bluuebunny/crossref_metadata_2025 huggingface dataset:
PRVs working in parallel configurations – Analysis of dynamics
Authors: Ulanicki Bogumil , Beaujean Philippe , Plancke Juliaan , Leruth Bastien
Published: March 2024

Société wallonne des eaux (SWDE) is the most important water production and distribution company in Wallonia, one of the regions in Belgium. Its distribution network stretches over 40,000 km. It covers nearly 200 municipalities and has more than one million connections. SWDE supplies drinking water to nearly 2.4 million people, constituting over 70 % of the Walloon population. Recently, SWDE has launched a major multimillion-Euro project to improve security of the water supply in Wallonia. The project involves a 500/800 mm extension of a 1000-mm wide, over 100 km long pipeline as a backbone of the distribution network, starting from the Eupen and Gileppe reservoirs. The project created a number of engineering challenges and one of them is considered in this paper. The work represents a design of a pressure control station which provides efficient operation over a wide range of flows from 40 m3/h to 1500 m3/h. Additionally, the pressure control scheme needs to work in a stable manner with the downstream flow control system. It is proposed to use two identical hydraulically controlled PRVs connected in parallel without any external PLC control loop. To make sure that such a configuration can be installed in practice it was necessary to carry out extensive simulations to understand the system dynamic behavior for different scenarios.
Hydraulic State Estimation: pilot implementation in a water distribution system
Authors: Díaz García Sarai , Ruiz Gómez Emilio , González Pérez Javier
Published: March 2024

Hydraulic State Estimation (HSE) is an effective tool for water supply systems monitoring. This technique provides the most likely hydraulic state of the network by making use of the available measurements through the system and the associated hydraulic model, enabling to track uncertainty through the process. HSE has been applied to water transport networks before, but extending its application to distribution systems currently constitutes a challenge. The deployment of smart meters offers a unique opportunity to gain distributed information within the distribution level, but it also poses questions regarding the temporal resolution that is available/achievable and how to combine different sources of information. This work presents a real HSE implementation in a Spanish water distribution pilot area (approximately 1,000 inhabitants). Implementation is possible thanks to the systematic measurement of water consumption at each dwelling (with 1 minute and 1 L resolution) and flow and pressure at the inlet to the pilot area. This application is conceived to provide a better understanding of the challenges and opportunities for HSE implementation in water distribution systems.
Modelling a comprehensive relation between water quality and cement degradation in the drinking water distribution network
Authors: Van Laarhoven Karel Antonie , Hocking Alex , Korevaar Martin
Published: March 2024

Over a quarter of the Dutch drinking water distribution network consists of cementitious pipe materials (e.g. asbestos cement, concrete and cement-coated cast iron). Leaching of cement components into the drinking water may lead to changes in water quality and to a reduction of a pipe’s structural integrity. Many utilities worldwide condition their drinking water to prevent leaching. Several indices can be derived from a given water composition to estimate how well the water composition will protect against leaching. While water utilities have so far achieved good results using for instance the Langlier Saturation Index (LSI) and the Calcium Carbonate Precipitation Potential (CCPP) as an indicators, scientific authors have already posed in the past that the protection against leaching depends on a complex interplay between many chemical components in both the drinking water and the cement, and that no single index is sufficient to guarantee protection. In the presented work, a comprehensive model of the chemical and physical interactions between a cement pipe and drinking water is constructed. The model takes any given drinking water composition for input and returns a description of the chemical and microstructural changes that develop in the pipe wall over time as leaching progresses. The model shows reasonable agreement with leaching experiments described in literature. Using such a model, one can evaluate the protective capabilities of a given water quality based on all its aspects rather than just the aspects captured by derived indices. The model can be a powerful research tool for studying the interactions between drinking water and pipe materials, but this approach should also already prove to be a valuable aid for process engineers to optimize their conditioning strategies in practice, and for asset managers to translate historical water quality data to an estimation of the structural integrity of their cementitious pipes.
Disinfection Residual Behaviour within Drinking Water Distribution Systems
Authors: Rogers Jade , Fish Katherine , Speight Vanessa , Fairley Hunter , Knowles Graham , Boxall Joby
Published: March 2024

