Predicting Pipe Failure Effects in Water Distribution Networks

Failures in the water distribution network are some most important factors in water losses, water shortage and dissatisfaction of users and secondary pollutions as well. This research aimed to analyze failure in water distribution networks during 2006-2008, for better water management. Daily failure reports in Ahvaz, Iran distribution network during 2006-2008, were collected from emergency department of Ahvaz water and Wastewater Company; thereafter, they were entered into an Excel database, also failures were defined by pipes type, pipe diameter, and cause of the failure, and finally the data were analyzed. Results indicated that asbestos and polyethylene pipes show maximum failures; maximum failure and fracture has occurred in pipes with 100 and 150 mm diameters. The most important factors affecting on failure were corrosion, traffic load and landslide. In addition, simultaneous influence of type and the diameter of the pipe on the failures were statistically significant. The depth of pipes establishment, corrosion and obsolescence of pipes as well as improper type of pipes were the most important causes of failures in Ahvaz Water Distribution Network. In this regard, upgrading pipe material setting standards and renewing water pipe network are the main strategies for failures minimization.

Several researchers have studied the use of informational entropy as a surrogate measure for the reliability of water distribution networks. The hypothesis is that the numerical value of network entropy in some way reflects the reliability of water distribution networks, and this appears to be supported by the analysis of some example water distribution networks. However, the precise relationship between the entropy value and some measures of reliability has not been formally established. The primary objective in this paper is to present an alternative methodology to calculate the informational entropy of water distribution networks. This methodology is termed as the Path Entropy Method (PEM), which provides some insights into the entropy of branching-tree networks and maximum-entropy flows of single-source networks. In addition, a quick method of computing the maximum-entropy value of single-source networks is presented and termed as the Simplified Path Entropy Method (SPEM).

Hydraulic analysis of pipe networks is generally done considering certain values for independent parameters of the system such as roughness of pipes, nodal demands, etc. Such analysis would inevitably lead to certain hydraulic responses of the system, i.e. nodal pressures, etc. This is while, inherent uncertainties associated with independent parameters as expected stresses, spread over the system and result in hydraulic stress in nodal pressure heads as dependent uncertainties. Using optimization tools, this study presents a reliable approach based on interval analysis to deal with these uncertain hydraulic stresses. In the proposed approach, the optimization problem is formulated in a manner that the nodal demands, roughness of pipes and water levels in elevated tanks would be the decision variables while extreme nodal pressures for unknown intervals are explored as the objectives functions. The large number of junctions in the case of real pipe networks, leads to inefficient iterative use of single objective optimization engine. In order to this problem, this study exploits a many-objective approach with an appropriate performance. Applying the proposed approach on a real pipe network shows that ±15% variation in nodal demands and pipes’ roughness in addition to ±1m in water levels might produce hydraulic stress in pressure heads from -13.7% to +10.2% with regard to the crisp values. In such a condition, it is possible for 125 junctions out of 128, to fail in satisfying the minimum required pressure head. It is demonstrated that the proposed approach has acceptable accuracy for analyzing hydraulic stress in real water distribution networks.

The analysis of a looped water distribution network, operating under pressure and in steady flow conditions, can be accomplished once the topology of the network, the geometry of the pipes, the water demands at the nodes and the head value of at least one node are known. In a water distribution network (WDN), water demands are assigned to the nodes, although in reality they are distributed along the pipes converging at such nodes. This classic assumption represents the total demand along a pipe as two lumped withdrawals at its terminal nodes. This paper demonstrates that the above approximation is wrong because it generates head loss errors which may be significant when network analysis is performed for calibration, system design, real-time operations, rehabilitation strategies, optimal operation studies, reliability analyses, etc. Therefore, an extension of the global gradient algorithm (GGA) for network analysis is proposed which entails a modified GGA permitting the effective introduction of the lumped nodal demands, and without forfeiting a correct physical representation of head losses, by means of a pipe hydraulic resistance correction.

In this research, reliability indicators of water distribution networks are evaluated under pipe failure conditions. The case studies include two benchmark and one real-life water distribution networks in Iran with more hydraulic constraints. Some important reliability indicators are presented such as resilience index, network resilience, modified resilience index and minimum surplus head index. GANetXL is used to do one-objective and two-objective optimization of the previously mentioned water distribution networks in order to not only minimize the cost, but also maximize the reliability indicators. Moreover, the results of a statistical analysis for each pipe is used to determine the sensitive pipes that are of the most failure probability. GANetXL is an optimization tool in Excel environment and works based on Genetic Algorithm. GANetXL has the capability of being linked to EPANET (Hydraulic simulation software). The results obtained clearly show that network resilience index is of poor performance when compared with the other indexes under pipe failure conditions, especially in real-life networks that include small pipe diameters. It was also showed that if a water distribution network was optimized only in terms of cost, there would be an unacceptable pressure drop at some nodes in case of pipe failure.

