Design Of Water Distribution Networks Research Articles

Optimal pipe-sizing design of water distribution networks using modified Rao-II algorithm

Optimal pipe-sizing design of water distribution networks using modified Rao-II algorithm

Coupled multi-objective optimization of water distribution network design and partitioning: a spectral graph-theory approach

ABSTRACT This paper proposes a novel method that integrates a tailored spectral graph metric and economic aspects within a coupled bi-objective optimization framework for the simultaneous pipe sizing and partitioning of water distribution networks (WDNs). Besides minimum pressure head and topological WDN connectivity, the telescopic diameter distribution criterion is applied through a new algorithm in the sizing phase to help the optimization converge towards sound engineering solutions. The application of the proposed method to a synthetic layout and a real Italian case study proves the superiority of the solutions obtained with the novel method from hydraulic and managerial/operational viewpoints in comparison with those obtained with a traditional decoupled method (separate pipe sizing and partitioning) in terms of i) compliance with service pressure constraints and ii) ease of management following WDN partitioning into district metered areas (DMAs).

Analysis and Design of Water Distribution Network for Jabalpur Cantonment Board Area

In this research paper focuses on studying and improving the water distribution network in the Jabalpur Cantonment Board Area. Water distribution networks are crucial infrastructures for ensuring reliable water supply to communities. However, many areas, including Jabalpur Cantonment, face challenges related to water distribution efficiency, reliability, and equitable access. The study begins with a comprehensive analysis of the existing water distribution network in the Jabalpur Cantonment Board Area. Factors such as network layout, pipe material, age of infrastructure, and water demand patterns are thoroughly examined to identify existing inefficiencies and areas for improvement. Using advanced engineering tools such as WaterGems and methodologies, an optimized design for the water distribution network is proposed. This design considers factors such as hydraulic efficiency, pressure management, and resilience to minimize water losses and ensure consistent water supply to consumers. Furthermore, the study incorporates considerations for future growth and development in the Jabalpur Cantonment Board Area. By employing sustainable design practices and incorporating modern technologies, the proposed water distribution network aims to meet the present and future water needs of the community while minimizing environmental impact. By implementing the proposed design improvements, the Jabalpur Cantonment Board can achieve a more sustainable, efficient, and reliable water supply network, ultimately improving the quality of life for its residents.

Optimization for cost-effective design of water distribution networks: a comprehensive learning approach

The Comprehensive Learning Gravitational Search Algorithm (CLGSA) has demonstrated its effectiveness in solving continuous optimization problems. In this research, we extended the CLGSA to tackle NP-hard combinatorial problems and introduced the Discrete Comprehensive Learning Gravitational Search Algorithm (D-CLGSA). The D-CLGSA framework incorporated a refined position and velocity update scheme tailored for discrete problems. To evaluate the algorithm's efficiency, we conducted two sets of experiments. Firstly, we assessed its performance on a diverse range of 24 benchmarks encompassing unimodal, multimodal, composite, and special discrete functions. Secondly, we applied the D-CLGSA to a practical optimization problem involving water distribution network planning and management. The D-CLGSA model was coupled with the hydraulic simulation solver EPANET to identify the optimal design for the water distribution network, aiming for cost-effectiveness. We evaluated the model's performance on six distribution networks, namely Two-loop network, Hanoi network, New-York City network, GoYang network, BakRyun network, and Balerma network. The results of our study were promising, surpassing previous studies in the field. Consequently, the D-CLGSA model holds great potential as an optimizer for economically and reliably planning and managing water networks.