Understanding the persistence of different disinfection residuals in drinking water distribution systems (DWDS) is critical to water quality safety and public health. Chloramines are expected to persist, due to their chemical composition, further into the extremities of DWDS providing reduced risks to water quality, however there is limited evidence of this from operational systems. Total chlorine data from regulatory sampling at WTW outlets and customer taps from multiple DWDS was collected and analysed.  The first data set compares long term performance from equivalent systems at the same time.  Results showed the decay of residuals was similar across all seasons in the DWDS analysed, showing little correlation with residual type. The second data set compares six systems that experienced a disinfection switch from chlorine to chloramine. Decay of total chlorine residuals was clearly reduced for three and marginally reduced for a fourth following the switch, suggesting these DWDS experienced increased persistence.  However, two sites showed little change. This analysis highlights the uncertainties around residual persistence under different disinfection types, indicating the assumption that chloramine persists for longer than chlorine in every distribution system is not a given.
An approach to improve drainage networks based on the study of flood risk
Authors: Bayas-Jiménez Leonardo , Martínez-Solano F. Javier , Iglesias-Rey Pedro L. , Boano Fulvio
Published: March 2024

In recent years, the number of pluvial floods in cities has increased. There are different reasons for this increase, but certainly the most important are the increase in the intensity of the rain and the growth of cities, which increases the impervious surface, decreases the concentration time, and consequently increases runoff. To determine the costs of flood damage, land use is very important in the estimation of the cost of flood damage because it defines the type, value, and vulnerability of the structures. On this wise, an approach of great interest to managers of drainage networks is the estimation of annual damages in the area under study, because by knowing these costs, corrective measures can be taken, and the investment needed to reduce flooding can be determined. This work aims to present a methodology to improve drainage networks with lack of capacity due to increased runoff with a focus on the study of the vulnerability to the risk of flooding. To improve the operation of the networks, the method considers the possibility of changing the existing pipes for others with greater capacity, the construction of storm tanks and the implementation of hydraulic controls. To find the best solutions, an optimization model was developed that uses a Pseudo Genetic Algorithm and the SWMM model to perform hydraulic simulations. To reduce calculation times, a search space reduction procedure is applied to identify the regions containing the best solutions. These actions are intended to improve the efficiency of the model. For the economic assessment of flood risks, the cost of flood damage is related to its probability of occurrence for different return periods. With these data, a curve with a log-linear relationship is constructed.  The cost of the flood risk is obtained by integrating the area under the curve obtained. The methodology is applied in a drainage network with flooding problems called Balloon, located in located in northern Italy. The results are useful to demonstrate the benefit of risk cost analysis to make decisions and underline the relevance of including optimization models to prevent future damage in cities.
Optimal design of sewer networks including topographic criteria and drop manholes
Authors: Saldarriaga Juan , Herrán Juana , Iglesias-Rey Pedro L.
Published: March 2024

Optimal sewer network design is a complex problem that has been widely studied in literature. A methodology published in the literature that has shown great potential is the proposed by Duque, Duque, Aguilar, Saldarriaga. This methodology uses Mixed Integer Programming to select the layout of the network and Dynamic Programming to select the optimal combination of diameters and depths of pipes. Although the methodology showed good performance, two improvements have been implemented in later works. The first one is to consider topographic criteria when selecting the layout. This demonstrated to reduce the sewer network design’s costs. The other improvement is the addition of drop manholes in the optimization process. This allowed the methodology to comply with maximum velocity constrains in hilly regions. The present work tested the three methodologies mentioned before in a sewer network located in Bogotá, Colombia to evaluate the contributions and disadvantages of each methodology considering the cost and the hydraulic features of the resulting designs, and the computational resources required with each methodology.
Dynamic edge betweenness centrality and optimal design of water distribution networks
Authors: Sitzenfrei Robert , Hajibabaei Mohsen , Hesarkazzazi Sina , Minaei Amin , Savić Dragan
Published: March 2024

The multi-objective design of water distribution networks (WDNs) as a nonlinear optimization problem is a challenging task. With two contradicting objectives (e.g., minimizing costs and maximizing resilience), Pareto fronts of optimal solutions can be obtained with, e.g., evolutionary algorithms. However, the main drawback of these algorithms is the high computational effort required to optimize large WDNs. Recently, a highly efficient method based on complex network theory (CNT) was developed, where within seconds, a wide range of Pareto near-optimal solutions can be obtained for the design of WDNs (i.e., determining optimal diameters). The developed method is based on a customized graph measure called demand edge betweenness centrality (EBCQ). This measure is based on the frequency of occurrence of an edge in the shortest path from a source node to a demand node. In addition, EBCQ sums up the demands routed through that edge, giving a valid flow estimation for an optimal design. In the graph of a WDN, the edges can have different weights. The weighting function used for  calculations can be ‘static’ or ‘dynamic’. A constant value is utilized for edge weights in the static weighting approach, while a dynamic weighting function implies that edge weights are modified when iterating through all demand nodes. In this context, using dynamic weighting functions for  (i.e., dynamic ) avoids concentrating  values in just a few edges (shortest-path trees) by considering redundancy in flow paths and better approximation of the hydraulic behavior. However, it is not clear how the parameters of dynamic weighting functions should be defined to achieve the best approximation of the Pareto-optimal front. This work performs a systematic investigation of dynamic weighting functions and gives guidance for optimal parameter selection. The comparative study between the CNT approach (with static and dynamic weights) and evolutionary optimizations on four WDN design problems confirms the capability of the proposed dynamic functions in providing optimal/near-optimal solutions.
A Bayesian Generative Adversarial Networks (GAN) to Generate Synthetic Time-Series Data, Application In Combined Sewer Flow Prediction
Authors: Bakhshipour Amin E. , Koochali Alireza , Dittmer Ulrich , Haghighi Ali , Ahmed Sheraz , Dengel Andreas
Published: March 2024

