving more adaptive and intelligent management of large-scale WDNs.

Pipe roughness calibration approach for water distribution network models using a nonlinear state observer

This paper introduces an online approach based on a nonlinear state observer (NSO) to calibrate the roughness of each pipe within a water distribution network (WDN) or a sector thereof. The NSO is designed to obtain the estimations of pipes' friction factors, which are then used to estimate the roughness. The core of the NSO is a dynamic WDN model formulated through a structured set of ordinary differential equations derived from fundamental physical principles and taking advantage of both graph theory and rigid water column theory. By applying a coordinate transformation, the WDN model is represented as a fully connected network of damped nonlinear oscillators, with each oscillator formulated as a Liénard system. This representation allows for estimating the friction factors for each pipe using only flow rate information. The proposed approach facilitates a continuous calibration when hydrodynamic data are readily accessible, which is a capability that empowers engineers to enhance, concurrently or proactively, the day-to-day operations of water distribution networks, such as control or diagnose tasks, whenever data are available. The results of numerical simulations are presented to illustrate the practical utility of the proposed method.

Burst Diagnosis Multi-Stage Model for Water Distribution Networks Based on Deep Learning Algorithms

Pipe bursts in water distribution networks (WDNs) pose significant threats to the safety of distribution networks, driving attention to deep learning-based burst detection and localization. However, the applicability of different pressure features still needs to be compared and verified. A large number of nodes challenges deep learning with the excessive number of classification categories and low recognition accuracy. To address these problems, this paper extracts different burst pressure features, including pressure value, pressure difference, and pressure fluctuation ratio, and inputs one of these features into a Burst Diagnosis Multi-Stage Model (BDMM) based on three CS-LSTMs (a combination of the Cuckoo Search algorithm and a long short-term memory network). The first model addresses a binary classification problem, outputting labels indicating whether a pipe burst has occurred. The second one solves a multi-classification problem, outputting the label of the burst partition, and the third model also solves a multi-classification problem, outputting the ID of the bursting junction. The model is tested on a real network and outperforms ELM. For basic burst identification tasks using CS-LSTM, differences among the three features are minimal, while pressure difference and pressure fluctuation ratio exhibit superior performance to pressure value when resolving more complex problems like burst junction localization.

The Hourly Peak Coefficient of Single-Family and Multi-Family Buildings in Poland: Support for the Selection of Water Meters and the Construction of a Water Distribution System Model

Taking care of water resources and minimizing water losses in water supply networks requires a broad approach to identifying and neutralizing operational problems. The correct selection of water meters to minimize apparent losses requires knowledge of the characteristic flows that may occur in the facility to which water is supplied. The research aimed to develop tools in the form of mathematical models and water consumption curves along with hourly water consumption coefficients to facilitate the process of selecting water meters for engineers and creating computer models of water supply systems. The research involved monitoring the flow of 76 single-family and multi-family buildings in four towns in Poland, followed by data analysis and development of tools supporting the selection of water meters and the construction of computer models of water distribution networks. High correlation coefficients of the studied variables indicate the results’ usefulness. Four models were developed to determine the maximum flow values in multi-family buildings (three models) and single-family buildings (one model) in the range of water meter diameters DN15-DN40. Characteristics of the average hourly peak coefficient (HPC) values were also developed, along with the range of changes in HPC values for single-family and multi-family buildings.

