Search Results (121)

Water distribution systems (WDSs) exhibit intricate, nonlinear behaviors shaped by both internal dynamics and external influences. The incorporation of additional models, such as contamination or population models, further increases their complexity. This study investigated WDSs under various uncertainty scenarios to enhance system stability, robustness, and control. In particular, we built upon prior research by exploring an Agent-Based Modeling (ABM) framework integrated within a WDS, focusing on three types of uncertainties: (1) adjustments to existing probabilistic parameters, (2) variations in agent movement across network nodes, and (3) changes in agent distributions across different node types. We conducted our analysis using the virtual city of Micropolis as a testbed. Our findings indicate that while the system remains resilient to uncertainties in predefined probabilistic parameters, substantial and often nonlinear effects arise when uncertainties are introduced in agent mobility and distribution patterns. These results emphasize the significance of understanding how WDSs respond to external behavioral dynamics, which is essential for managing real-world challenges, such as pandemics or shifts in urban behavior. This study underscores the necessity for further research into broader uncertainty categories and emergent effects to enhance WDS modeling and inform decision-making. Full article

The fractal dimension is a non-Euclidean measurement of how a fractal fills space and how irregular that arrangement is. Water distribution systems are non-Euclidean fractals whose fractal dimensions have provided insight into mathematical models to achieve optimal, minimum-cost designs. These insights are inconclusive, as they have not yet generalized the behavior of the fractal dimension of the hydraulic gradient surface of feasible designs with respect to near-optimal solutions. To approach a mathematical description for optimality in design and operation, this paper studied the fractal dimension of the energy, infrastructural, and flow distributions of mono-objective and biobjective designs. Mono-objective designs were obtained from the Optimal Power Use Surface, while biobjective designs used NSGA-II, OPUS/NSGA-II, and GALAXY. Their corresponding fractal dimensions were computed using the box-covering algorithm. Results show that the fractal dimension only depends on the topology. From this finding, fractal analysis is proposed as a tool to define topology in the design of water distribution systems to further minimize costs obtained using current design methodologies. Pipe roughness and demand sensitivity analyses revealed weak fractal behavior, suggesting the operative use of the fractal dimension as a pipe aging and demand variation indicator. Full article

This study focuses on Fortaleza, the largest metropolis in Brazil’s semi-arid region. Due to recurrent droughts, massive infrastructure like high-density reservoir networks, inter-municipal and interstate water transfer systems, and a seawater desalination plant have been implemented to ensure the city’s water security. To evaluate the quantitative and qualitative impact of introducing these diverse water sources into Fortaleza’s water supply macrosystem, adequate calibration of the operating and demand parameters is required. In this study, the macrosystem was calibrated using the Particle Swarm Optimization (PSO) method based on hourly data from 50 pressure head monitoring points and 40 flow rate monitoring points over two typical operational days. The calibration process involved adjusting the operational rules of typical valves in large-scale Water Distribution Systems (WDS). After parameterization, the calibration presented the following results: R² of 88% for pressure head and 96% for flow rate, with average relative errors of 13% for the pressure head and flow rate. In addition, with NSE values above 0.80 after calibration for the flow rate and pressure head, the PSO method suggests a significant improvement in the simulation model’s performance. These results offer a methodology for calibrating real WDS to simulate various water injection scenarios in the Fortaleza macrosystem. Full article

Water distribution systems (WDSs) are crucial for providing clean drinking water, requiring an efficient design to minimize costs and energy usage. This study introduces an enhanced life cycle energy analysis (LCEA) model for an optimal WDS design, incorporating novel criteria for pipe maintenance and a new resilience index based on nodal pressure. The improved LCEA model features a revised unit energy formula and sets standards for pipe rehabilitation and replacement based on regional regulations. Applied to South Korea’s Goyang network, the model reduces energy expenditure by approximately 35% compared to the cost-based design. Unlike the cost-based design, the energy-based design achieves results that can relatively reduce energy when designing water distribution networks by considering recovered energy. This allows designers to propose designs that consume relatively less energy. Analysis using the new resilience index shows that the energy-based design outperforms the cost-based design in terms of pressure and service under most pipe failure scenarios. The implementation of the improved LCEA in real-world pipe networks, including Goyang, promises a practical life cycle-based optimal design. Full article