Despite various breakthroughs of machine learning and data analysis techniques for improving smart operation and management of urban water infrastructures, some key limitations obstruct this progress. Among these shortcomings, the absence of freely available data due to data privacy or high costs of data gathering and the nonexistence of adequate rare or extreme events in the available data plays a crucial role. Here, the Generative Adversarial Networks (GANs) can help overcome these challenges. In machine learning, generative models are a class of methods capable of learning data distribution to generate artificial data. In this study, we developed a GAN model to generate synthetic time series to balance our limited recorded time series data and improve the accuracy of a data-driven model for combined sewer flow prediction. We considered the sewer system of a small town in Germany as the test case. Precipitation and inflow to the storage tanks are used for the Data-Driven model development. The aim is to predict the flow using precipitation data and examine the impact of data augmentation using synthetic data in model performance. Results show that GAN can successfully generate synthetic time series from real data distribution, which helps more accurate peak flow prediction. However, the model without data augmentation works better for dry weather prediction. Therefore, an ensemble model is suggested to combine the advantages of both models.
Infrastructure benchmarking for semi-real urban stormwater networks
Authors: Sitzenfrei Robert , Hesarkazzazi Sina , Hajibabaei Mohsen , Dastgir Aun
Published: March 2024

Attaining accurate information on the underground urban drainage/storm networks is a very difficult task, specifically in (developing) countries undergoing urban expansion. The problem of limited data availability gets even worse when considering that most case studies cannot made public due to security reasons. This, therefore, shed light on the lack of possessing enough case studies with different topological characteristics/attributes for the urban drainage research community to regularly explore and exploit their strategies/methodologies in a unified and integrated manner, allowing scholars to analyse and even compare their implications. To cover this research gap, we developed a ready-to-use open-access database of a multitude of (semi) real urban stormwater case studies with different characteristics. Specifically, optimized centralized and decentralized stormwater networks are considered for creating the benchmarks while redundant flow pathways are introduced into these structures (by leveraging complex network analysis) to create more diverse characteristics. This allows that if for a real case study area, no further information is available (e.g., developing countries), with the proposed approach, a feasible model for principal evaluations can be created with very low efforts. We expect more scholars join this momentum in the future by adding more case studies with various attributes to form a comprehensive and rich database not only in terms of hydrodynamic models and case studies but also digital sensors and observation data (e.g., flow meter, rain gauge data, etc.).
Methodology for analysis of the SDGs compliance in urban water systems. Implementation in case studies in the Valencian Community
Authors: Garcia Rodriguez Camila Andrea , Conejos Fuertes Pilar , Castillo Soria Jaime , Lopéz-Jiménez Petra Amparo , Perez-Sánchez Modesto
Published: March 2024

The current economic, social, and technological development demands high quantities of good quality water, putting in more stress water resources. However, if water resources could be managed in a sustainable way, it will be possible to satisfy the present needs without compromising its long-term capacity. Given the situation, it has become a challenge to manage water systems sustainably, achieving all the goals set by the SDGs. In this sense, the sustainability of hydraulic systems must be considered globally to integrate all the aspects that intervenes in the concept, such as the economic, social, technical, and environmental fields. Key performance indicators (KPIs) can help managers to audit and evaluate hydraulic systems over time and from a sustainable point of view. This contribution presents a methodology for the implementation of sustainability indicators that will allow to assess the compliance of all the SDGs goals that intervenes in urban water systems. The defined indicators are linked to the management of hydraulic systems, and it was developed a classification of use based on their usefulness, complexity and cost when measuring them in the systems. This study also proposes a classification by levels of sustainability of the water systems, such as the energy efficiency classification of electrical appliances. The methodology is applicable for all types of hydraulic systems (supply, sanitation, treatment) and for each of its processes (operation, maintenance, administration, infrastructure). For the implementation of the KPIs, the level of development of the country or region where the study is carried out is considered. Therefore, for each case different indicators are defined from those already established to consider the characteristics of the systems and for the calculation of each KPI. A description of the system must be carried out, to have a memory of the evolution of the complete system and to define the indicators that are relevant. Therefore, through the results of the analysis it is possible to establish a benchmarking on the sustainable aspects of the water distribution system would represent a design, diagnosis, and management tool, which will improve sustainability in hydraulic systems. By improving the performance and sustainability of the systems, it is not only possible to achieve the objectives of goal 6, but also other associated goals will benefit, which highlights the relevance of sustainability indicators in hydraulic systems.
Robustness of profile sampling in detecting dissolved lead in houshold drinking water
Authors: Dash Amitosh , Van Steen Jip , Blokker Mirjam
Published: March 2024