Gröbner bases for solving ΔQ-equations in water distribution networks

Water distribution networks are expected to fulfill the water demand by all consumers and at all times, even during critical scenarios, such as pipe failures. In this work, a methodology is proposed to maximize the quality of service during pipe failures by operating valves. The selection of the valves to operate is done by solving an optimization problem using Gondwana, a generic optimization tool for drinking water distribution networks. Different objective functions and different failure scenarios are investigated, considering a real-life water distribution network. The analysis is performed considering the peak demand condition. The proposed methodology is useful for water companies in managing the operation of their networks during critical scenarios.

Night flow analysis is predominantly used to identify incipient (gradual) leakages in real-life Water Distribution Networks (WDNs). However, due to extreme stochastic demand especially in residentially dominated district metering areas (DMAs) and the emergence of sleepless cities, traditional night flow analysis methods have become inefficient. This study proposes a semi-supervised sequence-to-sequence Bidirectional Long Short-Term Memory (BiLSTM) deep Auto Encoder (AE) method for night flow analysis in WDNs. To enhance the generalisation power of the deep AE, a vigorous data augmentation procedure based engineering domain knowledge and inherent properties of DMA is presented. The proposed method learns the underlying benign patterns in night flow (2:00 am to 4:00 am) and identifies significant deviations that represent the development of incipient leakages. The method was validated on residential, commercial, and industrial DMAs in a real-life WDN in the Ålesund Municipality of Norway. The results from the study showed that the proposed method is a robust and superior alternative to traditional night flow under extreme stochastic consumer demand. The proposed BiLSTM deep AE method was able to identify unreported leakages in the DMAs within 1 day or at most 4 days compared with at least 15 days identification time by traditional minimum night flow (MNF) and average night flow (ANF) analyses. Additionally, traditional minimum night flow analysis also produced more false positives compared with the proposed method. The results also revealed the usage of point estimates to represent night flow as done in MNF and ANF analyses fail to capture all salient information regarding night flow compared to the BiLSTM deep AE.

Future water demands and pipe roughness coefficients used in the design of water distribution networks (WDNs) have a high degree of uncertainty. Fuzzy analysis of WDNs provide how the uncertainties in independent or basic parameters (such as nodal demands and pipe roughness coefficients) are propagated to dependent or derived parameters (such as pipe flows, pipe velocities, and available pressure heads). Usually demand dependent analysis is used for such analysis. A WDN may be pressure deficient or may become pressure deficient because of high pipe roughness or inadequate pump pressure and may not be able to meet desired demands at all nodes. Thus, it is desirable to consider the nodal outflows as unknown and as a function of available pressure heads under pressure-deficient conditions. An approach for fuzzy analysis of a WDN that takes into account pressure-deficient condition using node flow analysis is presented in this paper. The proposed methodology is illustrated with an example network taken from literature. The methodology is useful in identifying vulnerable zones in WDNs and can be extended for reliability analysis under uncertainty of nodal demands and pipe roughness coefficients. Comparison of results with usual demand-dependent analysis shows that proposed method is better for identifying vulnerable zones.

