Benchmark Water Distribution Networks Research Articles

Identifying Contaminant Intrusion in Water Distribution Networks under Water Flow and Sensor Report Time Uncertainties

Contamination events in water distribution networks (WDNs) could have severe health and economic consequences. Contaminants can be deliberately or accidentally introduced into the WDN. Quick identification of the injection location and time is important in devising a mitigation plan to prevent further spread of the contaminant in the network. A method of identifying the possible intrusion point in a given network and reporting data is to use an inverse calculation by backtracking the potential path of the contaminant in the network. However, there is an element of uncertainty in the data used for calculation, particularly in water flow and sensor report time. Given the uncertainties, a method was developed in this study for fast and accurate contaminant source identification. This paper proposes a comparison filter of results by first identifying potential contaminant locations through backtracking, followed by a forward calculation to determine the injection time range, thereby reducing the potential suspects and providing likeliness comparison among the suspects. The effectiveness of the proposed method was examined by applying it to a benchmark WDN. By simulating uncertainties and filtering through the results, several possible contaminant intrusion locations and times were identified.

Global performance of metaheuristic optimization tools for water distribution networks

Numerous metaheuristic optimization algorithms are used for optimal design of water distribution networks. Each algorithm shows dissimilar characteristics depending on the network properties and the sensitivity analysis of the algorithm control variables. New performance metrics of metaheuristic optimization methods are proposed using simple but robust refined metrics and were applied to the available literature data for different algorithms which have previously been used for three popular benchmark water distribution networks. In general, recent performance metrics are devoted to measure effectiveness, efficiency, and reliability in a separate manner, which made some confusion, which is the best?. In the present work, the proposed metrics are used to calculate both of best global and average global performance of different optimization algorithms. The results show that the present metrics have a good distinctive performance between different algorithms. The Fittest individual referenced Differential Evolution is found to be the best algorithm.

Investigating the Impacts of Water Conservation on Water Quality in Distribution Networks Using an Advection-Dispersion Transport Model

With the increasing adoption of demand management strategies and water conservation practices, domestic water consumption is projected to decline in the future. The subsequent consumer-side demand reductions are expected to result in increased residence times in water distribution networks (WDNs), and thus could have negative effects on the water quality (WQ) reaching the consumers’ taps. This study evaluates the impacts of the projected decrease in residential water demands on the deterioration of the WQ in WDNs. This deterioration will likely be most prominent in the dead-end branches at the perimeters of WDNs, where the flow is characteristically low and intermittent. The assessment of WQ deterioration in the dead-end branches requires the implementation of an accurate WQ simulation model. To this end, a new Python-based software package, WUDESIM_Py, is first introduced. The WQ simulation engine in WUDESIM_Py is based on an advection-dispersion-reaction model and accounts for the spatial distribution of water demands along dead-end pipes. WUDESIM_Py comprises various sets of functions that allow the users to set-up and conduct WQ simulations as well as obtain and visualize simulation results. A complete description of the different functions, together with examples of how these functions can be implemented in different applications, is provided. Through conducting extensive simulations of benchmark WDNs, the results revealed that widespread adoption of water conservation practices can lead to significant WQ deterioration in the dead-end branches. Additionally, the results suggested that neglecting dispersive transport and spatially aggregating demands may result in overestimating residual chlorine concentrations in the dead-end branches, which could mask the real impacts of demand reduction on WQ deterioration.

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.

Optimization Difficulty Indicator and Testing Framework for Water Distribution Network Complexity

In the last three decades, benchmark water distribution networks (WDNs) have provided a common testbed for new optimization algorithms and design approaches. However, deriving generalized and reliable conclusions from such benchmark WDNs is difficult because their optimization difficulty levels (ODLs) are either too low or too high (i.e., biased). Final solutions do not consistently converge to a global optimum for a WDN problem with a high ODL. In addition, little effort has been given to quantifying and comparing the ODLs of WDNs with different characteristics and conditions. In this study, an ODL indicator was developed for WDNs: the coefficient of variation of the final solution fitness values. An ODL quantification framework was also developed with two phases: (1) generating network layouts with various topological characteristics, and (2) quantifying the statistics of the final solution quality and ODL by using a global parallel genetic algorithm. The proposed indicator and framework were applied to the design of a dense-grid B-city network and large C network, and the results demonstrated their applicability to generating a WDN benchmark problem with the target ODL.

