Large-scale Water Distribution Networks Research Articles

Hybrid particle swarm optimization and tabu search for the design of large-scale water distribution networks

ABSTRACT Water distribution network (WDN) optimization has received special attention from various technicians and researchers, mainly due to its high costs of implementation, operation and maintenance. However, the low computational efficiency of most developed algorithms makes them difficult to apply in large-scale WDN design problems. This article presents a hybrid particle swarm optimization and tabu search (H-PSOTS) algorithm for WDN design. Incorporating tabu search (TS) as a local improvement procedure enables the H-PSOTS algorithm to avoid local optima and show satisfactory performance. Pure particle swarm optimization (PSO) and H-PSOTS algorithms were applied to three benchmark networks proposed in the literature: the Balerma irrigation network, the ZJ network and the Rural network. The hybrid methodology obtained good results when seeking an optimal solution and revealed high computational performance, making it a new option for the optimal design of real water distribution networks.

A hybrid model-based method for leak detection in large scale water distribution networks

During the past decades, the problem of finding leaks in Water Distribution Networks (WDN) has been challenging. The quicker detection of leaks, on one hand prevents water loss and helps avoiding economic and environmental leakage consequences. On the other hand, increasing the speed of leak detection increases the false leak detection that imposes high costs. In this paper, we propose a fast hybrid method using AI algorithms and hydraulic relations for detecting and locating leaks and identifying the volume of losses material. The proposed method relies on simple and cost-effective flow sensors installed on each junction in the pipeline network. We demonstrate how influential features for leak detection would be generated by using hydraulic equations like Hazen–Williams, Darcy–Weisbach and pressure drop. Through exploiting Decision Tree, KNN, random forest, and Bayesian network we build predictive models and based on the pipeline topology, we locate leaks and their pressure. Comparing the results of applying the proposed method on various leak scenarios shows that the proposed method in this paper, outperforms other existing methods.

An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System

Water distribution is fundamental to modern society, and there are many associated challenges in the context of large metropolitan areas. A multi-domain approach is required for designing modern solutions for the existing infrastructure, including control and monitoring systems, data science and Machine Learning. Considering the large scale water distribution networks in metropolitan areas, machine and deep learning algorithms can provide improved adaptability for control applications. This paper presents a monitoring and control machine learning-based architecture for a smart water distribution system. Automated test scenarios and learning methods are proposed and designed to predict the network configuration for a modern implementation of a multiple model control supervisor with increased adaptability to changing operating conditions. The high-level processing and components for smart water distribution systems are supported by the smart meters, providing real-time data, push-based and decoupled software architectures and reactive programming.

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.

Economic nonlinear predictive control of water distribution networks based on surrogate modeling and automatic clustering

The operation of large-scale water distribution networks (WDNs) is a complex control task due to the size of the problem, the need to consider key operational, quality and safety-related constraints as well as because of the presence of uncertainties. An efficient operation of WDNs can lead to considerable reduction in the energy used to distribute the required amounts of water, leading to significant economic savings. Many model predictive control (MPC) schemes have been proposed in the literature to tackle this control problem. However, finding a control-oriented model that can be used in an optimization framework, which captures nonlinear behavior of the water network and is of a manageable size is a very important challenge faced in practice. We propose the use of a data-based automatic clustering method that clusters similar nodes of the network to reduce the model size and then learn a deep-learning based model of the clustered network. The learned model is used within an economic nonlinear MPC framework. The proposed method leads to a flexible scheme for economic robust nonlinear MPC of large WDNs that can be solved in real time, leads to significant energy savings and is robust to uncertain water demands. The potential of the proposed approach is illustrated by simulation results of a benchmark WDN model.

Multi-model based pressure optimization for large-scale water distribution networks

In the operation of urban water distribution networks (WDNs), the supply pressure to users should be kept low while meeting the lower bound to satisfy users’ water demand, reduce leakages and power consumption. In this paper, a multi-model based pressure optimization method for large-scale WDNs is proposed, where the pressure of the whole WDNs is represented by the pressure of key nodes and key pump station nodes based on the calculation of pressure transfer relationship and a developed network simplification method, the multiple linear models are constructed to approximate the pressure transfer relationships, and the models between the selected key nodes and key pump station nodes are used for the pressure optimization of the whole WDNs. The proposed methods are operation data based, which overcomes the difficulty in constructing accurate and complicated hydraulic models for large-scale WDNs. The network simplification reduces data requirements in multiple model construction and ensures real-time control. In the simulation case, which is part of Shanghai’s WDNs, the supply pressure to users is optimized to lower and more homogeneous value without violating the lower bound by applying the proposed approach, which is significant in reducing water leakages and energy consumption.

