Comment on dwes-2020-37

One of the ways to reduce the risk of contaminated water consumption is to optimally locate the quality sensors. These sensors warn users in the case of contamination detection. Analyzing the actual conditions of the contamination which enters the network is faced with many uncertainties. These uncertainties include the dose of contamination, time and location of its entry which have received less attention. Also, the uncertainty in the nodes' water demand causes changes in the distribution and contamination diffusion within the network. The main impetus of the present study is to determine the optimal quality sensor locations in the water distribution network in order to reduce the damage caused by contaminated water consumption prior to the contamination event detection. For this purpose, a parameter is defined as the maximum possible damage for calculating which the vulnerability and importance of the nodes have been considered in addition to the uncertainties in the location and time of the contamination entry. The importance of each node differs from that of other ones. Ranking the importance of the nodes is influenced by both land use and covered population ratio. In this study, six scenarios are defined for the contamination event in the water distribution network. These scenarios consider the effects of varying pollutant dose and the contamination input from nodes which are prone to its entry. Also, the NSGA-II has been utilized in order to minimize the damage with minimum number of sensors. The proposed model is evaluated on a real network in Iran. The results indicate that adding only one or two contamination warning sensors to the proposed locations can lead to the decreasing damage caused by the contaminated water consumption from 54 to 82%. According to the proposed method, the best answer for scenarios 1-6 was obtained for 7, 6, 6, 2, 2 and 2 sensors, respectively. The results showed that the slope of the pollution rate diagram does not change much from 6 sensors upwards in the first three scenarios, and from 4 sensors upwards in the second three scenarios. In scenarios 1-3, with 7, 6 and 6 sensors, respectively, in different nodes, the best placement is for 203-224 equivalent attack population, and in scenarios 4-6, with sensors in nodes 4 and 43, the best placement is for 225-279 equivalent attack population.

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.

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

In the first place, this paper presents an examination of the effects of missing cadastral information on the modeling and calibration of water distribution networks (WDN). For this purpose, cadastral errors were artificially introduced to the hydraulic models of the WDN of three cities of the province of Valle del Cauca (Colombia), with population in the range of 15,000–30,000 inhabitants. The cadastral errors were introduced by eliminating some pipes from the unaltered models using the resilience index as selection criterion. From this examination, typical responses observed during the calibration process were associated to specific topological and hydraulic errors and it was also noted that there is a direct relation between the resilience index and the effect of the removal of a certain pipe on the hydraulic performance of the model. In the second place, after identifying the effects of missing and erroneous cadastral and hydraulic information on the calibration of WDN models, the inverse procedure was carried out: the calibration process was used as a tool for detecting missing and erroneous cadastral and hydraulic information. This methodology was successfully applied to the WDN of Sector Estadio (250,000 inhabitants), which is a complex area of the city of Bucaramanga (Colombia); through the calibration of this WDN, errors such as failing water meters, pipes and valves that existed in the real system but were not in the model, significant errors in pipe diameters in the model, among others, were identified.

Energy Optimization of Supplied Flows from Multiple Pumping Stations in Water Distributions Networks

Digital twin-based resilience evaluation and intelligent strategies of smart urban water distribution networks for emergency management

Emerging economic focuses on the reliable infrastructure setups for growth. Developed countries also work in more realistic performance indicators for risk assessment of infrastructure. Thus, the reliability of water supply through the water distribution network is improving. Among the risk indicators, resilience is one of the important factors. Resilience of water distribution through pipe network is getting focus in the recent years. Hence, in this paper three important resilience indicators, namely ‘resilience index’, ‘network resilience index’ and ‘total surplus head index’ reported in the literature, are analyzed and the merits and demerits are highlighted. This analysis will be helpful for future research.

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

Bayesian Optimization is proposed for data-efficient learning of optimal control strategies aimed at minimizing the energy related costs for operating pumps in a water distribution network. The control strategies are defined as pressure-based rules, whose parameters are the decision variables of the optimization problem. A probabilistic description is used to model the optimization problem from parameters to energy cost. The probabilistic model is learned from data obtained by testing a set of parameters via software hydraulic simulation. Bayesian Optimization selects the next values of the parameters to evaluate in a principled way, proving to be able to find globally optimal control strategies, within relatively few trials (i.e., software simulation runs). The proposed Bayesian Optimization framework deals with a quite general formalization of the control problem, including constraints, also black box. Relevant results on a real-life water distribution network are reported, also in comparison with Pure Random Search and Genetic Algorithms.