A Multi-step Data Assimilation Framework to Investigate the Effect of Measurement Uncertainty in the Reduction of Water Distribution Network Model Errors

Water distribution network (WDN) models are a common decision support tool for understanding the behavior and performance of WDNs, aiding in the planning and management of WDN systems. The increasing availability of real-time data has recently promoted the exploration of Data Assimilation (DA) techniques to improve these models. However, flow, pressure and demand data are uncertain, particularly due to sensor characteristics such as precision and noise. An open question is to what extent DA can still improve hydraulic models when the data used to this end is uncertain. This paper proposes a three-step Ensemble Kalman Filter based DA approach for WDNs (3-EnKF-WDN), building on previous approaches, and advancing in two main fronts: the use of extended period simulation, and the use of pressure-dependent demand (PDD) analysis. Different scenarios considering uncertain sensor data, with varied precision and noise, are applied to two networks of different sizes, representative of real-world WDNs. The computational demand of the 3-EnKF-WDN method is also assessed. Results show that increasing sensor’s precision and decreasing the noise in state measurements reduce model error, as expected. However, we also found that model errors: 1) are reduced more effectively by using 3-EnKF-WDN than by increasing sensors’ precision; 2) are not reduced if certain noise thresholds are surpassed; 3) can be reduced without assimilating demand data if the WDNs are fully monitored with head sensors in all the nodes and flow sensors in all the links.

Improving Water Distribution Network Models with Data Assimilation: Unleashing the Power of Real-Time Improvements

Ensuring a reliable water supply in the face of changing conditions and growing demand is a critical global challenge. Water distribution networks (WDNs) are essential infrastructure, but traditional modeling methods based on historical data often struggle to adapt in real-time and integrate new information. In order to lower model errors in WDN models, this study explores the use of a Data Assimilation (DA) method that makes use of the Ensemble Kalman Filter. The study explores the effectiveness of the DA method, and a novel Greedy Algorithm (GA) for optimizing the location of sensors. The study shows that the DA method improves the WDN model and the GA is able to successfully determine optimal sensor locations. It was observed that increasing the number of sensors in the WDN increased the effectiveness of the DA method. This study highlights the potential of data assimilation to improve WDN modeling. Water utilities can gain from more precise predictions, greater system performance, and efficient decision-making for the management and maintenance of water distribution networks by allowing models to adapt and improve dynamically

A Review of Hydraulic Pressure for Water Supply Unites Considering Iraqi Geodetic Reference

The hydraulic pressure head of the flowing water (between the entrance and exit pipes) in water distribution networks depends on the length and the slope of these pipes, which can represent the head losses for each meter length, where the slope and length of the water supply network pipes are dependent on specified datum ground elevations. This ground elevation may result in a reduction in head losses with hydraulic grade pressure. As a result, the precision of the measured data used to observe ground elevations is critical to decrease the error of these losses and apply an accurate local reference. It's critical to comprehend the implications of using Geographic Information System (GIS) application technology for water distribution network models to evaluate the hydraulic grade head with Iraqi Geodetic Reference. These GIS input data are based on an analysis of the relationship between selected spatial data resolution and the precision of the expected hydraulic head for selected stations with a distribution network. This research aims to review the literature and try to track the conversion of geodetic references and datums to accurately identify the hydraulic pressure for the selected locations of the water supply network. It was found that recent researches tend to use GIS application to create novel methods to reduce head losses and error.

Application of Strength Pareto Evolutionary Algorithm II in Multi-Objective Water Supply Optimization Model Design for Mountainous Complex Terrain

Water distribution networks (WDN) model optimization is an important part of smart water systems to achieve optimal strategies. WDN optimization focuses on the nonlinearity of the discharge head loss equation, the availability of discrete properties of pipe sizes, and the conservation of constraints. Multi-objective evolutionary algorithms (MOEAs) have been proposed and successfully applied in the field of WDN design optimization. Previous studies have focused on comparing the optimization effects of algorithms in water distribution networks, ignoring the problems of unbalanced pressure distribution and water hammer at the nodes of the pipe network caused by the complex terrain in mountainous areas. In this paper, a multi-objective water supply optimization model that integrated cost, reliability, and water quality was established for a mountainous WDN in real engineering. The method of traversing the nodes to solve the water age was introduced to find a more scientific and practical water age solution model, with setting the weight function to evaluate the water age of the water supply model comprehensively. Strength Pareto Evolutionary Algorithm II (SPEA-II) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) were adopted to optimize the WDN design model in the mountainous complex terrain. The significance levels of the number of Pareto solutions (NOPS) and running time are 0.029 and 0.001, respectively, indicating that the two algorithms have significant differences. Compared to NSGA-II, SPEA-II has a better convergence rate and running time in multi-objective water supply optimization design. The solution set distribution of SPEA-II is more concentrated than that of NSGA-II, also the numerical value is better. The number of SPEA-II optimization schemes is larger and the scheme is more effective. Among them, the Pareto solution set of SPEA-II can obtain more desirable optimization results on cost, reliability index (RI) and water age. In summary, the study provides valuable information for decision makers in WDN with complex terrain.

Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment

Representing reality in a numerical model is complex. Conventionally, hydraulic models of water distribution networks are a tool for replicating water supply system behaviour through simulation by means of approximation of physical equations. A calibration process is mandatory to achieve plausible simulation results. However, calibration is affected by a set of intrinsic uncertainty sources, mainly related to the lack of system knowledge. This paper proposes a breakthrough approach for calibrating hydraulic models through a graph machine learning approach. The main idea is to create a graph neural network metamodel to estimate the network behaviour based on a limited number of monitoring sensors. Once the flows and pressures of the entire network have been estimated, a calibration is carried out to obtain the set of hydraulic parameters that best approximates the metamodel. Through this process, it is possible to estimate the uncertainty that is transferred from the few available measurements to the final hydraulic model. The paper sparks a discussion to assess under what circumstances a graph-based metamodel might be a solution for water network analysis.

A parallel computing architecture based on cellular automata for hydraulic analysis of water distribution networks

Water distribution networks (WDNs) are one of the largest infrastructures in society. Various methods for formulation and hydraulic analysis of water distribution networks, including numerical and non-numerical methods, have been previously proposed. Due to the complexity, the nonlinearity of the hydraulic equations of water distribution networks, and the need for multiple executions and uncertainties in parameters, solving the hydraulic model of water distribution networks has high time complexity. In this paper, a parallel computational architecture based on the concept of cellular automata is proposed to accelerate the numerical solution of the steady-state water distribution network model. Taylor series is proposed to solve hydraulic equations. The presented architecture was implemented as a parallel hardware platform on a field-programmable gate array. The performance of the proposed method was compared with EPANET software for networks with different complexities and topologies. The results show that the proposed parallel algorithm can accelerate the hydraulic analysis of regular water distribution networks up to 700 times and 250 times for small and large networks, respectively.

Pipe and isolation valve failure-impact analysis and prioritization model for an urban water distribution network

Abstract Pipe and isolation valve failure in an urban water distribution network (WDN) causes service interruption to the water users. It is important to identify and prioritize the maintenance of the most severe impact-causing pipes and valve failures. This study investigates the impacts of such failures in terms of the number of isolation elements, the number of affected customers, and the supply shortfall (SS). The study proposes an impact-based prioritization model for pipe and isolation valve repair/replacement in a WDN using the analytic hierarchy process (AHP). The WDN modeling, simulation, and generation of segments are carried out using the WaterGEMS software. The proposed methodology is illustrated with the help of a real-time WDN of Dire Dawa city in Ethiopia. Through the study, it is noticed that each valve/pipe failure has varied impacts and gets reduced with the increase in valve density. Further, the supply shortfall is the most important parameter for prioritizing the maintenance. The failure of the valves and pipes significantly affects the system's performance and should be repaired/replaced on priority basis. It is hoped that the proposal will help the decision-makers in the optimal utilization of limited resources available for repair/ maintenance.

