Real Water Distribution System Research Articles

Assessment of the minimal distance for placing household connections downstream of water distribution cross junctions

Abstract This study investigates the solute (chlorine residual) mixing phenomena downstream of a cross junction, considering its critical role in household connections. Experiments were conducted under turbulent flow conditions in a cross junction with two inlets at 90°, varying flow and chlorine concentration ratios at the inlets, i.e., simulating conditions commonly found in real water distribution systems. Results show that outlet chlorine concentrations primarily depend on the flow ratio at the inlets as well as on the inlet chlorine concentrations. Three-dimensional simulations were conducted to predict chlorine concentrations downstream of the cross junction. To evaluate the degree of downstream mixing, the percent coefficient of variation of tracer concentration (%CV) as a function of the axial position at different chlorine and flow ratios was computed from simulations. It was found that the flow ratio strongly affects it at downstream distances less than 50 pipe diameters, whereas the inlet chlorine concentration ratio has a weak effect. A novel correlation was derived as a function of flow ratio to ascertain the minimal distance for achieving the intended mixing level of %CV = 5 downstream of cross junctions. This correlation holds potential as a criterion for household connection location within water distribution networks for high-quality water delivery.

Fault detection using PDE-based observer in transport flow

This paper deals with the state fault detection scheme for distribution flow networks subject to continuously varying conditions at boundaries. A robust PDE detection observer for transport flow systems is designed. Directly built on the nonlinear hyperbolic systems of balance laws model with anti-collocated setup, the PDE observer based on backstepping theory provide the on-line estimation of signals that are not measured. The stability of the error equation is proved. The estimation and the observability time are used for fault detection; an adaptive threshold is defined for the purpose. The performances of the observer and the fault detection method are validated on actual flow data collected from a real water distribution system (WDS) for leakage detection. The leak detection time corresponds to the first alarm activation, confirms the effectiveness of proposed approach.

Generation of Synthetic Water Distribution Data Using a Multiscale Generator-Optimizer

Rare or limited access to real-world data has widely been a stumbling block for the development and employment of design optimization and simulation models in water distribution systems (WDS). Primary reasons for such accessibility issues could include data unavailability and high security protocols. Synthetic data can play a major role as a reliable alternative to mimic and replicate real-world WDS for modeling purposes. This study puts forth a comprehensive approach to generate synthetic WDS infrastructural data by (1) employing graph-theory concepts to generate multitudinous WDS skeleton layouts through retaining the critical topological features of a given real WDS; and (2) assigning component sizes and operational features such as nodal demands, pump curves, pipe sizes, and tank elevations to the generated WDS skeleton layouts through a multiobjective genetic algorithm (GA)–based design optimization scheme. Thousands of such generated-optimized networks are statistically analyzed in terms of the fundamental WDS characteristics both collectively and granularly. An outstanding novelty in this study includes an automatedly integrated algorithmic function that attempts to (1) simultaneously optimize the generated network in a biobjective scheme, (2) rectify pipe intersections that violate pipeline embedding standards, and (3) correct the unusual triangular loops in the generator by honoring the conventional square-shaped loop connectivity in a WDS. The proposed modeling approach was demonstrated in this study using the popular Anytown water distribution benchmark system. Generation and optimization of such representative synthetic networks pave the way for extensive access to representative case-study models for academic and industrial purposes while the security of the real-world infrastructure data is not compromised.

