Water Distribution Networks Research Articles

Enhancing efficiency and quality control: The impact of Digital Twins in drinking water networks.

This paper showcases the successful development and implementation of two Digital Twin prototypes within the Lab Digital Twins project, designed to enhance the efficiency and quality control of Aigües de Barcelona's drinking water network. The first prototype focuses on asset management, using (near) real-time data and statistical models, and achieving a 70% success rate in predicting pump station failures 137 days in advance. The second prototype addresses water quality monitoring, leveraging machine learning to accurately forecast trihalomethane levels at key points in the distribution system, and enabling proactive water quality management strategies, ensuring compliance with stringent safety standards and safeguarding public health. The paper details the methodology of both prototypes, highlighting their potential to revolutionize water network management. PRACTITIONER POINTS: Digital representation of assets and processes in the drinking water treatment network Early fault detection in assets, and predictions of trihalomethane formation in the drinking water distribution network Reduction on monitoring time and incident response for target assets by means of Digital Twins Improvement in visualization, prediction, and proactive measures for asset management and water quality control Contribution to the growing knowledge on Digital Twins and their potential to revolutionize water network operations.

Validation of an Enhanced Drinking Water Temperature Model during Distribution

Drinking water temperatures are expected to increase in the Netherlands due to climate change and the installation of district heating networks as part of the energy transition. To determine effective measures to prevent undesirable temperature increases in drinking water, a model was developed. This model describes the temperature in the drinking water distribution network as a result of the transfer of heat from the climate and above and underground heat sources through the soil. The model consists of two coupled applications. The extended soil temperature model (STM+) describes the soil temperatures using a two-dimensional finite element method that includes a drinking water pipe and two hot water pipes coupled with a micrometeorology model. The extended water temperature model (WTM+) describes the drinking water temperature as a function of the surrounding soil temperature (the boundary temperature resulting from the STM+), the thermal sphere of influence where the drinking water temperature influences the soil temperature, and the hydraulics in the drinking water network. Both models are validated with field measurements. This study describes the WTM+. Previous models did not consider the cooling effect of the drinking water on the surrounding soil, which led to an overestimation of the boundary temperature and how quickly the drinking water temperature reaches this boundary temperature. The field measurements show the improved accuracy of the WTM+ when considering one to two times the radius of the drinking water pipe as the thermal sphere of influence around the pipe.

Optimizing District Metered Areas and Associated Pressure Reducing Valves and Pumping Stations for Effective and Efficient Management of Water Distribution Network

Optimizing District Metered Areas and Associated Pressure Reducing Valves and Pumping Stations for Effective and Efficient Management of Water Distribution Network

Analysis of water losses in seven small and medium-sized water distribution networks in the south region of Cameroon (Central Africa)

ABSTRACT This paper analyzes and evaluates water losses in seven small and medium-sized water supply networks (WSNs) in the south region of Cameroon in order to contribute to improving the most effective approaches to reducing water losses in Sub-Saharan Africa's WSNs. Data were obtained from the regional delegation of the Camwater in the south region of Cameroon and analyzed using a variety of indices, including the failure intensity index, the unit coefficient of water loss per kilometer, the real leakage balance index, the non-revenue water basic, and the infrastructure leakage index. Results indicate that all the WSNs analyzed achieved mixed results compared with national data and very weak results compared with international standards. This is partly due to outdated infrastructure, rapid urbanization and urban population expansion, poor operation and maintenance policies, inadequate technical skills and technology, a lack of financial discipline, and political, cultural, and social influences. The results place the cumulative volume of water losses in the seven networks studied during the 5 years at 7.3 million cubic meters, which costs the water utility about 2.4 billion FCFA (or 4.4 million USD).

A Transformer-Based Approach to Leakage Detection in Water Distribution Networks.

The efficient detection of leakages in water distribution networks (WDNs) is crucial to ensuring municipal water supply safety and improving urban operations. Traditionally, machine learning methods such as Convolutional Neural Networks (CNNs) and Autoencoders (AEs) have been used for leakage detection. However, these methods heavily rely on local pressure information and often fail to capture long-term dependencies in pressure series. In this paper, we propose a transformer-based model for detecting leakages in WDNs. The transformer incorporates an attention mechanism to learn data distributions and account for correlations between historical pressure data and data from the same time on different days, thereby emphasizing long-term dependencies in pressure series. Additionally, we apply pressure data normalization across each leakage scenario and concatenate position embeddings with pressure data in the transformer model to avoid feature misleading. The performance of the proposed method is evaluated by using detection accuracy and F1-score. The experimental studies conducted on simulated pressure datasets from three different WDNs demonstrate that the transformer-based model significantly outperforms traditional CNN methods.