A practical way of assessing the impact of various pipe failure conditions on water distribution networks is described. The method avoids the need to undertake a full network analysis or simulation for each particular component failure. The method assesses the vulnerability of the network to the loss of any particular pipe element, and provides a quantitative estimate of the impact on each nodal demand, and the postfailure utilization of nodal sources and pipe elements. Implementation of the method requires only knowledge of the network configuration and a set of typical operating conditions, such as might already be available from a routine network analysis of the intact distribution network. Detailed non‐linear network analysis covering all possible failure conditions is thus avoided. In cases of networks supplied by a number of sources of differing quality, the same overall technique can also be used to determine the chemical/biochemical quality of supply at any point in the network. The results of the method can be combined with pipe failure probabilities to provide measures of network reliability.

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

Water pressure management in a water distribution network (WDN) is a key component applied to achieve desirable water quality as well as a trouble-free operation of the network. This paper presents a hybrid, two-stage approach, to provide optimal separation of a WDN into District Metered Areas (DMAs), improving both water age and pressure. The first stage aims to divide the WDN into smaller areas via the Geometric Partitioning method, which is based on Recursive Coordinate Bisection (RCB). Subsequently, the Student’s t-mixture model (SMM) is applied to each area, providing an optimal placement of isolation valves and separating the network in DMAs. The model is evaluated on a realistic network generated through Watergems and is compared against one variation of it implemented, including the Gaussian Mixture Model (GMM) as well as the Genetic Algorithm (GA) approach, obtaining impressive performance. The implementation of both stages was deployed in a MATLAB environment through the Epanet toolkit. The proposed system is very promising, especially for large size WDNs due to the decreased running time and noteworthy reduction of pressure and water age.

Multi-objective meta-heuristics are used to optimize water distribution networks (WDNs) as they can achieve a near-optimal balance between cost and resilience in a unified platform. Most of these algorithms include tuning of algorithm-specific control parameters for higher optimization efficiency, leading to an increased computational effort. The current study is inspired by the desire to address the above problem. The goal is to formulate a multi-objective Rao algorithm (MORao) considering an existing modified resilience index (MRI) in the optimal design of WDNs. The model is demonstrated to attain Pareto-optimal solutions to complex WDN problems without exclusive parameter tuning. The algorithm is written in Python and is linked to a hydraulic model of a WDN implemented in EPANET 2.2 using pressure-driven demand (PDD) analysis. The method is demonstrated on three widely used networks: Two-loop, Goyang, and Fossolo. The Pareto-optimal solutions examine a tradeoff between two objectives to recognize competitive solutions. The network's resilience is increased 2.5 times by only 0.8 times increase in least-cost of Two-loop network. This research indicates that this method can achieve a satisfactory level of performance with a limited number of function evaluations.

In many localities with limitations on the availability of water, Water Distribution Networks (WDNs) supply water only for a few hours in a day. In such networks, inefficient operational policies can lead to inequitable supply of water. This work proposes a data-driven approach for scheduling the supply side of WDNs for equitable distribution of available water. We formulate and solve a scheduling problem that makes use of flow measurements from the real network rather than a hydraulic model of the WDN. This helps reduce the effort required for model development and the errors arising out of it. Further, to avoid making excessive measurements on the network, we propose a heuristic for choosing measurements that are informative. In each step of this iterative procedure, a schedule is prepared using the available measurements and based on this, a new system state that has to be measured is identified. The iteration is completed when the measurements are made, and the database is updated. We demonstrate the advantages of this technique through simulations of a real WDN and also experiments performed on a lab-scale network.

Environmental issues can cause changes in source water availability in water distribution networks (WDNs). Thus, an efficient connection between the source and consumers is important for securing water serviceability, which can generally be achieved by minimizing energy losses. In this study, a novel two-phase design (TPD) model is proposed to design an energy-efficient WDN by maximizing a hydraulic geodesic index (HGI), which is the weighted shortest path from the source to the demand node. Before applying the TPD model for WDN design, a correlation analysis between the system HGI, hydraulic performance, and graph theory indices is conducted using 33 J-City networks to verify the proposed HGI. Next, the TPD model is used to determine the optimal layout of the grid network (Phase I). Based on this layout, the optimal diameter set is identified in Phase II. The TPD is thereafter compared with the traditional single-phase design (SPD) model, which determines the optimal layout and diameter simultaneously, and a least-cost model for each phase in the grid network layout and pipe-sizing problem. The correlation analysis clearly indicates that the system HGI with the weighted graph theory successfully determines the hydraulic performance without any hydraulic analysis. Furthermore, TPD is advantageous for designing energy-efficient, hydraulically and structurally sustainable, and resilient networks, as compared to SPD and the least-cost model. The TPD model is expected to provide a better opportunity to prepare for extreme water availability changes by enhancing the hydraulic performance and efficiency through a better connection between the source and nodes.