Leak detection in water distribution systems by classifying vibration signals

In addition to the issues of water quality and economic losses, leakages in pipe networks are a potential danger to public safety. Early detection of water leaks in pipe networks is crucial for implementing countermeasures to reduce structural damage and water losses. The method of data collection based on sensors installed in pipeline networks has gained a lot of attention due to its potential for application in real-time monitoring systems for leak detection. However, many models developed on data collected from simulations, engineered tests or field experiments, and few have been validated using real network data. To make further investigations in real water distribution systems (WDSs), machine learning (ML)-based leak detection models were developed and validated using signal data collected by piezoelectric accelerometers installed in WDSs over several cities of China. Nonlinear features describing the non-stationary characteristics contained in leak signals are extracted from the signals to build feature sets as inputs to the common classifiers, i.e., support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). In addition, the spectrograms obtained by the short-time Fourier transform processing are used for the convolutional neural network SqueezeNet. The effects of different features, different dataset compositions, and different models for leak detection are compared and analyzed. SqueezeNet has the best performance with 95.15% accuracy in identifying leaks, and KNN is the best of the three classifiers with superior sensitivity and 88.17% accuracy in identification. This paper demonstrates the effectiveness and practicability of using machine learning models to identify leaks in real pipe networks. The application of machine learning models can perform effective leak detection with minimal human intervention, thus providing a meaningful contribution to the study of leak detection in real WDSs. The proposed model aims to identify leakages under operational conditions in real-life pipe networks with higher efficiency and reduced manual involvement. This paper demonstrates the applicability of accelerometers based on microelectromechanical systems for leak detection and establishes real network-based machine learning models; thereby, providing meaningful contribution to the literature regarding leak detection in a real WDS.

A practical physical watermarking approach to detect replay attacks in a CPS

Cyber Physical Systems (CPSs) are considered as an integration of computing, networking, and physical processes. In such systems physical processes are monitored and controlled by the combination of computation, communication, and control technologies. Examples of CPSs include process control systems, medical devices, robots, and critical infrastructures such as water treatment plants, water distribution systems, and power grid. Often, these systems are vulnerable to attacks as the cyber components such as Supervisory Control and Data Acquisition (SCADA) workstations, Human Machine Interface (HMI), and Programmable Logic Controllers (PLCs) are potential targets for attackers. Replay attacks are easy to perform on such systems and can potentially lead to significant damages to the system. Therefore, timely detection of replay attacks is important to mitigate the attack consequences. In this work, we propose a practical watermarking technique to detect the attacks. Experiments are performed on a real WAter DIstribution system (WADI) to explore how to tackle the replay attacks using watermarking signal.

Strategy to Enhance Emergency Interconnected Operation of Water Distribution System

This study identified the causes of insufficient emergency interconnected operation (EIO) performance, such as pressure-related problems and connection problems caused by elevation differences between blocks, the characteristics and locations of emergency interconnection pipes (EIPs), and pumps. Then, it tested four strategies to improve the EIO performance, including increasing the EIP diameter or installing additional EIPs, pressure reducing valves (PRVs), or pumps. The advanced pressure-driven analysis model was applied to quantify the EIO performance improvement achieved using these strategies. Further, these strategies were tested in a real water distribution system. To solve the low-pressure problem, the EIP diameter was increased and an additional pump was installed; the former did not significantly improve, whereas the latter improved supply by 20–30%. To solve the high-pressure problem, PRVs were installed to maintain the EIO performance effectively. To solve connection problems, new EIPs were installed. Although this improved the supply performance, the installation of pumps was recommended to overcome elevation differences. The proposed strategies should contribute to the allocation of facilities such as EIPs, pumps, and PRVs for realizing effective EIO.

Economic feasibility analysis of variable-speed pumps by simulating 15 multiple water distribution systems

Abstract The UN Sustainability Goals address measures to reduce environmental pollution. Water distribution systems (WDSs) use electric energy, which pollutes the atmosphere through, at least partly, the burning of coal. This study simulates, through modeling, variable-speed pumps (VSPs) on 15 different real WDSs on the network solver EPANET and analyzes the payback period. An algorithm is introduced here to select the optimal pump speed pattern to save the most energy while satisfying the constrain of sufficient pressure at all times and all locations. It was found that five of the 15 systems operated unsuccessfully using a VSP, due to the VSP operating at lower speeds causing a lower pressure than normal, thereby causing the pressure to become negative. Additionally, a new chart that compares the payback period, project life, and energy costs between the base case and the VSP case was developed and different regions on the chart reflect different decision criteria.