Design of water distribution systems using an intelligent simple benchmarking algorithm with respect to cost optimization and computational efficiency

Abstract The increasing stress on the water distribution network (WDN) considering demand satisfaction with minimum cost has inspired designers to apply various optimization techniques to meet the consequent challenges. The traditional way of using optimization methods, e.g. stochastic meta-heuristic algorithms, have come along with various constraints to explore an optimum solution. In this study, a newly developed meta-heuristic algorithm called the Simple Benchmarking Algorithm (SBA) is used to optimize pipe size. A modified approach with SBA having interfaces with the EPANET 2.0 hydraulic simulation model is used to compute the minimum cost of the two-loop network and the Hanoi benchmark WDN. Results show that SBA is more efficient in obtaining the least possible cost with fast convergence.

Towards serious gaming for water distribution networks sizing: a teaching experiment

Abstract Real-life engineering problems relate to different technical aspects to be considered at the same time. Traditional teaching techniques for engineering students (i.e., future decision-makers for such problems) sometimes need to be supplemented to convey this complexity, and thus innovative approaches are needed. A new and useful approach allowing a more intuitive understanding of real-life problems is serious gaming (SG), which combines a game environment and utility functions to address real problems. This paper describes a first attempt to use SG to help engineering students learn and deal with the complexities of designing water distribution networks given multiple objectives and uncertainty. This application of SG relates to five benchmark water distribution networks, and students were asked to find the optimal value of pipe diameters to minimize the capital cost of pipes. The results of the experiment show that students learn in less time how to design water distribution networks while enjoying the experience. Most students found the approach useful, claiming that the difficulty in approaching the pipe sizing problem decreased considerably as the practice of the game increases. The results of the experiment suggest that SG may have value in learning how to design other engineering systems.

Bayesian optimization of pump operations in water distribution systems

Bayesian optimization has become a widely used tool in the optimization and machine learning communities. It is suitable to problems as simulation/optimization and/or with an objective function computationally expensive to evaluate. Bayesian optimization is based on a surrogate probabilistic model of the objective whose mean and variance are sequentially updated using the observations and an “acquisition” function based on the model, which sets the next observation at the most “promising” point. The most used surrogate model is the Gaussian Process which is the basis of well-known Kriging algorithms. In this paper, the authors consider the pump scheduling optimization problem in a Water Distribution Network with both ON/OFF and variable speed pumps. In a global optimization model, accounting for time patterns of demand and energy price allows significant cost savings. Nonlinearities, and binary decisions in the case of ON/OFF pumps, make pump scheduling optimization computationally challenging, even for small Water Distribution Networks. The well-known EPANET simulator is used to compute the energy cost associated to a pump schedule and to verify that hydraulic constraints are not violated and demand is met. Two Bayesian Optimization approaches are proposed in this paper, where the surrogate model is based on a Gaussian Process and a Random Forest, respectively. Both approaches are tested with different acquisition functions on a set of test functions, a benchmark Water Distribution Network from the literature and a large-scale real-life Water Distribution Network in Milan, Italy.

Pressure Zoning Approach for Leak Detection in Water Distribution Systems Based on a Multi Objective Ant Colony Optimization

Leakages result in considerable loss of water in water pipe networks. Therefore it is an important issue to detect leakage amount and its approximate location. Leakages in water distribution system are directly related to the operating pressure. In the current study, a new model is proposed for leakage amount and location detection and it is applied into two benchmark water distribution networks. In the proposed method, the water distribution networks are divided into three pressure zones in order to consider the leakage differences in different operating pressures. Then, nodal pressures and demands are calibrated using a new multi objective ant colony based optimization model. In this method, leaks are simulated as extra nodal demands. For determining the nodes where leakage happens, a probability based scheme is used. The leakage occurrence probability varies depending on the pressure zone that each node is located. The results illustrate the applicability of the proposed model for detecting the leakages in water distribution systems.