Pressure Management of Large-Scale Water Distribution Network Using Optimal Location and Valve Setting

This paper presents a new method to optimize the location and setting of Pressure Reducing Valves (PRVs) for adjusting the nodal pressure at different hours of a day by maximizing the reliability of Water Distribution Network (WDN). The methodology is based on Particle Swarm Optimization (PSO) algorithm that is written in the MATLAB code by linking to a hydraulic model of the WDN which is implemented in the EPANET. For verifying the proposed method, a sample network and a real large-scale WDN are applied. The results indicate that the determination of the optimal location and setting of PRVs in the WDN are effective in adjusting nodal pressures and reducing leakage rates. Comparison of the results for the cases with optimized PRVs and without PRVs in a real WDN showed that the average leakage rate is decreased about 15.0% and the average reliability index is increased about 24.8%.

Enhancing cooperative distributed model predictive control for the water distribution networks pressure optimization

In water distribution networks (WDNs), the supply pressure to users should be homogeneous and close to the lower bound so as to reduce leakages and energy consumption while meeting users’ water demand. The large-scale features and incomplete structure information of modern WDNs increase the difficulty in modeling and pressure optimization. In this paper, an enhancing cooperative distributed model predictive control (EC-DMPC) strategy is proposed to solve this problem with enhanced operating performance. Firstly, an efficient modeling method and a partitioning method based on operation data are proposed to decompose the WDNs and construct the subsystem models. These methods are based on k-Shape clustering and canonical correlation analysis with considering the mechanism of WDNs. In order to increase the flexibility of coordination while guaranteeing the global optimization performance, a neighbourhood optimization coordination strategy is adopted, where each subsystem considers the performance of itself and that of strongly affected subsystems. Moreover, a robust invariant set and a robust invariant set control law are utilized to handle the disturbance caused by the ignored weak couplings. Part of Shanghai’s WDNs is used as the study case to demonstrate the application of the proposed methods. A comparative experiment study is developed. Results have shown the efficiency of the proposed methods. By the coordination of the EC-DMPC strategy, when the water demand of users changes, the users’ pressure can be maintained homogeneous, and the average pressure decreases, which is beneficial in the reduction of leakages and energy consumption. The distributed strategy could also improve the flexibility and scalability for large-scale WDNs.

A Cooperative Co-Evolutionary Approach to Large-Scale Multisource Water Distribution Network Optimization

Potable water distribution networks (WDNs) are important infrastructures of modern cities. A good design of the network can not only reduce the construction expenditure but also provide reliable service. Nowadays, the scale of the WDN of a city grows dramatically along with the city expansion, which brings heavy pressure to its optimal design. In order to solve the large-scale WDN optimization problem, a cooperative co-evolutionary algorithm is proposed in this paper. First, an iterative trace-based decomposition method is specially designed by utilizing the information of water tracing to divide a large-scale network into small subnetworks. Since little domain knowledge is required, the decomposition method has great adaptability to multiform networks. Meanwhile, during optimization, the proposed algorithm can gradually refine the decomposition to make it more accurate. Second, a new fitness function is devised to handle the pressure constraint of the problem. The function transforms the constraint into a part of the objective to punish the infeasible solutions. Finally, a new suite of benchmark networks are created with both balanced and imbalanced cases. Experimental results on a widely used real network and the benchmark networks show that the proposed algorithm is promising.

Distributed zone MPC of pressure management for water distribution network systems

In the pressure management of large scale water distribution network (WDN), a distributed zone model predictive control (MPC) is proposed to keep the terminal water head in thedesired pressure range for satisfying the customer's demand, avoiding frequently operating of the actuator and reducing the correlation between subsystems. To ensure the existence of feasible solutions of constrained distributed zone MPC, a new reference trajectory of the tank level is introduced as an optimised variable. With the consideration of the desired pressure range constraints on the new reference trajectory and some physical constraints on the corresponding physical variables, a distributed zone MPC is presented to minimise the weighted sum of the three terms in the proposed performance index. To achieve the convergence of distributed zone MPC optimisation problem, an augmented Lagrangian formulation is applied to the distributed coordinated strategy. The proposed distributed zone MPC is applied to the WDN in the Shinan district of Shanghai, and the effectiveness of the method is illustrated.