Review of Nitrification Monitoring and Control Strategies in Drinking Water System.

Nitrification is a major challenge in chloraminated drinking water systems, resulting in undesirable loss of disinfectant residual. Consequently, heterotrophic bacteria growth is increased, which adversely affects the water quality, causing taste, odour, and health issues. Regular monitoring of various water quality parameters at susceptible areas of the water distribution system (WDS) helps to detect nitrification at an earlier stage and allows sufficient time to take corrective actions to control it. Strategies to monitor nitrification in a WDS require conducting various microbiological tests or assessing surrogate parameters that are affected by microbiological activities. Additionally, microbial decay factor () is used by water utilities to monitor the status of nitrification. In contrast, approaches to manage nitrification in a WDS include controlling various factors that affect monochloramine decay rate and ammonium substrate availability, and that can inhibit nitrification. However, some of these control strategies may increase the regulated disinfection-by-products level, which may be a potential health concern. In this paper, various strategies to monitor and control nitrification in a WDS are critically examined. The key findings are: (i) the applicability of some methods require further validation using real WDS, as the original studies were conducted on laboratory or pilot systems; (ii) there is no linkage/formula found to relate the surrogate parameters to the concentration of nitrifying bacteria, which possibly improve nitrification monitoring performance; (iii) improved methods/monitoring tools are required to detect nitrification at an earlier stage; (iv) further studies are required to understand the effect of soluble microbial products on the change of surrogate parameters. Based on the current review, we recommend that the successful outcome using many of these methods is often site-specific, hence, water utilities should decide based on their regular experiences when considering economic and sustainability aspects.

Developing stacking ensemble models for multivariate contamination detection in water distribution systems

This study presents a new stacking ensemble model for contamination event detection using multiple water quality parameters. The stacking model consists of a number of machine learning base predictors and a meta-predictor, and it is trained using cross-validation to capture different features in multiple water quality parameters and then used for water quality predictions. For each water quality parameter, the residuals between predicted and measured data are classified to identify anomalies with thresholds derived from the sequential model-based optimization method and detection probabilities updated using Bayesian analysis. Alarms derived from individual water quality parameters are fused to enhance the anomaly signals and improve the detection accuracy. The proposed stacking-based method is evaluated using a data set of six water quality parameters from a real water distribution system with randomly simulated events. The stacking-based method could detect 2496 events out of a total 2500 events without a false alarm. The results show that the stacking method outperforms an artificial neural network (ANN) benchmark method in contamination event detection. The stacking method has a higher true positive rate, lower false positive rate and higher F1 score than the ANN method. This implies that the stacking method has great promise of detecting contamination events in the water distribution system.

Stochastic Approach for the Analysis of Demand Induced Transients in Real Water Distribution Systems

AbstractA stochastic approach for modeling and analysing the transients due to the users’ water consumptions in a real water distribution system is presented. The analysis is based on field measure...

Sensitivity Analysis for Performance Evaluation of a Real Water Distribution System by a Pressure Driven Analysis Approach and Artificial Intelligence Method