Water Distribution Networks (WDNs) are crucial for meeting current and future urban water demands. Knowledge of future water demand at different time scales is fundamental for optimal WDN design and operations. Various predictive models of water demand have been proposed in the literature, ranging from traditional time series analysis to cutting-edge machine learning and deep learning techniques. However, the task of forecasting urban water demand remains mostly decoupled from the design of the optimal control of the related WDN. Current performance assessment of demand predictive models focuses predominantly on forecast accuracy, overlooking their practical implications on WDN operations. Meanwhile, the existing research on WDN management often assumes either perfect knowledge of future water demands or employs empirical approximations and aggregate statistics to estimate future water needs. This study bridges this gap by scrutinizing the actual operational value of water demand forecasts in designing optimal operations of WDNs.Here, we develop a forecast-informed optimal WDN control framework to evaluate the operational value of water demand forecasts for WDN operations. Our framework comprises two main modules. The first module computes water demand forecasts. We comparatively evaluate a suite of different forecasting models —including Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors, Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory (a network augmented with an attention mechanism) —across various forecast horizons (1/6/12 hours and 1/3/7/14 days ahead). The most accurate forecasts are integrated into the second module, an economic nonlinear Model Predictive Control (MPC) algorithm designed to optimize WDN operations. Within this module, an Artificial Neural Network (ANN) – based surrogate model encapsulates the hydraulics of the physical WDN, enhancing the optimization process while reducing complexity.We demonstrate our forecast-informed optimal WDN control framework on a real WDN of a rural town with approximately 10,000 inhabitants. Water demand data collected in the Netherlands for a period of 10 years (2007-2017) at 5-minute resolution and corresponding meteorological data are used to train the water demand forecasting models. The MPC module computes the optimal control sequence for 7 pumps and 1 valve in the WDN to minimize pump energy costs while meeting water demands and ensuring safety storage in 5 tanks. Initial findings reveal that the ANN-based surrogate model can accurately incorporate the WDN characteristics (R2 > 0.85), facilitating its integration into the MPC for an efficient and simplified representation of a real-world WDN. Further, MPC fed by 24-hour ahead water demand forecasts achieves potential energy savings of approximately 18% compared to a benchmark rule-based control strategy. Our framework yields a versatile simulation-based optimization tool for evaluating the impact of demand forecasts on WDN management. Future research efforts will aim at refining and comparing deterministic and stochastic water demand forecasts within the MPC framework, under diverse operational objectives.

Water distribution network (WDN) design problems generally consider minimising network costs while meeting minimum allowable pressure under specified demand patterns. However, extension and renovation of networks based on assessment of demand uncertainty causes an increase in efficiency of the system and monetary savings. Attention to uncertain changes in pattern and the value of nodal demands in an operational period can thus show the effect of uncertainties in the network's efficiency. Using Monte Carlo simulation and shuffled frog leaping algorithm optimisation, this paper analyses the efficiency of a designed network based on nodal demand uncertainty during the operational period. Assessment of a network efficiency index according to a probabilistic normal distribution for nodal demand in an operational period shows that a network design that does not consider nodal demand uncertainty decreases network efficiency, especially at the end of the operational period. Moreover, an increase in demand uncertainty decreases network efficiency. The results of the proposed model for a benchmark WDN show that a network designed deterministically for a 30-year operational period is unable to secure nodal demands with suitable pressure after 10 years. In addition, the minimum required pressure considered in network design is met in approximately the first 18 years of the operational period.

Water distribution systems are considered vital arteries of urban communities. The purpose of a water supply distribution system or network is to provide water to consumer with adequate pressure and quality. The quality must be healthy and standards in terms of chemical and biological standards. Chlorine is used as the most common disinfectant in municipal drinking water supply networks due to its high efficiency and economy. The aim of this study was to assess the effect of environmental temperature on residual chlorine, turbidity, TOC and HPC in Abadan, Iran, WDN, during summer and winter seasons. In this cross-sectional study, according to the water supply maps, the 232 samples from different point of WDN were analyzed to measurement of free residual chlorine, total organic carbon (TOC), Turbidity (NTU), and heterotrophic plate count (HPC) (cfu/100 ml), based on the standard methods for examination of water and wastewater (APHA). Samples were collected from effluent of water treatment plant to distribution system according to the Cluster sampling model. Regression analysis was used to investigate the relationship between variables. Results show that temperatures in Abadan typically range from 29 to 46 °C at summer and 12 to 27 °C in winter. Statistical analyzes show that as the ambient temperature increased, the free residual chlorine in the distribution network from the water treatment plant decreased to the end of the distribution network and reached zero at some end points (R2 = 0.97). Also TOC, Turbidity (NTU), and HPC (cfu/100 ml) showed a significant increase with increasing environmental temperature in distribution network (P < 0.05). According to the results of this study, in cities with high seasonal air temperatures, due to changes in the water quality of the distribution network, it is necessary to take re-chlorination, change the coagulation and flocculation process of surface water treatment plants in, and continuous monitoring of WDN in hot seasons.KeywordsEnvironment temperatureFree residual chlorineNOMTurbidity and HPCWater distribution system