Model Reduction and Outer Approximation for Optimizing the Placement of Control Valves in Complex Water Networks

The optimal placement and operation of pressure control valves in water distribution networks is a challenging engineering problem. When formulated in a mathematical optimization framework, this problem results in a nonconvex mixed integer nonlinear program (MINLP), which has combinatorial computational complexity. As a result, the considered MINLP becomes particularly difficult to solve for large-scale looped operational networks. We extend and combine network model reduction techniques with the proposed optimization framework in order to lower the computational burden and enable the optimal placement and operation of control valves in these complex water distribution networks. An outer approximation algorithm is used to solve the considered MINLPs on reduced hydraulic models. We demonstrate that the restriction of the considered optimization problem on a reduced hydraulic model is not equivalent to solving the original larger MINLP, and its solution is therefore sub-optimal. Consequently, we investigate the trade-off between reducing computational complexity and the potential sub-optimality of the solutions that can be controlled with a parameter of the model reduction routine. The efficacy of the proposed method is evaluated using two large scale water distribution network models.

Coordinated Decentralization-Based Optimization of Disinfectant Dosing in Large-Scale Water Distribution Networks

AbstractDisinfectants are typically used in water distribution networks (WDNs) to maintain the microbiological quality of potable water throughout the network. Disinfectant residual levels in such ...

A Stochastic Framework for Reliability and Sensitivity Analysis of Large Scale Water Distribution Networks

The hydraulic reliability and sensitivity analysis of large scale water distribution systems in presence of uncertainty is considered in this work. The assessment of the network reliability and sensitivity is performed by an efficient Markov chain Monte Carlo method, namely Subset simulation. Prescribed nodal heads of storage tanks, nodal demands and pipe roughness coefficients are modeled as uncertain parameters and described in a probabilistic manner. Failure is assumed to occur when the minimum nodal head in the network is lower than a minimum allowable value. The efficiency of the proposed method is demonstrated with the analysis of a real water distribution network consisting of a large number of nodes and pipes (of the order of thousands). The corresponding reliability problem represents a high dimensional problem. The approach gives an important insight into the performance, reliability and sensitivity of a class of complex utility networks.

Towards Achieving a Reliable Leakage Detection and Localization Algorithm for Application in Water Piping Networks: An Overview

Leakage detection and localization in pipelines has become an important aspect of water management systems. Since monitoring leakage in large-scale water distribution networks (WDNs) is a challenging task, the need to develop a reliable and robust leak detection and localization technique is essential for loss reduction in potable WDNs. In this paper, some of the existing techniques for water leakage detection are discussed and open research areas and challenges are highlighted. It is concluded that despite the numerous research efforts and advancement in leakage detection technologies, a large scope is still open for further research in this domain. One such area is the effective detection of background type leakages that have not been covered fully in the literature. The utilization of wireless sensor networks for leakage detection purposes, its technical challenges as well as some future research areas are also presented. In a general remark, practical application of these techniques for large-scale water distribution networks is still a major concern. In this paper, an overview of this important problem is addressed.

Efficient reduction of optimal disinfectant booster station placement formulations for security of large-scale water distribution networks

ABSTRACTIn response to a contamination incident in water distribution networks, disinfectant booster stations can be used to neutralize the contaminant and protect the public. In this paper, two mixed-integer linear programming formulations are proposed for optimal placement of disinfectant booster stations. The first formulation minimizes the contaminant mass consumed by the public, while the second formulation minimizes the number of people who ingest the contaminant above a mass threshold. The proposed formulations consider uncertainty in both the location and time of the contamination incident, resulting in an intractably large stochastic programming problem. This manuscript proposes a series of reductions that decrease the size of the problem by up to five orders of magnitude. The results show that the use of a small number of disinfectant booster stations can be very effective as a response strategy.

Decomposition based Multi Objective Evolutionary Algorithms for Design of Large-Scale Water Distribution Networks

In last two decades, multiobjective evolutionary algorithms (MOEAs) have shown their merit for solving different optimization problems within the context of water resources and environmental engineering. MOEAs mainly use the concept of Pareto dominance for obtaining the trade-off solutions considering different criteria. A new alternative method for solving multiobjective problems is multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses scalarizing the objective functions. In this paper, decomposition strategies are developed for the large-scale water distribution network (WDN) design problems by integrating the concepts of harmony search (HS) and genetic algorithm (GA) within the MOEA/D framework. The proposed algorithms are then compared with two well-known non-dominance based MOEAs: NSGA2 and SPEA2 across four different WDN design problems. Experimental results show that MOEA/D outperform the Pareto dominance methods in terms of both non-domination and diversity criteria. MOEA/D-HS in particular could provide very high quality solutions with a uniform distribution along the Pareto front preserving the diversity and dominating the solutions of the other algorithms. It suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated large-scale WDN design problems.