Proper performance of water distribution networks (WDNs) plays a vital role in customer satisfaction. The aim of this study is to conduct a sensitivity analysis to evaluate the behavior of WDNs analyzed by a pressure-driven analysis (PDA) approach and the classification technique by using an appropriate artificial neural network, namely the Group Method of Data Handling (GMDH). For this purpose, this study is divided into four distinct steps. In the first and second steps, a real network has been analyzed by using a Pressure-Driven Analysis approach (PDA) to obtain the pressure, and α coefficient, the percentage of supplied flow. The analysis has been performed by using three different values of the design peak coefficient k*. In the third step, the Group Method of Data Handling (GMDH) has been applied and several binary models have been constructed. The analysis has been carried out by using input data, including the real topology of the network and the base demand necessary to satisfy requests of users in average conditions and by assuming that the demand in each single one-hour time step depends on a peak coefficient. Finally, the results obtained from the PDA hydraulic analysis and those obtained by using them in the GMDH algorithm have been compared and sensitivity analysis has been carried out. The innovation of the study is to demonstrate that the input parameters adopted in the design are correct. The analysis confirms that the GMDH algorithm gives proper results for this case study and the results are stable also when the value of each k*, characteristic of a different time hour step, varies in an admissible technical range. It was confirmed that the results obtained by using the PDA approach, analyzed by using a GMDH-type neural network, can provide higher performance sufficiency in the evaluation of WDNs.

Effects of residual disinfectants on the redox speciation of lead(ii)/(iv) minerals in drinking water distribution systems.

This study investigated the reaction kinetics on the oxidative transformation of lead(ii) minerals by free chlorine (HOCl) and free bromine (HOBr) in drinking water distribution systems. According to chemical equilibrium predictions, lead(ii) carbonate minerals, cerussite PbCO3(s) and hydrocerussite Pb3(CO3)2(OH)2(s), and lead(ii) phosphate mineral, chloropyromorphite Pb5(PO4)3Cl(s) are formed in drinking water distribution systems in the absence and presence of phosphate, respectively. X-ray absorption near edge spectroscopy (XANES) data showed that at pH 7 and a 10 mM alkalinity, the majority of cerussite and hydrocerussite was oxidized to lead(iv) mineral PbO2(s) within 120 minutes of reaction with chlorine (3 : 1 Cl2 : Pb(ii) molar ratio). In contrast, very little oxidation of chloropyromorphite occurred. Under similar conditions, oxidation of lead(ii) carbonate and phosphate minerals by HOBr exhibited a reaction kinetics that was orders of magnitude faster than by HOCl. Their end oxidation products were identified as mainly plattnerite β-PbO2(s) and trace amounts of scrutinyite α-PbO2(s) based on X-ray diffraction (XRD) and extended X-ray absorption fine structure (EXAFS) spectroscopic analysis. A kinetic model was established based on the solid-phase experimental data. The model predicted that in real drinking water distribution systems, it takes 0.6-1.2 years to completely oxidize Pb(ii) minerals in the surface layer of corrosion scales to PbO2(s) by HOCl without phosphate, but only 0.1-0.2 years in the presence of bromide (Br-) due the catalytic effects of HOBr generation. The model also predicts that the addition of phosphate will significantly inhibit Pb(ii) mineral oxidation by HOCl, but only be modestly effective in the presence of Br-. This study provides insightful understanding on the effect of residual disinfectant on the oxidation of lead corrosion scales and strategies to prevent lead release from drinking water distribution systems.

Hydraulic Calibration of Pipe Network Model Using An Improved Genetic Technique (Dept.C)

This research paper exhibits the development and calibration capability of a computer optimization code, called SO-OPTIM. This code copes with several problems concerning water distribution networks which include: new design, rehabilitation, calibration; optimal scheduling of pumps, optimal operation of pump stations in the presence of water elevated tanks, and optimal pressure regulation. These problems are treated using a powerful genetic algorithm optimization technique which consists of a nonlinear objective functions subjected to both linear and non linear constraints. Different modifications were carried out in the simple genetic algorithm. The mentioned code links the optimization model and a hydraulic solver which applies the principles of a new technique used for pipe network analysis in steady state conditions. The proposed code is applied in two different water distribution networks. The first one is a hypothetical network with synthetic calibration data and the SO OPTIM code is applied to perform extended period calibration. The second network is a real water distribution system of Damnhour city, Egypt, with actual data and field measurements and in this case the code performs steady state calibration.