Distributed Nonconvex Optimization for Control of Water Networks with Time-coupling Constraints

AbstractIn this paper, we present a new control model for optimizing pressure and water quality operations in water distribution networks. Our formulation imposes a set of time-coupling constraints to manage temporal pressure variations, which are exacerbated by the transition between pressure and water quality controls. The resulting optimization problem is a nonconvex, nonlinear program with nonseparable structure across time steps. This problem proves challenging for state-of-the-art nonlinear solvers, often precluding their direct use for near real-time control in large-scale networks. To overcome this computational burden, we investigate a distributed optimization approach based on the alternating direction method of multipliers (ADMM). In particular, we implement and evaluate two algorithms: a standard ADMM scheme and a two-level variant that provides theoretical convergence guarantees for our nonconvex problem. We use a benchmarking water network and a large-scale operational network in the UK for our numerical experiments. The results demonstrate good convergence behavior across all problem instances for the two-level algorithm, whereas the standard ADMM approach struggles to converge in some instances. With an appropriately tuned penalty parameter, however, both distributed algorithms yield good quality solutions and computational times compatible with near real-time (e.g. hourly) control requirements for large-scale water networks.

Study on Large-Scale Urban Water Distribution Network Computation Method Based on a GPU Framework

Large-scale urban water distribution network simulation plays a critical role in the construction, monitoring, and maintenance of urban water distribution systems. However, during the simulation process, matrix inversion calculations generate a large amount of computational data and consume significant amounts of time, posing challenges for practical applications. To address this issue, this paper proposes a parallel gradient calculation algorithm based on GPU hardware and the CUDA Toolkit library and compares it with the EPANET model and a model based on CPU hardware and the Armadillo library. The results show that the GPU-based model not only achieves a precision level very close to the EPANET model, reaching 99% accuracy, but also significantly outperforms the CPU-based model. Furthermore, during the simulation, the GPU architecture is able to efficiently handle large-scale data and achieve faster convergence, significantly reducing the overall simulation time. Particularly in handling larger-scale water distribution networks, the GPU architecture can improve computational efficiency by up to 13 times. Further analysis reveals that different GPU models exhibit significant differences in computational efficiency, with memory capacity being a key factor affecting performance. GPU devices with larger memory capacity demonstrate higher computational efficiency when processing large-scale water distribution networks. This study demonstrates the advantages of GPU acceleration technology in the simulation of large-scale urban water distribution networks and provides important theoretical and technical support for practical applications in this field. By carefully selecting and configuring GPU devices, the computational efficiency of large-scale water distribution networks can be significantly improved, providing more efficient solutions for future urban water resource management and planning.

Near Real-time Leak Location by Inverse Analysis Integrating Measurement Uncertainty

AbstractThis paper presents a novel model-based method for near real-time pipe burst location in water distribution networks by integrating measurement uncertainty into inverse analysis. The method accounts for expected errors between measured and computed values, providing a pipe burst location area whose size varies according to the expected error level and the burst size. The proposed method is demonstrated and compared with the traditional inverse approach using a real case study with artificial bursts of different sizes and with different pressure signal noise levels. The performance of both methods is also assessed and discussed considering the effect of seasonal water demands. The traditional inverse analysis fails to accurately locate the pipe burst events, and depending on the expected error level and pipe burst size, the obtained locations may be significantly further away from the real burst location. Conversely, the proposed method does not point to the exact burst location but provides an approximated area in which step-testing can be carried out to pinpoint the exact burst location; the size of this area can be larger or smaller depending on the burst flow rate and signal uncertainty.

LSTM-Autoencoder Based Detection of Time-Series Noise Signals for Water Supply and Sewer Pipe Leakages

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies.