The norm for dissolved lead in drinking water is lowered in the Netherlands per 2023, from 10 to 5 µg/L. The effect is that: (a) homeowners and the Dutch water utilities want to find problematic lead components and remove them; and (b) the utilities want to know if copper service mains with lead soldering are expected to lead a norm exceedance. This calls for an improved sampling protocol (collected at the tap) to trace dissolved lead. We help solve this problem using hydraulic and water quality simulations in EPANET. With short time steps, low flow rates, and short pipe lengths, a water quality time step of 1 second is not short enough. As EPANET cannot work with timesteps smaller than 1 second, we devised a work-around to ensure that the numerical solution does not result in a large error. A household is defined as a system of pipes with various locations where water is used. The water demand patterns at each tap are generated using SIMDEUM. A few (sections of the) pipes are assigned to contain lead wherefrom dissolution occurs. The benchmark scenario involves a two-person household with average Dutch water consumption and with solely the service line containing lead. Next, fourteen scenarios are considered wherein either the geometry of the system, the water consumption, or location/extent of the lead releasing surface(s) are varied individually. Of the sampling protocols considered, the results show that “profile sampling” is the most promising. In such a protocol, twenty consecutive samples of 300 ml are collected following a minimum of six hours of prolonged stagnation. Irrespective of the scenario (with a lead component), at least one of the twenty samples is guaranteed to possess a high concentration of lead, owing to our choice of a fine sample volume. Just two days of profile sampling can potentially unveil: (1) the location of the lead releasing component, (2) the volume of the lead releasing region, and (3) the saturation concentration of lead dissolution. The key conclusions (for the current assumptions) are as follows. A lead service line potentially leads to an exceedance of the new norm, necessitating immediate action. The results also provide evidence that one week of proportional sampling is insufficient. Presently, it is considered to be the gold standard for measuring weekly intake of dissolved lead, however, it is susceptible to the stochastic nature of water demand leading to variations exceeding 50%. Lastly, a profile sampling protocol possesses the best opportunity to detect dissolved lead in the household drinking water network in a robust manner. In the future, a bespoke experimental facility (“PilotCity indoor installation”) will be used to validate the assumptions surrounding the lead dissolution and advection/diffusion model.
Iteratively tuning the regularisation parameter in an inverse method for localising leaks in water distribution networks
Authors: Liu Yuanyang , Stoianov Ivan , Pecci Filippo
Published: March 2024

Novel methods for detecting and localising leaks in water distribution networks are being increasingly investigated as water utilities face unprecedented financial and environmental challenges in reducing water losses. A promising method includes the solution of the regularised inverse problem that minimises the difference between simulated and measured data in addition to the regularisation term. However, the results of leak localisation are sensitive to the choice of the regularisation parameter. In this paper, we propose and investigate a method for iterative tuning of the regularisation parameter to improve the leak localisation performance in case of multiple time steps measurements. The numerical investigation utilises a benchmarking network model, and different metrics are applied to evaluate the results of the leak localisation performance, such as the performance and the distance metric.
Important Factors For Water Main Break Prediction Across 13 Canadian Systems
Authors: Dziedzic Rebecca , Gharaati Sadaf
Published: March 2024