Resilience-based decision-making for urban water distribution systems (WDSs) is a challenge when WDS sensing data contain incomplete or missing values. This study investigated the impact of missing data imputation on a WDS resilience evaluation depending on missing data percentages. Incomplete datasets for the nodal pressure of the C-town WDS were developed with 10%, 30%, and 50% missing data percentages by manipulating a true dataset for normal operation conditions produced using EPANET. This study employed multiple imputation methods including classification and regression trees, predictive mean matching, linear regression regarding model error, and linear regression using projected values. Then, resilience values were evaluated and compared using unimputed and imputed datasets. An analysis of performance indicators based on NRMSE, NMAE, NR-Square, and N-PBIAS revealed that higher missing-data percentages led to increased deviation between the true and imputed datasets. The resilience evaluation using unimputed datasets produced significant deviations from the true resilience values, which tended to increase as the missing data percentages increased. However, the imputed datasets substantially contributed to reducing the deviations. These findings underscore the contributions of data imputation to enhancing resilience evaluation in WDS decision-making and suggest insights into advancing a resilience evaluation framework for urban WDSs with more reliable data imputation approaches. Full article

Water distribution systems (WDSs) are critical infrastructure systems designed to safely supply water to consumers. As complex systems, they require constant operational decision-making, which is often the result of an optimization process. WDSs require power for pumping and the operation of water treatment facilities. Power is supplied through power grids (PGs)—essential infrastructure which must be strategically operated as well, under constraints. This work is focused on the effects of PG operation on water quality, which is a major operational challenge of WDSs. The inclusion of the PG as part of the WDS optimal operation problem has the potential of influencing flow directions in the WDS, which in turn affects water quality. In this work, a model for the optimal operation of water and power networks is constructed, including water age considerations. The model is applied to a simple case study, containing a small WDS connected to a small PG. The results demonstrate the effect of PG operation on water quality. Full article

An EPA-SWMM model was used for the simulation of the intermittent water distribution system (WDS) of a small municipality in southern Italy. The model was compared with field data collected during an experimental campaign carried out in the intermittent WDS. The whole cycle of operation of the WDS was simulated, including the filling, distribution and emptying phases of the intermittent network. The modelling also included water leakages and private tanks that are normally interposed between network pipes and end users. Comparison of model results and experimental observations concerned water levels at the reservoirs and pressures at specific nodes of the WDS during some days of the experiments. Full article

Water distribution systems (WDSs) are designed to deliver potable water across urban areas. Unpredicted changes in water demands and hydraulics can increase the residence time in pipes, leading to the growth of microbes and decreased water quality at some locations in a network. During the COVID-19 pandemic, large-scale reductions in demands, especially in industrial and commercial areas as individuals worked from home, led to hot-spots of increased water age. In response to reduced water quality, consumers may avoid using tap water for end uses including drinking, cooking, and cleaning. The lack of access to clean water can create high costs for some households due to the cost of buying bottled water. Inequitable access to safe, affordable water is explored in this research in the context of the COVID-19 pandemic through a coupled framework. This research extends an existing agent-based modeling (ABM) framework that simulated COVID-19 transmission, social distancing decision-making, reductions in water demands, and flows in a water distribution system. The ABM is extended in this work to simulate households that perceive water quality problems with tap water and choose to buy bottled water for cooking, cleaning, and hygienic purposes. Agents choose tap water avoidance behaviors based on water age, a surrogate for water quality. Equity is evaluated using the cost of water, both tap and bottled, as a percentage of income. An optimization approach is coupled with the ABM framework and applied to design operational strategies that improve equitable access to safe affordable water. A graph theory approach identifies valves that should be opened and closed to improve water quality at nodes and maximize equity. The results demonstrate an increase in water age due to social distancing behaviors, and water of high age is observed to be disproportionately located near industrial areas. Adjusted income demonstrates inequities in access to safe and affordable water. Operational strategies are developed to improve equity for a community through valve operations that improve the equitable delivery of safe water. This research develops an approach to assess equity of the quality of delivered water and can be used to facilitate WDS management that provides equitable access to safe water. Full article

Water distribution systems (WDSs) play a vital rule in communities, ensuring well-being and supporting social and economic growth. Consequently, they are recognized as critical infrastructures for which identifying priorities in design and maintenance interventions is a fundamental step. This work applies a methodology founded on graph theory based algorithmic approach to identify a connected subgraph named the Primary Network (PN) in complex WDSs by focusing on the potentially most suitable paths for transferring water resources in a perspective of interconnection of water sources. Furthermore, the PN can constitute the supporting infrastructure on which to set up a possible WDS sectorization. An efficient implementation is discussed, and the results are presented for a real WDS. Full article

Variability in water use and user characteristics influences the operational management of water distribution systems (WDS). Types of water use and external factors including socioeconomic characteristics and weather variables can affect the normal operation of WDS. Accurate demand prediction is crucial, yet existing methods lack industry-wide comparability. This study applies a supervised learning model, IONET, that utilizes feedforward neural networks for short-term demand forecasting. IONET incorporates lagged demand, seasonal predictors, and weather variables. Tested on Italian DMA data, it swiftly produces accurate forecasts across various horizons. Feature importance analysis underscores the significance of seasonal variables and lagged demand. The IONET model offers prompt training and valuable insights for optimizing WDS management, facilitating the digital transformation of water infrastructure. Full article