Multi-criteria decision-making methods for optimal design of intermittent water distribution network

ABSTRACT Multi-objective optimization of water distribution networks (WDNs) is crucial for achieving a balance between cost, equity, and reliability. The present study conducts a comparative analysis, evaluating the effectiveness of four multi-objective optimization algorithms implemented within the EPANET framework for the optimal design of three distinct WDNs taken from the literature. The networks were optimized considering three objectives: maximizing network resilience, and uniformity coefficient (CU, a measure of uniformity), alongside minimizing the cost of the network. Further, multi-criteria decision-making (MCDM) analysis is carried out, encompassing various MCDM techniques and weighting methods, to explore the sensitivity of solution rankings. In Network 1, non-dominated sorting genetic algorithm 2 (NSGA-II) produced suboptimal solutions with CU and In values below 0.4, while caRamel, multi-objective particle swarm optimization based on crowding distance (MOPSO-CD), and multi-objective evolutionary algorithm with decomposition (MOEA/D) showcased increasing CU and In values towards 1, paralleled by a rise in network cost. The performance of NSGA-II in Network 3 was unsatisfactory (CU < 0.32), while the other algorithms demonstrated satisfactory values (>0.98) for reliability and (>0.5) for CU. The findings offer valuable insights into cost-effective strategies for achieving equity in water supply without substantial impact on overall network cost. The findings of the study also underscore the sensitivity of ranking outcomes in MCDM analysis to the choice of technique and weighting method.

Circulatory system-based optimization algorithm with dynamic penalty function for optimum design of large-scale water distribution networks

Water distribution networks (WDNs) are a crucial component of urban infrastructure, and their optimization plays a vital role in minimizing construction costs. However, existing heuristic algorithms for WDN optimization often rely on specific parameters, limiting their performance and hindering their applicability to diverse network configurations, particularly in large-scale systems. To address this challenge, a novel method called circulatory system-based optimization (CSBO) is proposed, which minimizes the need for algorithm-specific parameters and achieves optimal designs across WDNs of various scale. The impact of a dynamic penalty function tailored to pressure constraints on the optimal design is also investigated. This study encompasses the optimization of the Goyang and combined gravity networks and the large-scale Balerma irrigation and Kang and Lansey networks under identical conditions. The results of the standard and dynamic CSBO approaches prove that the proposed algorithms have a superior performance, particularly in convergence rate and stability, compared to previous methods.

A Review Article on Analysis and Design of Water Distribution Network for Jabalpur antonment Board Area

The provision of a reliable and efficient water distribution system is fundamental for ensuring adequate water supply to urban areas. This review article aims to analyze and evaluate the design aspects, hydraulic performance, and challenges related to the water distribution network in the Jabalpur Cantonment Board Area. The study comprehensively examines the current state of the water distribution system, evaluates its operational efficiency, and proposes recommendations for enhancing its performance and sustainability.The review commences by providing an overview of the Jabalpur Cantonment Board Area, highlighting the demographic, geographic, and infrastructural aspects relevant to the water distribution network. It delves into the existing system layout, network configuration, pumping stations, reservoirs, pipe materials, and other infrastructure components contributing to the distribution of potable water to the residents.Furthermore, the review article conducts a detailed analysis of hydraulic modeling techniques and software applications utilized in assessing the performance and optimization of the water distribution network. It scrutinizes factors influencing network efficiency, including water demand patterns, pressure management, leakage detection, and water loss reduction strategies.Through a critical evaluation of the existing challenges faced by the water distribution network in Jabalpur Cantonment, the review identifies issues such as aging infrastructure, inadequate maintenance, uneven distribution, non-revenue water, and insufficient pressure management.

A Review Article on Analysis and Design of Water Distribution Network for Jabalpur Cantonment Board Area

The Jal Jeevan Mission (JJM) stands as a transformative initiative aimed at ensuring accessible and potable water supply to every rural household across India. This review article delves into assessing the comprehensive impact of the JJM on rural water supply systems, with a primary focus on evaluating its assessment methodologies, distribution network designs, and analytical aspects.The assessment section scrutinizes the strategies employed under the JJM for gauging the water needs of rural communities. It examines the mission's effectiveness in identifying and addressing the diverse and dynamic requirements of different regions, considering factors such as population density, topography, and water quality.Furthermore, the design of the distribution network within the JJM framework is critically analyzed. This includes an exploration of the planning, engineering, and implementation methodologies used to establish robust and efficient water supply networks in rural areas. The article delves into the technological innovations, if any, adopted for optimizing water distribution, storage, and management.