MAGNets: Model Reduction and Aggregation of Water Networks

Hydraulic models of large water distribution networks can have thousands of components, and real-time simulation of these systems can be slowed down by their complexity. Several methods have been developed for which the size of the water network models can be reduced while the hydraulic performance of the reduced systems remains very similar to the original model. Model reduction allows users to model only components of interest, thus saving expensive computation time. However, while model reduction has been widely adopted for control and optimization purposes, few tools are available to reduce models on command. This paper introduces MAGNets, an open-source Python package capable of reducing and aggregating EPANET-compatible water models using the variable elimination reduction method. The package allows the user to specify the operating point around which the model will be reduced, the nodes that must remain in the network, and the maximum nodal degree of nodes removed. The reduced model preserves pressure heads of the original model with high accuracy and results in faster running times. The reduction algorithm iteratively removes nodes from the full model and updates the adjacent nodal demands and pipe properties until the final reduced model is achieved. To test the effect of the order of node removal on the size of the reduced model and computational complexity, we tested three different strategies for reduction order. Results suggest that dynamically updating reduction order greatly improves model performance compared to static and random orders. To increase usability, the Python package includes twelve benchmark networks for testing and validation. MAGNets allows for the quick and efficient reduction of hydraulic models as a way to facilitate time-sensitive tasks, such as real-time state estimation and control.

Review of Urban Drinking Water Contamination Source Identification Methods

When drinking water flows into the water distribution network from a reservoir, it is exposed to the risk of accidental or deliberate contamination. Serious drinking water pollution events can endanger public health, bring about economic losses, and be detrimental to social stability. Therefore, it is obviously crucial to research the water contamination source identification problem, for which scholars have made considerable efforts and achieved many advances. This paper provides a comprehensive review of this problem. Firstly, some basic theoretical knowledge of the problem is introduced, including the water distribution network, sensor system, and simulation model. Then, this paper puts forward a new classification method to classify water contamination source identification methods into three categories according to the algorithms or methods used: solutions with traditional methods, heuristic methods, and machine learning methods. This paper focuses on the new approaches proposed in the past 5 years and summarizes their main work and technical challenges. Lastly, this paper suggests the future development directions of this problem.

Online Edge Flow Imputation on Networks

An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respectively via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</i> a sparse line graph identification strategy based on a group-Lasso and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ii)</i> a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation. The advantages of this first SC-based attempt for time-varying signal imputation have been demonstrated through numerical experiments using EPANET models of both synthetic and real water distribution networks.

Port-Hamiltonian based control of water distribution networks

We design controllers for a nonlinear model of a water distribution network (WDN). Most existing approaches to the control of WDNs model the pumps as simple pressure gains. We show that a commonly used empirical model of a pump leads to a port-Hamiltonian (pH) and hence stable system. We use standard arguments to show that WDNs are incrementally pH, and show that local PI controllers preserve stability. These controllers are robust in that they do not require specific knowledge of the equilibrium input. We further show that controllers based on physical feedback that WDN operators usually rely on have merit in the pH framework. All of these controllers are shown to be useful in mitigating disturbances and tracking setpoints corresponding to assignable equilibria.

Multi-objective and risk-based optimal sensor placement for leak detection in a water distribution system

The optimal sensor placement for leak detection in a water supply network has been studied extensively. The previous studies usually assume that each node of the water distribution network has the same importance. However, this assumption does not consider the difference in the impact when a leak occurs at the different nodes on the water distribution network. When the number of sensors is limited, the detection of only leakage events that can cause serious consequences is usually prioritized. This study proposes a multi-objective optimization method for sensor placement while taking into consideration the differences in the risk of leakage for the different nodes. First, five types of risk-based leakage functions are obtained based on the impact on the water distribution network when a leakage occurs at each node. The pressure at the node drops, which causes the water demand and pipe pressure to decrease while the flow rate increases. The multi-objective optimization method is then used to obtain the Pareto front according to the various risk-based leakage functions. Finally, the multi-criteria decision analysis method is used to rank and cluster the Pareto fronts, the optimal solution for each cluster is obtained, and these solutions are compared and analyzed. This study contains a case study that is based on the example water distribution network model C-town. When the number of sensors is limited, a more reasonable optimal sensor placement scheme can be obtained by considering the different risk-based leakage functions of the nodes, with a smaller impact on the water distribution network when a leakage occurs. • A multi-objective optimization sensor placement for leak detection in water distribution system. • Five types of risk-based leakage functions are obtained based on the impact of each leakage. • The impacts of the leakage occurring at each node of the pipe network are compared.