Reliability-based design of Water Distribution Networks using Self-Adaptive Differential Evolution algorithm

This paper presents the application of Self-Adaptive Differential Evolution (SADE) algorithm for reliability-based design of Water Distribution Networks (WDNs). The algorithm is first applied and tested for deterministic design of various benchmark WDNs. Reliability-based design is then performed on the benchmark WDNs as well as real WDN in India by employing the SADE algorithm considering the uncertainty in nodal water demands. Monte Carlo simulation approach is used to incorporate the uncertainty in nodal demands; and pressure driven analysis is adopted for simulating the distribution of flows and pressures in pipe networks with the help of EPANET toolkit. A thorough comparison is made between the performance of SADE and DE methods. The results illustrate that SADE performs much better than DE since SADE converges faster with a higher success rate, making it a more preferable option for WDN design problem. The results of the study also illustrate that the optimal cost increases with increasing uncertainty level, but the percentage increase in cost is problem specific, which requires a robust tool for handling such complexities. The consistent results obtained by the application of SADE algorithm makes it a preferable option for reliability-based design of large WDNs including real world problems.

Non-Dominated Sorting Harmony Search Differential Evolution (NS-HS-DE): A Hybrid Algorithm for Multi-Objective Design of Water Distribution Networks

We developed a hybrid algorithm for multi-objective design of water distribution networks (WDNs) in the present study. The proposed algorithm combines the global search schemes of differential evolution (DE) with the local search capabilities of harmony search (HS) to enhance the search proficiency of evolutionary algorithms. This method was compared with other multi-objective evolutionary algorithms (MOEAs) including NSGA2, SPEA2, MOEA/D and extended versions of DE and HS combined with non-dominance criteria using several metrics. We tested the compared algorithms on four benchmark WDN design problems with two objective functions, (i) the minimization of cost and (ii) the maximization of resiliency as reliability measure. The results showed that the proposed hybrid method provided better optimal solutions and outperformed the other algorithms. It also exhibited significant improvement over previous MOEAs. The hybrid algorithm generated new optimal solutions for a case study that dominated the best-known Pareto-optimal solutions in the literature

Parameter Improvement of the Soccer League Competition Algorithm by Introducing Stubborn Players: Application to Water Distribution Network

A modified soccer league algorithm is presented in this paper. The effect of stubborn fixed players is investigated and the algorithm is implemented to three benchmark water distribution networks. The modified algorithm is compared to several algorithms. The results show that the modified algorithm performs better than the soccer league competition algorithm, in particular, on the average number of evaluations required to find the optimal cost. Computational results show that the utility benefit of both the individual player and team is essential. The algorithm becomes more reliable when utility benefits are high and as the number of fixed players increases.

Hydraulic Reliability Assessment and Optimal Rehabilitation/Upgrading Schedule for Water Distribution Systems

This paper develops an innovative approach to optimize a long-term rehabilitation and upgrading schedule (RUS) for a water distribution system with considering both hydraulic failure and mechanical performance failure circumstances. The proposed approach assesses hydraulic reliability dynamically and then optimizes the long-term RUS in sequence for a water distribution system. The uncertain hydraulic parameters are treated as random numbers in a stochastic hydraulic reliability assessment. The methodologies used for optimization in a stochastic environment are: Monte Carlo Simulation, EPANET Simulation, Genetic Algorithms, Shamir and Howard’s Exponential Model, Threshold Break Rate Model and Two-Stage Optimization Model. The proposed approach is conducted on a simulation model of water distribution network in a computer by two universal codes, namely the hydraulic reliability code and the optimal RUS code. The applicability of this approach is verified in an example of a benchmark water distribution network.

Assessment of metrics for resilient design of water distribution networks

Water distribution networks (WDNs) play a crucial role in the well-being of human populations and economic prosperity. It is essential that they cope with abnormal operating conditions and recover functionality quickly. Traditionally, WDNs are designed using cost and reliability objectives, but there is a lack of consensus on the definition and quantification of reliability which typically is a computationally intense process. Subsequently, various reliability-like metrics, called resilience indices, have been developed and demonstrated in the design of WDNs. Few studies exist that thoroughly evaluate the performance of the previously developed resilience metrics. This paper investigates three resilience metrics by evaluating their performance on three benchmark WDNs for several simulated mechanical failure states. The metrics studied are: (a) resilience index, (b) network resilience index (NRI), and (c) modified resilience index (MRI). The metric MRI performed better overall but NRI produced cheaper designs that performed better in the case of WDN-I. It is recommended that a better metric that incorporates different dimensions of resilience, such as robustness and redundancy, should be developed in the future.