Water main breaks can jeopardize the safe delivery of clean water and incur significant costs. To mitigate these risks, water main breaks have been predicted through physical and statistical approaches. The latter are less complex and can provide satisfactory results with less data. While many factors can contribute to breaks, the factors applied in previous studies depended on local data availability. Because other studies have focused on a few systems at a time, a broad comparison of factor importance has not been possible. This limits the understanding of the impact of different factors on water main deterioration. The present study identifies the most important factors driving water main breaks across 13 Canadian water systems. Twenty-eight factors describing physical, historical, protection, environmental and operational attributes were compiled and cleaned. Availability of each attribute differed by system. To evaluate the importance of both numerical and categorical attributes together, two approaches were tested, categorical principal component analysis (CATPCA) and recursive feature elimination with cross-validation (RFECV). The target variable in both cases was set as yearly break status, either broken or non-broken. While CATPCA provides the contribution of each attribute to the target, RFECV provides a tuned predictive model with selected attributes. The RFECV approach was applied with Random Forest and XGBoost models, both types of machine learning models which have been shown to produce accurate results in water main break prediction. Results from both approaches showed that physical and historical attributes are generally important across all systems. Other types of data, i.e. protection and operational are less available. When protection data is available it was shown to be even more important than physical and historical attributes. Specifically, with CATPCA, lining age and lining material were found to have a higher contribution to break status than pipe age and lining status. With RFECV lining age and lining material were also included in the best models, in particular for systems with greater percentage of lined pipes. These results indicate the choice and timing of lining are key in extending the service life of water mains. Furthermore, this data should be collected if protection practices are in place, to more accurately predict deterioration and future costs. The results also point to an opportunity to collect more operational data. Among attributes collected by only one utility, pipe pressure, roughness, and dead-end, were found to be important in CATPCA and RFECV. Thus, pipe dissipation and water stagnation could lead to greater pipe deterioration. Further studies are required to quantify the impacts of different pressure ranges and network designs on deterioration.
Multi-objective insights and analysis on data driven classifiers for anomaly detection in water distribution systems
Authors: Reynoso Meza Gilberto , Carreño-Alvarado Elizabeth Pauline , Hernández-Alba Mayra
Published: March 2024

Machine learning techniques have shown to be a powerful tool for extracting and/or inferring complex patterns from data. In the case of the so-called supervised learning, a given learner representation could learn such patterns using labeled data. For example, a helpful approach is to adjust a learner to detect anomalies: historical data can be used, where those events are identified, to find a pattern to classify new data as an anomaly (true event) or not (false event). In this example, the learner's objective is to act as a binary classifier, where a balance between false negatives (predict a typical operation, when in fact an anomaly exists) and false positives (predict an anomaly, when there is not). This balance is attained via an optimization (learning phase), where the learner representation is adjusted. Multi-objective optimization techniques have a natural way of dealing with such problems. They perform a simultaneous optimization of conflicting objectives. As a result, a set of Pareto-optimal solutions, the Pareto front, is calculated. This idea could be used in the training process of binary classifiers. Nevertheless, this requires an integral methodology, merging multi-objective optimization and multi-criteria decision making. While it is true that this idea is not new, methodologies and guidelines are still missing to conduct this process. In this work, we move toward the definition of an integrated methodology of multi-objective learning for binary classifiers for anomaly detection. An anomaly detection database for water distribution systems is used for such a purpose. Preliminary results show to be competitive regarding the F1-score to similar approaches.
Domain Analysis of Water Distribution Networks
Authors: Berardi Luigi , Ciliberti Francesco Gino , Laucelli Daniele Biagio , Giustolisi Orazio
Published: March 2024

Water Distribution Networks (WDNs) represent spatially organized infrastructures, whose main function is to deliver water from hydraulic sources to meet the customers’ demands and pressure requirements of the system through interconnected pipes. The time-varying water demands and the asset deterioration greatly contribute to the complexity in WDNs. Therefore, little changes of the structural layout expose the system to higher variations of WDN functioning, sometimes unpredictable to evaluate due to uncertainties of the hydraulic conditions, affecting WDNs control and management. The Complex Network Theory (CNT) has been recognized to provide a wide range of metrics for identifying the WDNs emergent behavior, only based on the analysis of the topological domain, even before using hydraulic models. This contribution deals with the application of several tailored centrality metrics, such as betweenness and edge betweenness, accounting for various combinations of the intrinsic relevance of the spatial elements of the systems, for identifying the emergent domain behavior of WDNs. The analysis has been applied to each subnetwork of a large real WDN, aimed at providing helpful indications for hydraulic model validation, identification of main pipes for improving the calibration of the system, maintenance works as well as relevance of main flow paths to plan operations.
Development of prediction model of ozone dosage and residual ozone concentration using machine learning methods in ozone process of drinking water treatment process
Authors: Hyung Jinseok , Kwon Jaeyoung , Kim Taehyeon , Park Haekuem , Koo Jayong
Published: March 2024