Pulsed Demand Modeling for the Optimal Placement of Water Quality Sensors in Water Distribution Networks

AbstractThis paper deals with the issue of the consistency of water distribution network (WDN) modeling concerning the nature of demand for the optimal water quality sensor placement. In this regard, a biobjective optimization for the sensor placement is carried out by minimizing the contamination extension or the contaminated population in the trade‐off with the number of sensors installed, by considering two alternative demand modeling conditions, that is, smooth and pulsed demand, respectively. The real layout of the two Italian WDNs is considered for the application of the methodology. The results show that for a given placement of sensors, the contamination extension always increases when the pulsed demand is considered. Furthermore, optimal sensor positions are affected by the pulsed demand especially in the case of highly looped WDN layouts and when the extent of contamination is used as objective function.

Transformative Insights Into Water Distribution Network Modeling and Optimization Through Epanet At Ranibandh and Khatra Blocks in Bankura

Water, the most precious natural resource on earth, has become a valuable commodity compared to other consumer goods.lt acts as a universal solvent containing essential minerals.Over time, there has been a change from using water from natural sources such as springs and rivers to groundwater for daily needs. Pipelines networks are laid to lift treated water to overhead reservoirs and boosting stations. This study is about three combined pipeline networks including two boosting lines of the Ranibandh and Khatra blocks of Bankura District in West Bengal. Three water distribution networks operate 16 hours a day. To check the water distribution networks and whether they are optimized, we have drawn the network and determined the head, pressure, head gradient and velocity to avoid problems in these distribution networks.EPANET software is used to analyze such pressure, headloss, base demand and height at various intersections of the provided models.The goal is to keep these parameters within the desired range by optimizing pipe diameters.From there, the goal is to determine head loss, flow, velocity, etc. in various parts of such pipelines. Point of view is to observe what is happening and report the results using the EPANET software. Inputs are given based on the data obtained and the results are graphically interpreted accordingly.Results through simulations confirm that the network systems are nearly optimized by keeping these parameters within the desired ranges, if cost analysis is included and simulation results are compared with practical observations, this system can be optimized in a better way.

Pressure-Leak Duality for Leak Detection and Localization in Water Distribution Systems

Water utilities are challenged to reduce their water losses through detecting, localizing, and repairing leaks as quickly as possible in their aging distribution systems. In this work, we solve this challenging problem by detecting multiple leaks simultaneously in a water distribution network for the Battle of the Leak Detection and Isolation Methods. The performance of leak detection and localization depends on how well the system roughness and demand are calibrated. In addition, existing leaks affect the diagnosis performance unless they are identified and explicitly represented in the model. To circumvent this chicken-and-egg dilemma, we decompose the problem into multiple levels of decision-making (a hierarchical approach) where we iteratively improve the water distribution network model and so are able to solve the multileak diagnosis problem. First, a combination of time series and cluster analysis is used on smart meter data to build patterns for demand models. Second, point and interval estimates of pipe roughnesses are retrieved using least squares to calibrate the hydraulic model, utilizing the demand models from the first step. Finally, the calibrated primal model is transformed into a dual model that intrinsically combines sensor data and network hydraulics. This dual model automatically converts small pressure deviations caused by leaks into sharp and localized signals in the form of virtual leak flows. Analytical derivations of sensitivities with respect to these virtual leak flows are calculated and used to estimate the leakage impulse responses at candidate nodes. Subsequently, we use the dual network to (1) detect the start time of the leaks, and (2) compute the Pearson correlation of pressure residuals, which allows further localization of leaks. This novel dual modeling approach resulted in the highest true-positive rates for leak isolation among all participating teams in the competition.