General metrics for segmenting infrastructure networks

The classic modularity index for community detection in complex networks was recently tailored to water distribution networks (WDNs) and extended in order to be cut-position sensitive. Next, the WDN-oriented modularity index was enhanced in order to overcome the resolution limit of the classic modularity. Nonetheless, the modularity-based metrics developed so far allow the networks to be segmented into modules/segments that are similar to each other according to specific pipe characteristics (e.g., pipe lengths, distributed demand, background leakages, etc.). The present work extends and proves the strategy to overcome the resolution limits focusing on an infrastructure index that drives WDN segmentation toward modules that are internally similar with respect to given attributes (e.g., pipe diameters, average pipe pressures, average pipe elevations, etc.), since this aim is suitable for several practical purposes. The introduction of the attribute-based infrastructure index permits a comprehensive discussion and comparison of the metrics for infrastructure network segmentation through simple examples. Finally, the practical implications of increasing the resolution of internally similar modules are demonstrated on a well-known benchmark WDN considering various pipe attributes.

Sequence Analysis-based Hyper-heuristics for Water Distribution Network Optimisation

Hyper-heuristics operate at the level above traditional (meta-)heuristics that ‘optimise the optimiser’. These algorithms can combine low level heuristics to create bespoke algorithms for particular classes of problems. The lowlevel heuristics can be mutation operators or hill climbing algorithms and can include industry expertise. This paper investigates the use of a new hyper-heuristic basedon sequence analysis in the biosciences, to develop new optimisers that can outperform conventional evolutionary approaches. It demonstrates that the new algorithms develop high quality solutions on benchmark water distribution network optimisation problems efficiently, and can yield important information about the problem search space.

Quadratic approximations for pipe friction

This paper develops quadratic approximations to the classical models of Darcy–Weisbach and Hazen–Williams for frictional head loss in water pipes. Key elements of the technique are selecting the approximation domain and minimizing the relative error. A comparison for individual pipes over a range of diameter, roughness, and Reynolds number showed that the approximation provides accuracy consistent with the experimental error in the classical equations. Two benchmark water distribution networks are also considered. In these systems, flows and pressures computed using the approximation were consistent with simulations using the classical models. Applications of the approximation include mathematical optimization problems where polynomial expressions are desirable.

Optimization of Water Distribution Network: A Comparison using Genetic Algorithm and Particle Swarm Optimization

In this paper the authors compare two optimization techniques, namely the Genetic Algorithm and the Particle Swarm Optimization for water distribution networks. The effectiveness of the two algorithms is tested on two benchmark water distribution networks namely, the New York City water supply tunnel system and the Hanoi water distribution network and a comparison of the two techniques is presented. Furthermore the two algorithms are applied to two local water distribution networks in Mauritius which is very old and no such study has been done before on these data. This study will be beneficial to the government for decision making more specific for water management of the island following the severe drought problems during the last three years.

Reliability based optimal design of water distribution networks considering life cycle components

Water distribution systems play an important role in supplying water to consumers in a timely and efficient manner. The importance and complexity of such systems lead to extensive research in the area of optimal design of water distribution networks. Traditionally, only system costs are considered in design with few models incorporating environmental impacts. This paper presents a model for designing sustainable water distribution networks by minimising life cycle costs and life cycle CO2 emissions, while ensuring hydraulic reliability for the life time of the system. The model integrates a multi-objective genetic algorithm with water network simulation software, EPANET. A traditional benchmark water distribution network is used to demonstrate the model. Eight scenarios have been developed to test and validate the model for a variety of objectives with different constraints. Trade-offs between life cycle costs and life cycle emissions, along with hydraulic reliability of the system are illustrated.

Extended Period Simulation for Pressure-Deficient Water Distribution Network

A water distribution network is said to be under pressure-deficient condition when hydraulic-head or pressure-head at any of its nodes fall below the required level. The demands at these pressure-deficient nodes cannot be fully satisfied. To make the analysis simple, it is generally assumed that the demand is fully satisfied irrespective of the pressure-head available and this approach is referred as demand-driven analysis. For simplicity, demand-driven analysis received great attention in the past and several computer packages are readily available for the analysis of normal operational water distribution network. On the other hand, head-driven or pressure-driven analysis could be effectively used to analyze the pressure-deficient condition as these methods consider the head-discharge relationship at every node. This paper presents a pressure-deficient analysis algorithm using the widely used demand-driven analysis-based solver. The proposed algorithm is validated through benchmark water distribution network and then extended period simulation is carried out for a multiple source pumped network. The analysis shows that the proposed algorithm could find the partial nodal outflows and corresponding hydraulic-head values in a single hydraulic simulation run.