The Implementation of IoT Sensors in Fog Collector Towers and Flowmeters for the Control of Water Collection and Distribution

This study describes the implementation of Internet of Things (IoT) sensors in flow meters installed in drinking water systems and in fog catchers built in low-income, high-altitude communities in the Andes region of Ecuador, taking studies at the University de las Fuerzas Armadas ESPE as our reference. The influence and management of these intelligent sensors are analyzed, as well as a basic review of the materials and methods used in their implementation. The importance of validating the accuracy and reliability of IoT sensors compared to professional devices is highlighted, especially in mountain areas with difficult access. Additionally, the cost–benefit of using IoT sensors in fog catchers and drinking water distribution networks is mentioned, which depends on several factors such as the scale of the project, the objectives to be achieved concerning monitoring, and the available resources. Finally, it is highlighted that using Internet of Things (IoT) sensors in construction and water collection systems has proven beneficial in detecting possible effects on its operation and determining consumption and supply flows for a given population.

Using Artificial Neural Networks to Predict Leakage Location in Water Distribution Networks

One of the major types of water wastage in a distribution network is the leakage that occurs in pipes or other network components such as connections. Leaks in urban water supply networks impose many costs and workforce on governments and related organizations annually. If the cause of leakage is traced to the cracks and slits on various parts of a network, the amount of leakage from the location of the crack is said to be directly associated with the amount of pressure at that point. To trace leakage in water distribution networks, this study introduced a method based on hydraulics modeling and the inverse solution of flow equations to predict the location and amount of leakage in water distribution networks, by considering the values of the measured pressure in some network nodes. For this, a hydraulic model of the network under study was provided in the EPANET Hydraulic Analysis software, and the network analysis of various values and states of hypothetical leaks led to obtaining pressure values in the different nodes of the network via modeling water modeling under a steady state. Then, the artificial neural network, having been trained, was used to provide measured pressures in some network nodes at the testing hour as input data to the neural network to locate possible leaks in the water distribution network and to predict their approximate values. The findings were found to enjoy desirable accuracy. DOI: https://doi.org/10.52783/pst.835

Comprehensive Resilience Assessment Framework for Water Distribution Networks

A novel comprehensive resilience assessment framework for drinking water systems is proposed integrating different resilience perspectives (i.e., robustness, autonomy, flexibility, reliability, preparedness and recovery), oriented by objectives, criteria and metrics, applicable at the tactical level. The resilience assessment framework is applied to a Portuguese real water distribution network, enabling the evaluation of the system’s resilience. The infrastructure dimension is the main contributor to the low resilience results, particularly in terms of infrastructural robustness, as the infrastructure has exceeded the average service life and has low rehabilitation rates. In terms of autonomy, the system highly depends on external water and energy sources. Regarding the service dimension, most of the drinking water available is used for non-potable uses (e.g., irrigation), without alternative sources. The detailed diagnosis identified network area R6 as the priority area. Assets rehabilitation, increasing storage capacity, finding alternative water and energy sources, and minimizing non-potable uses are relevant improvement measures that promote the reinforcement of the system’s resilience. The resilience assessment framework is a very useful tool for the daily and tactical management of drinking water systems.

Investigating water quality dynamics in distribution networks with dynamically adaptive connectivity

ABSTRACT Water distribution networks with dynamically adaptive connectivity offer greater operational flexibility. While this strategy has demonstrated improvements in pressure management and network resiliency, further research is needed to better understand its impact on water quality dynamics. This paper investigates the short-term variability of disinfectant residuals in a real-world distribution network operated with dynamic connectivity. We simulate water quality dynamics under two control configurations with pressure control and automatic flushing valve operations. Our simulation results inform the development of flow variability metrics to reveal the relationship between changing hydraulic conditions and increased water quality dynamics. These metrics can (i) improve observability by supporting the placement of additional water quality monitoring locations and (ii) enhance controllability by enabling the formulation of optimization problems that incorporate hydraulic surrogates for modelling water quality. Furthermore, we validate the identified regions of increased water quality dynamics using continuous disinfectant data from a large-scale experimental programme. Our findings emphasize the need for further analytical and experimental research to manage water quality in distribution networks with dynamically adaptive connectivity and hydraulic control.