A Graph-Theoretic Framework for Assessing the Resilience of Sectorised Water Distribution Networks

Water utilities face a challenge in maintaining a good quality of service under a wide range of operational management and failure conditions. Tools for assessing the resilience of water distribution networks are therefore essential for both operational and maintenance optimization. In this paper, a novel graph-theoretic approach for the assessment of resilience for large scale water distribution networks is presented. This is of great importance for the management of large scale water distribution systems, most models containing up to hundreds of thousands of pipes and nodes. The proposed framework is mainly based on quantifying the redundancy and capacity of all possible routes from demand nodes to their supply sources. This approach works well with large network sizes since it does not rely on precise hydraulic simulations, which require complex calibration processes and computation, while remaining meaningful from a physical and a topological point of view. The proposal is also tailored for the analysis of sectorised networks through a novel multiscale method for analysing connectivity, which is successfully tested in operational utility network models made of more than 100,000 nodes and 110,000 pipes.

A MapReduce based Parallel Niche Genetic Algorithm for contaminant source identification in water distribution network

In recent years, water pollution incidents happen frequently, causing serious disasters and society impact. It is advocated that water quality monitoring sensors shall be deployed in water distribution network to enable real-time pollution detection such that we can effectively detect the water pollution event to reduce the risk. Besides event detection, it is also important to identify the contaminant source for depollution actions. But how to use the information derived from the monitoring sensors to identify the contaminant source is a non-trivial task. Contamination source identification problem is characterized by its extremely high computation complexity, uncertainty and non-uniqueness of the solution in a large-scale water distribution network with dynamic water demands. To tackle this issue, we develop a MapReduce based Parallel Niche Genetic Algorithm (MR-PNGA) that is not only able to achieve high identification accuracy but also to explore the cloud resources for performance improvement. The accuracy and efficiency of MR-PNGA is extensively validated on an 8-server cluster.

Efficient Preconditioned Iterative Methods for Hydraulic Simulation of Large Scale Water Distribution Networks

In this paper, we consider the use of an efficient null space algorithm for hydraulic analysis that employs preconditioned conjugate gradient (PCG) methods for solving the Newton linear equations. Since large water network models are inherently badly conditioned, a Jacobian regularization is employed to improve the condition number to some degree, this resulting in an inexact Newton method whose analyses is presented. Based on this analysis, constraint preconditioners are used to improve the condition number further for more efficient use of CG solvers. Operational networks are used to study the computational properties of the various approaches.

Preface: CCWI 2013

Since 1991 “Computing and Control for the Water Industry – CCWI” is a well-established series of bi-annual meetings. On 2–4 September 2013, the 12th edition (CCWI2013) was held in Perugia (Italy) and was focused on Information for Water Systems and Smart Cities. CCWI2013 emphasized the integration between the more conventional themes of the analysis and planning of water systems and information technology. Such an integration offers opportunities for design solutions and innovative models for the challenging problems of water system management in an urban perspective, a key concept for smart cities (Brunone and Giustolisi, 2014). The large number of presentations – more than 200 with many young researchers from Europe and overseas – confirmed the interest for CCWI2013 themes and the increasing success of CCWI Conferences (Fig. 1). This CCWI2013 special issue contains a selection of extended CCWI2013 papers which give a taste of the Proceedings of the Conference published on Procedia Engineering Elsevier (www.sciencedirect.com/science/journal/ 18777058/70). In fact their topics range from sustainabilitybased indicators of the urban water systems (Behzadian et al., 2014), design and control parameters in water treatment (Ramphal and Sibiya, 2014), numerical simulation of the transition between surface and pressurized flow (Fernandez-Pato and Garcia-Navarro, 2014) to optimisation of pump and valve schedules in complex large-scale water distribution networks (Skworcow et al., 2014), and a real data-based model to simulate the behavior of a pumping station equipped with variable speed pumps (Capponi et al., 2014). Figure 1. Last nine editions of CCWI conferences: number of papers published in the proceedings (from Brunone and Giustolisi, 2014).