This study introduces a novel deep learning framework for detecting leakage in water distribution systems (WDSs). The key innovation lies in a two-step process: First, the WDS is partitioned using a K-means clustering algorithm based on pressure sensitivity analysis. Then, an encoder–decoder neural network (EDNN) model is employed to extract and process the pressure and flow sensitivities. The core of the framework is the PP-LCNetV2 architecture that ensures the model’s lightweight, which is optimized for CPU devices. This combination ensures rapid, accurate leakage detection. Three cases are employed to evaluate the method. By applying data augmentation techniques, including the demand and measurement noises, the framework demonstrates robustness across different noise levels. Compared with other methods, the results show this method can efficiently detect over 90% of leakage across different operating conditions while maintaining a higher recognition of the magnitude of leakages. This research offers a significant improvement in computational efficiency and detection accuracy over existing approaches. Full article

Contamination poses a significant risk to public health by degrading water quality in water distribution systems (WDSs). As one of the key tasks of a response strategy to contamination incidents in a WDS, pipe system flushing has been widely implemented in practice. However, due to the complexity of the network structure and chemical reaction within the pipe system, determining the flushing duration is still one of the significant challenges for a given network. To address this problem, a model for determining the flushing duration is developed. This model is based on calculating the traveling trajectory of the contaminant inside the network. This is carried out by discretizing the one-dimension advection equation and calculating the variation of the contaminant concentration from one segment to another over time. As a preliminary study, we focus on simplified scenarios where contaminants exhibit no chemical reaction within the WDS. The proposed model is applied and analyzed through a simulation study and a laboratory testbed. The results demonstrate the efficacy of the model for determining flushing duration, which can offer valuable insights for real-world applications and serve as a crucial reference for water utility companies. Full article

Water distribution systems (WDSs) need electric power supply to operate their pumps. Long-lasting power outages (blackouts) can disrupt the availability of water for citizens. If the water supply is limited by constrained pumping capacities caused by the blackout, water demand reduction could help preserve this limited supply, while increased water withdrawal, i.e., stockpiling, could deplete it. This study investigates the effects and subsequent uncertainty of demand response, especially stockpiling, on WDSs in a blackout. Therefore, we (i) model residential water demand reduction, regular water demand, and water stockpiling in a blackout, (ii) simulate the effect of the demand response on the WDS of Darmstadt, Germany, and (iii) investigate uncertainty resulting from the demand response and initial states of the WDS at time of the onset of the blackout. The findings indicate that the demand response and initial tank levels are the main sources of uncertainty and that demand-side management bears the potential to improve water service availability during a blackout. Full article

This research explores the integrated management of water distribution systems (WDS) and power distribution systems (PDS) to improve their resilience to extreme scenarios. This study delves into the dynamics of a locally managed PDS as an example of extreme operational conditions. The primary objective is to minimize load shedding (LS) in the PDS through strategic load shifting in the interconnected WDS, demonstrating the potential of cooperative decision making between the two critical systems. The optimization framework offers a novel approach to managing flexible resources during emergencies by utilizing the mutual links between a WDS and a PDS. Typically, WDSs and PDSs are operated by different operators such that cooperation is limited. This study presents how communication based on limited information sharing between the two systems is sufficient to increase resilience and improve the systems’ functionality, emphasizing the advantages of cooperative decision making. This paper highlights the significance of cross-sectoral collaboration, presenting a viable pathway for managing local infrastructure systems under extreme conditions while ensuring uninterrupted service delivery to communities. Full article

Most existing approaches to ensuring water quality in water distribution systems (WDSs) are deterministic, i.e., they do not consider uncertainties, although they may have significant impacts on the water quality. It is well recognized that water demand represents a predominant uncertainty in a WDS. In addition, water age is often used as an important parameter to describe the water quality in a WDS and can be influenced by water demand and control elements such as pressure-reducing valves (PRVs). Therefore, the aim of this study is to carry out a probabilistic analysis of the impact of demand uncertainty on the water age in the distribution network. Based on the solution of deterministic optimization to minimize the water age, Monte Carlo simulation will be carried out by sampling the uncertain demand to evaluate the stochastic distribution of water age, as well as other operating variables like pressure and flow. As a result, the probability of violating the constraints of such variables can be determined, with the reliability of the operating strategy (e.g., the settings of the PRVs) given by deterministic optimization provided. In cases of low reliability, it is necessary to modify the operating strategy in order to decrease the probability of constraint violation. For this purpose, a chance-constrained optimization problem is formulated, and its benefits for ensuring the user-defined reliability are studied. A benchmark network is used to verify the proposed approach. Full article