Efficient Leak Localization in Water Distribution Systems Using Multistage Optimal Valve Operations and Smart Demand Metering

AbstractThis paper proposes a multistage method for burst leak localization through valve operations (VOs) and smart demand metering in district meter areas (DMAs) of water distribution systems (WDSs). Each stage includes partitioning of the DMA into two subregions using VOs and identification of potentially leaking pipes within the subregions through water balance analysis based on smart demand meters. Such a process is performed repeatedly (multiple stages) to narrow down the spatial range for pinpointing leak locations. To improve efficiency, a bisection optimization problem is formulated to localize the minimum leak areas using the lowest number of VOs, which is solved by a graph theory‐based method. The utility of the proposed method is demonstrated using two DMAs (DMA1 and DMA2) of a real WDS with different topological properties. Results show that the proposed method can efficiently localize artificial burst leaks in DMA1 within 7–15% of the total pipe length, implying that the proposed method is theoretically effective in localizing pipe burst leaks. The real application to DMA2 has identified two leak regions with 2.3 and 4.2 km of pipe length (around 3–6% of the entire DMA2) using 18 VOs. These two burst leaks have been subsequently confirmed and pinpointed using listening rods by practitioners of the local water utility. These results indicate that the proposed multistage method is effective and efficient for burst leak localization, which can be promising for wide practical applications due to rapid developments of smart WDSs (e.g., smart demand meters or control valves).

THMs, HAAs and NAs production from culturable microorganisms in pipeline network by ozonation, chlorination, chloramination and joint disinfection strategies

Disinfection is an indispensable process to inactivate pathogens, while unexpected disinfection by-products (DBPs) would also be formed between the reaction of residual disinfectants and microorganisms in the water distribution system (WDS). However, there are few studies referring to the formation of DBPs and DBPs-associated toxicity under various disinfection methods based on microorganisms in the real WDS. In addition, the main contributors of bacterial communities or components that generate DBPs are unclear. In this study, the formation of trihalomethanes (THMs), halo-acetic acids (HAAs), nitrosamines (NAs) from culturable microorganisms in pipeline network by ozonation(O3), chlorination (Cl2), chloramination (NH2Cl) and joint disinfection methods were compared, meanwhile, their calculated toxicities under different oxidation scenarios were also discussed. Moreover, 16S ribosomal ribonucleic acid (rRNA) gene sequencing was used to identify the main microbial communities. The results demonstrated that THMs and HAAs increased with increasing disinfectant dosages, while the quantity of NAs (mainly nitroso dimethylamine (NDMA)) was not significantly related to disinfectant dosages for each disinfection strategy. Chloroform (TCM) and dichloroacetic acid (DCAA) were the dominant THMs and HAAs species, respectively. NDMA existed in the samples before disinfections, which may due to the metabolic activity of microorganisms. Pre-O3 increased THMs formation during subsequent Cl2 and NH2Cl treatment. However, pre-O3 effectively reduced HAAs produced by subsequent chlorination. O3/Cl2 disinfection had the highest DBPs formation potential (DBPFP) (883.6 nM), while its calculated toxicity was similar to that in Cl2 disinfection treatment. Pseudomonas was the most abundant bacterial genus in biofilm of WDS pipeline. This study can aid in an optimal disinfection strategy for water treatment plants to reduce the toxicity of DBPs caused by biomass in pipelines and ensure water quality safety.

Performability Evaluation of Water Distribution Systems During Maintenance Procedures

A Water Distribution System (WDS) is a critical infrastructure for society and economy, subject to frequent maintenance either for contingencies or planned operations. Maintenance procedures affect the hybrid dynamics of a WDS at stochastic time points, representing the completion of repair activities that change the WDS topology and operation mode. Hence, the problem of performability evaluation of the WDS behavior during a maintenance intervention falls in the class of stochastic hybrid systems (SHSs), for which existing numerical or simulative approaches cannot afford the complexity of realistic WDSs. We propose a viable approach that computes the expected demand not served during a maintenance procedure by integrating fluid-dynamic analysis of the WDS with quantitative evaluation of the procedure timing, notably assuming non-Markovian repair times over a bounded support. Different solution techniques are presented to evaluate the joint distribution of the times when the procedure affects the WDS, performing either simulation of the procedure model or state-space analysis based on an extension of the method of stochastic state classes. Feasibility and effectiveness of the proposed methods are assessed on a real WDS in terms of result accuracy and computational complexity, showing that the overall approach could be efficiently applied in higher level tasks including activity scheduling, resource planning, and budget allocation.