The water distribution network plays a key role in ensuring and providing a high quality of life for the public, with supply dependability being the most crucial component. A communities thrive is dependent on the availability of safe drinking water for its members. As the population of any community increases, the request for water also increases which imposes additional load to the existing water distribution system. As a result, the present water distribution system may become untrustworthy to meet the demand of the increased population. It is vital to provide an adequate and equitable quantity of water through a well-designed network of pipes in order to meet the ever-increasing water demand of the population. LOOP (Version 4.0) can be used to develop and simulate gravity and pumped water distribution systems that are novel, partially or completely functional. LOOP 4.0 is used to determine the cost of pipe network. EPANET is a computer program that models hydrodynamic behaviour in a pressurised pipe network over a long period of time. The Examination presents the hydraulic analysis of pipe network of Narangi area in Virar city using Loop 4.0 and EPANET 2.0. The results confirmed that the pressure at all junctions and velocities at all pipes are acceptable to deliver appropriate water to the study area’s network, as well as aiding in a better understanding of the study area’s pipeline system. The study also deals with the cost of distribution network.KeywordsLOOP 4.0EPANET 2.0Arc GISElevationNodesPipe networkPressureWater supply

Water stands as the fundamental cornerstone of life, and with the human population growing exponentially, the critical importance of sustainable water management becomes increasingly evident. Water not only serves as the essential working fluid for major chemical processes, including thermal energy generation, but it is also integral to manufacturing and domestic use. Despite its significance, water is a scarce commodity, and the planet continues to lose this precious resource due to theft, pipe bursts, and leakages in water distribution networks (WDNs). The consequences of water loss extend beyond the impact on physical resources, as it is intricately linked to financial challenges. Thus, water loss has the potential to result in substantial financial setbacks for companies, organizations, and government entities, underscoring the interdependence of water management and fiscal sustainability. In response to these challenges, digital technologies have been harnessed to optimize WDNs. A major constraint lies in deciphering the applicable digital technologies due to the multidisciplinary nature of the research area encompassing WDNs and digital technologies; therefore, this study adopts a systematic literature review (SLR) combined with digital tools, to comprehensively extract all aspects of a WDN, ranging from sensors to enterprise resource planning (ERP), including equipment and systems. This literature forms the foundational basis for constructing a framework aimed at creating a content repository for digital WDN sustainability. To further develop the framework, this study reviews international best practices in digital WDN. This is adopted in developing a digital WDN framework based on the literature and classifying a database structure from sensor to ERP for a generic WDN. Furthermore, the study explores tools and systems for optimization as well as updating the theoretical structure with Johannesburg WDN data and provides a comprehensive evaluation for enabling digital WDN in South Africa. The digital WDN framework comprises three distinct components: security, a central management database (CMDB), and best practice architecture. The architecture involves five layers, including the physical layer, communication layer, data storage and analysis layer, benefit layer, and application layer. Utilizing systematic literature review and bibliometric keyword analysis, the study determines the equipment, systems, and their uses, leading to the development of a generic WDN. Validation of the framework using the generic WDN emphasizes the equipment and systems used in each of the five layers. Furthermore, the framework is validated using Johannesburg Water's WDN, leading to the development of a CMDB. The CMDB underscores the possibility of storing physical layer and communication layer configuration items (CIs) and highlights asset management, CI relations, change management, and historian capabilities. Maintaining an accurate and updated CMDB necessitates employing asset discovery solutions, while CMDB relationship mapping plays a crucial role in assessing the ripple effects of CI changes within a complex system. Finally, the measurement of the digital maturity of Johannesburg Water's WDN using the Administração e Gestão de Sistemas de Salubridade (AGS) maturity index reveals a maturity index of 62%. The index emphasizes strong capabilities in GIS integration and data collection through the SCADA system. Despite advancements in planning systems, such as domestic flow metering, there remains an opportunity to enhance asset management capabilities for continuous improvement in WDN operations.

This paper explores numerically the benefits of water discharge prediction in the real time control (RTC) of water distribution networks (WDNs). An algorithm aimed at controlling the settings of control valves and variable speed pumps, as a function of pressure head signals from remote nodes in the network, is used. Two variants of the algorithm are considered, based on the measured water discharge in the device at the current time and on the prediction of this variable at the new time, respectively. As a result of the prediction, carried out using a polynomial with coefficients determined through linear regression, the RTC algorithm attempts to correct the expected deviation of the controlled pressure head from the set point, rather than the currently measured deviation. The applications concerned the numerical simulation of RTC in a WDN, in which the nodal demands are reconstructed stochastically through the bottom-up approach. The results prove that RTC benefits from the implementation of the prediction, in terms of the closeness of the controlled variable to the set point and of total variations of the device setting. The benefits are more evident when the water discharge features contained random fluctuations and large hourly variations.