The ozone process, which is the latter process of the water purification process, injects ozone to remove taste odor substances from tap water. Still, it is difficult to work the ozone process due to recent changes in water quality, such as taste and odor substances due to climate change. Therefore, this study developed an ozone injection rate determination model and a residual ozone concentration prediction model to properly remove flavor odor substances from raw water and proposed an operational diagnosis and optimal decision-making method for the ozone process in water purification. An ozone injection rate determination model and a residual ozone concentration prediction model were developed using data on water quality, flow rate, and operating conditions measured at Seoul's Y water purification plant. Two models were developed: the random forest and the MLP models. The performance difference between the two was verified by comparing the correlation coefficient and error index. Bayesian optimization, a global search method within a given composition space, was used to determine hyperparameters for each model. RMSE was selected as an objective function to determine the optimal hyperparameter through cross-validation. If the above model is applied to the ozone process, it is expected that an immediate response to changes in raw water quality and human error prevention will be possible.
Energy equations to analyze pressurized water transport systems
Authors: Del Teso March Roberto , Gómez Sellés Elena , Estruch-Juan Elvira , Cabrera Marcet Enrique
Published: March 2024

Pressurized water systems are high energy demanders. They must deliver the demanded volume of water at the minimum service pressure. There is currently a marked change in production and energy tariffs, which is causing prices for water services to rise. In addition to the economic aspect, the energy demand of water systems has an environmental implication linked to gas emissions. With increasing demands, the climate change situation and the shift in energy tariffs, it is necessary to improve the efficiency of pressurized water transport systems. The lower the kWh required to supply, the more energy efficient they will be. The energy analysis of the systems will allow us to know the current situation of the systems, and to know if it is necessary to undertake improvement measures. There are different processes to know the energy status of the networks. The simplest one is to carry out a diagnosis, with little data, which will give an initial idea of the energy status of the networks. This diagnosis can be carry through by applying Bernoulli's equation. Applied from the supply points to the most unfavorable point of the system. If it is desired to know more precisely the energetic status of the networks, the use of the integral energy equation seems more reasonable. This equation makes it possible to audit the system and to know in detail the energy use for a given control volume. It allows the energy supplied to be broken down into: useful energy, structural energy losses (linked to topography) and operational energy losses (friction losses, pumping losses, leakage losses and excess energy). In order to be able to apply this method, it is mandatory to have the mathematical model of the system. This paper discusses the advantages and disadvantages of carrying out the energy analysis of a system using the Bernoulli equation (diagnosis) or the energy integral equation (audit), and when it is convenient to apply one or the other. On one hand, the Bernoulli equation makes it possible to estimate the energy level of the network with very little data, without knowing exactly in which processes the energy introduced into the system is invested. The audit based on the integral energy equation, on the other hand, requires precise data collection and mathematical modelling, but it will provide a detailed understanding of the energy breakdown of the network. Depending on the objective of the energy analysis, it seems reasonable to apply one process or another. For a first estimation and as a start of the energy analysis, it will be sufficient to carry out a diagnosis as quickly as possible, which will allow to know if it is necessary to continue with a more in-depth research of the system status such as an energy audit.
IoT.H2O - Supervision and Control for Water Systems based on Low-Cost Hardware and Open-Source Software
Authors: Roclawski Harald , Sterle Laura , Böhle Martin , Krätzig Thomas
Published: March 2024

An adequate monitoring and control of water production and distribution systems is essential for operating the system efficiently, avoiding water losses and preventing damage to the system. Usually this is achieved by SCADA systems. But SCADA systems are technically complex and expensive. They are often not affordable for small water utilities. Their systems are operated with a very low level of automatization and monitoring causing inefficient operation, water loss or even interruption of service. The Internet of Things concept is very promising to realize a cost-effective tool for monitoring and control of water systems. The potential of this approach is investigated in the research project IoT.H2O funded in the WaterJPI call IC4WATER. In the paper, the concept of the IoT system for monitoring and control of water supply systems is explained. Special gateways and IoT devices have been developed which communicate with the IoT platform by the LoRaWAN network protocol or WiFi. It is shown how a pump test rig can be controlled with these devices. Also, results of field installations in a water distribution system in Germany are presented. Moreover, it is shown how data from existing SCADA systems and results from network simulations with EPANET can be included in the IoT system and how the data can be processed and evaluated by the IoT platform. By combining the data of different systems, the developed IoT system provides the user a cost-effective tool for monitoring and control of a water system.
Grey-box dynamic model of a drinking water treatment plant
Authors: Korotaj Blaž , Novak Hrvoje , Vašak Mario
Published: March 2024