Genetic Algorithm Based Combinatorial Optimization for the Optimal Design of Water Distribution Network of Gurudeniya Service Zone, Sri Lanka

This paper brings an in-detail Genetic Algorithm (GA) based combinatorial optimization method used for the optimal design of the water distribution network (WDN) of Gurudeniya Service Zone, Sri Lanka. GAmimics the survival of the fittest principle of nature to develop a search process. Methodology employs fuzzy combinations of pipe diameters to check their suitability to be considered as the cost-effective optimal design solutions. Furthermore, the hydraulic constraints were implicitly evaluated within the GA itself in its aim for reaching the global optimum solution. Upon analysis, the results of GA approach delivered satisfactory design outputs.

IoT Water Management and Distribution System for Smart City using Artificial Intelligence

Year after year, the accessible percentage of the world's freshwater resources decreases. According to a World Economic Forum assessment, increased water consumption will result in catastrophic global shortages during the next two decades. The majority of water loss occurs during distribution in pipes during shipment. A water distribution system" using the Internet of Things (IoT) combining "cloud and fog computing" is presented for water distribution and underground pipeline health monitoring to remove the loss. Consumer needs must be assessed to design an effective IoT-based water supply system. As a result, a deep learning method known as Long short-term memory (LSTM) is suggested to estimate customer water consumption. An integrated IoT "Water Distribution Network (WDN)" is being created by employing hydraulic engineering and more accurate demand forecasts. The WDN design will minimize transport losses while maintaining water quality for users. This will result in the creation of smart systems for distributing water. Finally, the suggested algorithms' performance is analyzed and compared. The results demonstrate that the water distribution system with intelligence can effectively monitor the network.

Selection of Real-Coded Genetic Algorithm parameters in solving simulation–optimization problems for the design of water distribution networks

Abstract The design of the water distribution network (WDN) is very difficult mainly due to the nonlinear relation between head and flow. The distribution network should also be cost-effective. In any simulation–optimization approach, the computational time requirement is very high for very complex problems. The optimization module plays a crucial role in reducing the computational time. This study aims to apply a simulation–optimization approach to designing a WDN. EPANET is selected as the simulation model and Real-Coded Genetic Algorithm (RCGA) is selected as the optimization module. To link the simulation module with the optimization module, a program is written in MATLAB. The developed simulation–optimization approach was applied to two benchmark network problems to check the suitability of the method. The parameters of the RCGA were optimized for the two networks. The computational efficiency of the developed simulation–optimization model is checked based on the number of function evaluations. For both networks, the number of function evaluations to get the optimum network design was less than the number of function evaluations required for other methods mentioned in the literature.

Optimisation of pumping and storage design through iterative hydraulic adjustment for minimum energy consumption

ABSTRACT Water distribution networks (WDNs) require large capital investment and ongoing operational costs, resulting in their optimisation being a highly researched field. Despite the benefits tanks bring to networks, most optimisation models omit them as decision variables due to the complexity they can introduce to heuristic approaches. This paper addresses the least-cost optimisation of WDN design and operation through the development and application of the Small-network Configurator for Optimising Pump Energy consumption (SCOPE). The SCOPE algorithm incorporates pipes, pumps and tanks as decision variables and solves the optimisation problem through an iterative approach that pairs EPANET simulation results with subsequent hydraulic calculations to converge on the pumping and storage configuration which yields the lowest energy consumption. The SCOPE output can then be used to seed further optimisation techniques. This approach has been tested on the synthetic Nametown and real-life Dili networks and demonstrated annual cost savings of 10% and 25%, respectively.