A stochastic framework for hydraulic performance assessment of complex water distribution networks: Application to connectivity detection problems

This paper is concerned with the hydraulic performance assessment of large scale water distribution networks in presence of uncertainty. In particular, the associate connectivity detection problem is examined in detail. For this purpose, a Bayesian system identification methodology is combined with an efficient hydraulic simulation model. A number of hydraulic model classes are defined as potential connectivity events. Based on information from flow rates in the pipes, the proposed updating technique provides estimates of the most probable connectivity scenarios. Such scenarios correspond to the model classes that maximize their evidences or posterior probabilities. The effectiveness of the proposed identification framework is illustrated by applying the connectivity detection approach to a real water distribution system.

Quality Fuzzy Predictive Control of Water in Drinking Water Systems

With the big demand on water supply during the last century due to population growth, the approbation of new technology to assure water quality at lower cost is essential. This paper presents a drinking water distribution system (DWDS) based on a nonlinear fuzzy modeling technique. The approach uses a multi-input multi-output (MIMO) Takagi–Sugeno (T–S) fuzzy model, which is relevant for constructing a large class of nonlinear processes. The proposed framework is validated on a real drinking water distribution system, the MIMO fuzzy T–S model was implemented, in the context of nonlinear predictive control to regulate the water quality (the chlorine concentration in drinking water). The objective is to keep the system outputs within upper and lower limits from the requirement of health regulations.

Simulation-Based Analysis of a Real Water Distribution Network to Support Emergency Management

Water distribution systems have a safety-critical role and are vulnerable to a range of intentional threats. Committing to emergency preparedness, critical and missing metrics are the residual water storage in tanks and the estimated operative autonomy (in hours). This article presents a high-fidelity simulator developed to calculate the mentioned metrics and to evaluate the impact of adverse events, such as faults and/or cyber-attacks on a real water distribution system. This simulator utilizes a refined model of the network for accurate results; a commercial simulator to trustworthily solve the hydraulic equations; and an ad-hoc module to follow the evolution in time of the event. A large real water distribution network has been modeled thanks to real data provided by an operator and used as a case study. A comprehensive impact analysis of the single failure has been carried out. Results show that the simulator has been able to elicit unforeseen use cases and correctly quantify the required metrics. This article has been implemented in the framework of the European project ATENA.

Optimizing inspection routes in pipeline networks

Maintaining an aging network is a challenge for many water utilities due to limited budgets and uncertainty surrounding the physical condition of buried pipeline assets. The deployment of robotic inspections provides high quality data, but these platforms have limited use due to cost and operational constraints. To facilitate cost-efficient inspections, operators need to identify high-risk assets while accounting for the effectiveness of the tools at hand. This paper addresses inspection planning with the goal of finding an optimal route while considering tool limitations. An exact integer programming formulation is presented where only three factors are used to characterize tool constraints. Two classes of solution methods are explored: 1) tree based searches, and 2) integer programming. This paper demonstrates how each method can be used to identify optimal paths within a real water distribution system. Empirical trials suggest that tree-based search methods are the most efficient when the path limit is short, but do not scale well when the path length increases. In contrast, integer-programming methods are more effective for longer path lengths but have scalability issues for large network sizes. Data preprocessing, where the input network size is reduced, can provide large computation time reductions while returning near-optimal solutions.