In the paper a concept of a general reaction volume in a drinking water treatment plant (DWTP) is introduced, where several such volumes need to be stacked in series with corresponding input chemicals used in them to finally represent the entire plant. A general case in which a reaction volume is not fixed is elaborated. The overall derived plant model is of a grey-box form with a small number of tunable parameters, which is beneficial for adaptation to a particular DWTP based on its laboratory and on-line measured data. The developed mathematical models of reaction volumes are stacked and inputs in different volumes selected such that they represent a concrete DWTP for which a decision-support system is being developed. A preliminary validation is performed against laboratory-determined plant input-output data.
A Thorough Cybersecurity Dataset for Intrusion Detection in Smart Water Networks
Authors: Taormina Riccardo , Murillo Andrés F. , Tippenhauer Nils Ole , Galelli Stefano
Published: March 2024

The increase in the number and complexity of cyber-physical attacks on water distribution systems requires better intrusion detection systems. So far, the design and validation of such systems has relied on datasets, such as the EWRI 2017 BATtle of the Attack Detection ALgorithms (BATADAL), that provide a detailed representation of hydraulic processes in response to cyber-physical attacks. However, the BATADAL, generated with epanetCPA, does not include an equivalent and detailed representation of the processes occurring within the industrial communication system. Here, we fill in this gap by presenting the BATADAL 2.0 dataset, generated with the DHALSIM simulator, a novel co-simulation environment that can represent the hydraulic processes, digital control, and network communication of smart water networks. The dataset includes a broad variety of attacks and network anomalies. Most importantly, the availability of both process and network data is expected to pave the way to more advanced and accurate detection algorithms.
PathoInvest: Pathogen Contamination Investigations during Emergencies
Authors: Eliades Demetrios G. , Paraskevopoulos Sotirios , Vrachimis Stelios , Kyriakou Marios , Pavlou Pavlos , Kouzapas Dimitris , Milis George , Smeets Patrick , Medema Gertjan , Polycarpou Marios , Panayiotou Christos
Published: March 2024

Emergencies and disasters (such as earthquakes and floods), may contaminate drinking water systems with pathogens, that can affect the health of both First Responders and Citizens. As part of the Horizon 2020 “Pathogen Contamination Emergency Response Technologies” (PathoCERT) project, we are developing a Digital Twin tool (PathoINVEST) to assist First Responders and Water Authorities in investigating and responding efficiently to drinking water contamination events. In this paper, we present preliminary work on PathoINVEST, its architecture, and how it operates with the PathoCERT ecosystem of technologies. Moreover, using an illustrative case study, we demonstrate how PathoINVEST will process data and produce useful insights for the First Responders during a realistic contamination event. This work demonstrates how different research results can be integrated into a holistic water contamination emergency management system, in accordance with the needs of First Responders who need to make decisions within a limited time frame and to reduce the impact of a contamination event.
Optimization of reservoir treatment levels considering uncertainty in mixing at cross junctions in water distribution systems using Info-gap decision theory
Authors: Ostfeld Avi , Boindala Sriman Pankaj , Jaykrishnan G
Published: March 2024

Water distribution systems are affected by several uncertainties in multiple stages. This uncertainty makes solving the optimal design and management of WDS a multifaceted problem. Past research has focused only on solving design and management problems of system hydraulics. There have been very few studies that involve considering uncertainties that affect the water quality aspect of WDS. One of the major assumptions in solving the design and management problems of WDS is considering uniform and instantaneous mixing at the cross junctions. However, in reality, this is not true. This assumption is made due to the lack of computational power to accurately estimate the level of mixing at every junction in a water distribution network. This study focuses on considering this level of mixing as uncertain/unknown and provides the optimal treatment levels required at the reservoirs to ensure the system is immune to the level of mixing occurring at the junctions to satisfy the water quality requirements at the customer level. Info-gap decision theory-based optimization approach combined with the cuckoo search metaheuristic is proposed in this study to handle the uncertainty. The proposed methodology is applied to a 4x4 grid hypothetical network example. The study's objective is to provide the best designs that can handle the maximum variation of the level of mixing at junctions within the given budget by the designer. The maximum variation of the level of mixing is reported for different budget levels. The designs are compared with the deterministic case using Monte-Carlo simulations.
Numerical and experimental investigation on leak discharge-pressure relationship for linear elastic deforming pipes
Authors: Gao Jinliang , Li Yuanzhe , Chen Jianxun , Wu Wenyan , Qi Shihua , Yu Jingyang
Published: March 2024