Optimal Design of Intermittent Water Distribution Network Considering Network Resilience and Equity in Water Supply

In urban areas of developing countries, due to industrialization and population growth, water demand has been increasing significantly, thereby increasing stress on the existing water distribution systems (WDSs). Under these circumstances, maintaining equity in the allocation of water becomes a significant challenge. When building an intermittent water distribution system, it is important to provide a minimum level of supply that is acceptable as well as water supply equity. A non-dominated sorting genetic algorithm (NSGA-II) is employed for the optimal design of an intermittent water distribution network (WDN). Network resilience is taken as a measure of reliability (In), while the uniformity coefficient (CU) is taken as a measure of equity in the water supply. Maximizing network resilience, uniformity coefficient, and minimization of cost of the network are considered as the objectives in the multi-objective optimization model. Pressure-driven analysis (PDA) is used for the hydraulic simulation of the network. The NSGA-II model is applied and demonstrated over two water distribution networks taken from the literature. The results indicate that reliability and equity in WDNs can be accomplished to a reasonable extent with minimal cost.

Optimization of Water Distribution Network Design Using Rafflesia Optimization Algorithm Based on Opposition-based Learning

<p>About 70% of the total cost of the water distribution system is used in the design of water distribution network (WDN), and selecting the most suitable pipe diameter for the WDN is the main way to reduce construction costs. The Rafflesia optimization algorithm (ROA) is a novel meta-heuristic algorithm, which was proposed recently. It has the characteristics of escaping local optimal solutions and stable performance. To further increase the solution quality and convergence speed of the algorithm, the opposition-based learning strategy is adopted in this paper to initialize the ROA algorithm population (namely the OBLROA algorithm). In this paper, the two-loop pipe network is taken as an actual test case, and the OBLROA algorithm is used to design the minimum cost pipe diameter combination. The experimental results show that the OBLROA algorithm can find the lowest cost pipe diameter combination of the two-loop pipe network under the constraints of pressure and velocity. Compared with some previous research work, the OBLROA algorithm needs the least number of evaluations to find the optimal solution, showing strong competitiveness.</p> <p> </p>

Application of Strength Pareto Evolutionary Algorithm II in Multi-Objective Water Supply Optimization Model Design for Mountainous Complex Terrain

Water distribution networks (WDN) model optimization is an important part of smart water systems to achieve optimal strategies. WDN optimization focuses on the nonlinearity of the discharge head loss equation, the availability of discrete properties of pipe sizes, and the conservation of constraints. Multi-objective evolutionary algorithms (MOEAs) have been proposed and successfully applied in the field of WDN design optimization. Previous studies have focused on comparing the optimization effects of algorithms in water distribution networks, ignoring the problems of unbalanced pressure distribution and water hammer at the nodes of the pipe network caused by the complex terrain in mountainous areas. In this paper, a multi-objective water supply optimization model that integrated cost, reliability, and water quality was established for a mountainous WDN in real engineering. The method of traversing the nodes to solve the water age was introduced to find a more scientific and practical water age solution model, with setting the weight function to evaluate the water age of the water supply model comprehensively. Strength Pareto Evolutionary Algorithm II (SPEA-II) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) were adopted to optimize the WDN design model in the mountainous complex terrain. The significance levels of the number of Pareto solutions (NOPS) and running time are 0.029 and 0.001, respectively, indicating that the two algorithms have significant differences. Compared to NSGA-II, SPEA-II has a better convergence rate and running time in multi-objective water supply optimization design. The solution set distribution of SPEA-II is more concentrated than that of NSGA-II, also the numerical value is better. The number of SPEA-II optimization schemes is larger and the scheme is more effective. Among them, the Pareto solution set of SPEA-II can obtain more desirable optimization results on cost, reliability index (RI) and water age. In summary, the study provides valuable information for decision makers in WDN with complex terrain.

Improving Multi-Objective Optimization Methods of Water Distribution Networks

Water distribution network design is a complex multi-objective optimization problem and multi-objective evolutionary algorithms (MOEAs) such as NSGA II have been widely used to solve this optimization problem. However, as networks get larger, NSGA II struggles to find the diverse and uniform solutions that are critical in multi-objective optimization. This research proposes an improved version of NSGA II that uses three new-generation methods to target different regions of the Pareto front and thus increase the number of solutions in critical regions. These methods include saving an archive, local search around extreme and uncrowded Pareto front, and local search around the knee area of the Pareto front. The improved NSGA II is tested on benchmark networks of different sizes and compared to the best-known Pareto front of the networks determined by MOEAs. The results show that the proposed algorithm outperforms the original NSGA II in terms of broadening the Pareto front solution range, increasing solution density, and discovering more non-dominated solutions. The improved NSGA II can find solutions that cover all parts of the Pareto front using a single algorithm without increasing computational effort.