A multi-point leakage prediction statistical method using Bayesian inference in water distribution networks

ABSTRACT Leakage in water distribution networks (WDNs) not only leads to serious water loss and pipe contamination but also affects residents’ daily water. Accurate localization of leaks in WDN is significant to conserve water resources and reduce economic losses. However, in traditional optimization and verification methods for leak detection, factors such as modeling and pressure monitoring point errors are not taken into account, resulting in the deviation from the actual location of leaks. To address the mentioned issues, this article presents a WDN leakage probability prediction method based on Bayesian inference. The method converts the prior probability of leakage events into posterior probability. Then, by utilizing the posterior probability density function, the uncertainty of modeling and measurement errors in the hydraulic simulation model are quantified. The probability distribution of leakage pipes and leakage quantity within the leakage area is calculated, allowing for the location prediction and corresponding magnitude of leakage. The experimental results indicate that the prediction model can detect unknown quantities of leakage events. By collecting multiple sets of leakage data, it is possible to accurately predict the location and quantity of leaks, enhancing the efficiency of leakage detection in large-scale water supply networks and providing decision-making assistance for water utilities.

Prevention of bacterial adhesion in water conduction systems using vanillin-functionalized grates

Pathogenic microorganisms can adhere to the surfaces of water conducting systems, finally resulting in biofilm structures. Considering the importance of the adhesion capability of cells and the biofilm formation in these surfaces, their inhibition by using naturally occurring antimicrobial compounds can be a potential alternative or complement to water chlorination. This work aimed to study the antiadhesive capability of vanillin-functionalized stainless-steel surfaces against bacteria present in low-pressure water distribution network. Stainless-steel surfaces were pretreated (by sanding or polishing) and activated, allowing the covalent anchoring of vanillin to the stainless-steel. Characterization of functionalized surfaces, by macroscopic, microscopic, and elemental analysis confirmed the efficiency of the vanillin anchoring protocol. Subsequently, in vitro antiadhesive capability assays were performed with Staphylococcus epidermidis as a model microorganism to assess the antiadhesiveness capacity of the functionalized surfaces. This assay allowed the confirmation of the capacity of vanillin-functionalized surfaces to prevent the adhesion of S. epidermidis to the stainless-steel surfaces. Finally, grates were functionalized and placed in low-pressure water conduction pipes in the city of Valencia, Spain. After placement, samples of the biofilm adhered to the grates were taken at certain times and analyzed through metagenomic analysis. The results showed statistically significant differences in both the amount and type of bacterial strains adhered to vanillin-functionalized and non-functionalized grates. These findings demonstrated for the first time the potential application of vanillin-functionalized stainless-steel surfaces in water conduction systems to avoid the adhesive ability of bacteria, preventing their contamination and/or biofilm formation.

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.

Optimization of chlorine consumption in water distribution networks by using the new ant colony optimization (ACOR) algorithm

Optimization of chlorine consumption in water distribution networks by using the new ant colony optimization (ACOR) algorithm

Signal Processing and Pattern Recognition for Leak Detection in a Water Distribution Network

Signal Processing and Pattern Recognition for Leak Detection in a Water Distribution Network

Graph-Theory Metrics for the Prioritization of Water Distribution Network Assets of Tyndall AFB, Florida

Graph-Theory Metrics for the Prioritization of Water Distribution Network Assets of Tyndall AFB, Florida

Comparative Analysis of Leak Detection Methods Using Hydraulic Modelling and Sensitivity Analysis in Rural and Urban–Rural Areas

Water scarcity is a significant global challenge, exacerbated by leakages in water distribution networks. This paper addresses the challenge of detecting leakages in rural and urban–rural water supply systems through hydraulic modelling and a sensitivity analysis. Two distinct real-world network models were studied to assess real and simulated leakage scenarios varying in location and magnitude. A distinct leakage detection approach utilizing outflow measurements from hydrants was tested. Additionally, the effectiveness of various statistical measures—such as correlation, angular closeness, Euclidean distance, Manhattan distance, Chebyshev distance, cosine similarity, and Spearman correlation—were evaluated to determine their efficacy in leakage detection. Different methods for identifying leak candidates were explored and compared, either by selecting a single leak candidate based on similarity measures or by identifying a group of candidates to mark leak hotspots. Density-based spatial clustering of applications with noise was used to assess the number of potential leak candidate groups. The study’s findings contribute to the optimization of leak detection strategies in water supply networks, particularly in rural settings, where detection is challenging due to scarce measurement datasets, budget restrictions, and operational constraints.