Large amount of research investigated the flow rate from an individual leak as a function of the internal pressure, but few of them managed to separate the effect of pipe deformation and that of leak hydraulics. In this study, experiments were carried out simultaneously measuring leak area, flow rate and pressure head so that the discharge coefficient can be experimentally determined. The experiments under both free and submerged condition were conducted. A few anomalies were found. For example, the leakage flow rate of submerged discharge was significantly greater than that of free discharge under full turbulence regime. Meanwhile, for submerged discharge, the discharge coefficient was not constant, but increased first and then decreased. Through CFD simulation, this study provided preliminary expansions of the two anomalies. The less leak flow rate of free discharge may be caused by the friction of water flow and air, and the abnormal decrease of the discharge coefficient of submerged discharge may due to cavitation phenomenon. The phenomena observed in this study could offer more information for researchers about leak hydraulics.
Study on iron dispersion law and control measures of dead-end branch pipe
Authors: Gao Jinliang , Chen Jianxun , Wu Wenyan , Wang Guanghui , Ding Qiang , Qi Shihua , Li Bo
Published: March 2024

The paper aims to investigate the dispersion law and mechanism of total iron from dead-end metal branch pipe to main pipe under the impact of hydraulic disturbance. A full scale experimental water distribution network including a transport main pipe as well as six dead-end branch was set up and computational-fluid-dynamics (CFD) simulation are adopted to study the dispersion phenomenon. The cavity flow theory is adopted to clarify the inner dispersion mechanism. Firstly, the CFD model is examined through the comparison of results between the two experimental methods under the same condition. Secondly, performing the effect of the Reynolds number in main pipe on the total dispersion concentration of iron. Thirdly, the characteristics of interaction between flow field and the inner mechanism linked to streamline map and concentration distribution map are analysed based on the concept of cavity flow. The future research content is briefly proposed.
Non-intrusive water usage classification considering limited training data
Authors: Pavlou Pavlos , Vrachimis Stelios G. , Eliades Demetrios G. , Polycarpou Marios M.
Published: March 2024

Smart metering of domestic water consumption to continuously monitor the usage of different appliances has been shown to have an impact on people’s behavior towards water conservation. However, the installation of multiple sensors to monitor each appliance currently has a high initial cost and as a result, monitoring consumption from different appliances using sensors is not cost-effective. To address this challenge, studies have focused on analyzing measurements of the total domestic consumption using Machine Learning (ML) methods, to disaggregate water usage into each appliance. Identifying which appliances are in use through ML is challenging since their operation may be overlapping, while specific appliances may operate with intermittent flow, making individual consumption events hard to distinguish. Moreover, ML approaches require large amounts of labeled input data to train their models, which are typically not available for a single household, while usage characteristics may vary in different regions. In this work, we initially propose a data model that generates synthetic time series based on regional water usage characteristics and resolution to overcome the need for a large training dataset with real labeled data. The method requires a small number of real labeled data from the studied region. Following this, we propose a new algorithm for classifying single and overlapping household water usage events, using the total domestic consumption measurements. The classification procedure is described below: 1) During the offline feature learning stage, a dataset of labeled data corresponding to water-use profile signals is analyzed to some predefined features, such as event volume, event duration, event flow peak, and event signature, to extract its statistical properties, 2) The event classification stage monitors the provided measurement time-series for events between zero-flow intervals. The identified events are then classified using Dynamic Time Wrapping and an optimization procedure that finds the best label for the observed event based on the features learned in the first stage and similarity indices. Non-classified events are processed using a variation vector technique to identify the combined events which are then split into sub-single events and classified.Extended version of this article is selected for possible publication in a Special Issue in the Journal of Hydroinformatics.
Supervised machine learning models for leak detection in water distribution systems
Authors: Basnet Lochan , Ranjithan Ranji , Brill Downey , Mahinthakumar Kumar
Published: March 2024

Water distribution systems (WDSs) face a significant challenge in the form of pipe leaks. Pipe leaks can cause loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. Locating pipe leaks has been a constant challenge for water utilities and stakeholders due to the underground location of the pipes. Physical methods to detect leaks are expensive, intrusive, and heavily localized. Computational approaches provide an economical alternative to physical methods. Data-driven machine learning-based computational approaches have garnered growing interest in recent years to address the challenge of detecting pipe leaks in WDSs. While several studies have applied machine learning models for leak detection on single pipes and small test networks, their applicability to the real-world WDSs is unclear. Most of these studies simplify the leak characteristics and ignore modeling and measuring device uncertainties, which makes the scalability of their approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases that account for the realistic leak characteristics (multiple, multi-size, and randomly located leaks) and incorporating noise in the input data to account for the model- and measuring device- related uncertainties. A machine learning-based approach that uses simulated pressure as input to predict both location and size of leaks is proposed. Two different machine learning models: Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN), are trained and tested for the four study cases, and their performances are compared. The precision and recall results for the L-Town network indicate good accuracies for both the models for all study cases, with CNN generally outperforming MLP.