Pareto-optimal design of water distribution networks: an improved graph theory-based approach

Abstract One of the main drawbacks of using evolutionary algorithms for the multi-objective design of water distribution networks (WDNs) is their computational inefficiency, particularly for large-scale problems. Recently, graph theory-based approaches (GTAs) have gained attention as they can help with the optimal WDN design (i.e., determining optimal diameters). This study aims to extend a GTA to further improve the quality of design solutions. The GTA design is based on a customized metric called ‘demand edge betweenness centrality’, which spatially distributes nodal demands through the weighted edges of a WDN graph and provides an estimation of water flows. Assigned edge weights can be constant (i.e., static) or modified iteratively (i.e., dynamic) during the design process, leading to different flow estimations and alternative design options. Three hydraulic-inspired dynamic weights are developed in this study to better reproduce hydraulic behavior and, consequently, find better solutions. Additionally, this work proposes a framework for the optimal design of multi-source WDNs and provides guidelines for obtaining near-optimal solutions in such networks. A comparative study between GTAs and evolutionary optimizations confirms the efficiency of the improved GTA in providing optimal/near-optimal solutions, especially for large WDNs, with a runtime reduction of up to seven orders of magnitude.

Multi‐Objective Optimization Models for the Design of Water Distribution Networks by Exploring Scenario‐Based Approaches

AbstractDemand in a water distribution network (WDN) is an aleatory variable, owing to the unpredictable behaviors of water users. Therefore, it is one of the main reasons for uncertainty in the design of this infrastructure. The increasing number of water demand data sets offers opportunities to improve the traditional deterministic design approaches of WDNs by combining statistical and optimization methods. Robust optimization (RO) takes demand uncertainty into account by studying solutions that perform well under any possible demand scenario, that is, any possible realizations of this variable in the lifetime of a WDN. The right choice of scenario is therefore essential to ensure the reliability of the designed network. This paper presents a statistical methodology for generating scenarios to be used to solve a robust design optimization problem. It involves three steps: (a) descriptive analytics of historical data to derive the marginal distributions of peak hour demand in each node of the WDN, (b) generation of a very large number of snapshots by stratified sampling from the correlated marginal distributions of nodal peak demand, (c) generation of the peak demand scenarios by reducing the number of snapshots. Two heuristic techniques are proposed to reduce the number of snapshots, and for each of them, two different numbers of scenarios are derived. Two multi‐objective RO models are solved: the first model includes cost minimization and a mean‐variance Generalized Resilience and Failure index maximization objectives, and the second one additionally considers the minimization of the maximum undelivered demand, formalized using a regret function.

Hybrid Optimization Algorithms of Firefly with GA and PSO for the Optimal Design of Water Distribution Networks

A novel two-hybrid optimization model of particle swarm optimization (FAPSO) and firefly algorithm with genetic algorithm (FAGA) are introduced to improve the performance of the conventional firefly algorithm for the least cost design of water distribution networks. The performance of the models is tested through application to three of the well-known benchmark networks available in the literature and also to the real case study of the El-Mostakbal City network, Ismailia, Egypt. The performance of the different algorithms was determined by evaluating the minimum, maximum, mean and standard deviation of costs, the function evaluation number, the consumed computational time for 1000 evaluations and the success rate calculated using the fuzzy logic concept for different optimal solutions slightly greater than the known optimal solution (by about 1.0% and 2.0%) were utilized for testing the convergence and search capabilities of the models. It was found that the FAGA model is superior to the standard firefly and FAPSO models in exploring the search space, exploiting the promising areas and convergence to the optimal solution and can be considered as a reasonable optimization technique for the management of water distribution networks.

Optimal Design of Water Distribution Network Using Improved Artificial Bee Colony Algorithm

Optimal Design of Water Distribution Network Using Improved Artificial Bee Colony Algorithm