Integrated performance assessment model for water distribution networks

Pressure reducing valves (PRV) are widely used to enhance the performance of water distribution networks. Recently, PRVs have tended towards more sophisticated devices to realize complex control strategies. The present work compares network performances achieved with different PRV control methodologies considering variable demand conditions. A widely analyzed water distribution network is modeled using EPANET and used to simulate the PRV operation. The results show the effectiveness of the different control strategies, highlighting the amount of saved water, the pressure deficit occurring in critical nodes, and the power dissipated by the valve that can be potentially recovered using pumps as turbines or other emerging energy recovery technologies.

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.

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.

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.

Ensuring a reliable water supply in the face of changing conditions and growing demand is a critical global challenge. Water distribution networks (WDNs) are essential infrastructure, but traditional modeling methods based on historical data often struggle to adapt in real-time and integrate new information. In order to lower model errors in WDN models, this study explores the use of a Data Assimilation (DA) method that makes use of the Ensemble Kalman Filter. The study explores the effectiveness of the DA method, and a novel Greedy Algorithm (GA) for optimizing the location of sensors. The study shows that the DA method improves the WDN model and the GA is able to successfully determine optimal sensor locations. It was observed that increasing the number of sensors in the WDN increased the effectiveness of the DA method. This study highlights the potential of data assimilation to improve WDN modeling. Water utilities can gain from more precise predictions, greater system performance, and efficient decision-making for the management and maintenance of water distribution networks by allowing models to adapt and improve dynamically

The goal of this article is to study the impact that pressure management in distribution systems has, on the quality of the supplied water in terms of its water age, using the water distribution network of Kos city, capital of Kos Island in Greece was used as the case study network. This was achieved through network simulation using the Watercad V8i software, followed by the division of the network in district metered areas (DMA) and the placement of pressure reducing valves (PRV) in the entering nodes of each DMA. Research aimed also to optimize DMAs’ borders using the water age as the optimization criterion, instead of maximum pressure reduction. Different scenarios were tested on the calibrated and validated hydraulic model of Kos city water distribution network.

Water scarcity has led to an increase in the extraction of fresh water from aquifers, dams and lakes in certain regions where water availability is low. It has created serious problems in overexploitation of ground and surface water resources. This issue has been intensified due to population growth and increases in energy and water demands in the industry, agriculture, and households. In this chapter, a mathematical model for energy and water distribution networks in a macroscopic system is proposed. This model considers that the water and electricity demands can be satisfied by the existing power plants in the region and the installation of new power-desalination plants. Also, the model considers that the water demand can be satisfied by supplying water from dams, rivers, and aquifers. The model considers a macroscopic system that involves several cities in a water-stressed region. It accounts for variations in water demands throughout the year, for domestic, agricultural, and industrial users. The model considers both installation costs and operating costs of the new power-desalination plants, the installation of new storage tanks, pumping, and piping costs. The results show attractive solutions, where interesting economic profits can be obtained as well as the potential recharge of aquifers can be achieved.

In this work, the local PCA approach is used as a statistical process control tool for drinking water distribution(DWD) systems to detect and isolate sensor faults. The multivariate statistical process monitoring task is carried out by learning a finite mixture model to describe the local statistical behavior in each cluster, followed by the determination of the local statistical confidence limits. The objective of a water distribution system is to convey treated water to consumers through a pressurized network pipe. The aim is diagnosing sensor faults in DWD. Experimental results using a model of an actual water distribution network illustrate the effectiveness of the proposed approach.

Optimal Operational Scheduling of Pumps to Improve the Performance of Water Distribution Networks

Probabilistic reliability measures for the performance of water distribution networks are developed and analytical methods for their computation explained. The paper begins with a review of reliability considerations and measures for water supply systems, making use of similar notions in other fields. It classifies reliability analyses according to the level of detail with which the water system is modeled, and then concentrates on methods relevant to networks. Two probabilistic measures, reachability (connection of a specific demand node to at least one source) and connectivity, are explored for use in water distribution systems. Two algorithms for their computation are presented, one for series‐parallel networks and one for general networks. These measures are computed for two systems, each with ten nodes. Additionally, the probability that a given point receives sufficient supply is proposed for use as a reliability measure. For the calculation of this measure, an algorithm is provided that combines a capacitated network algorithm with a method to efficiently search through network configurations involving multiple link failures. This measure is calculated for the two sample systems.

Smart meters for enhanced water supply network modelling and infrastructure planning

Computer models of water distribution networks are commonly used to simulate large systems under complex dynamic scenarios. These models normally use so-called demand-driven solvers, which determine the nodal pressures and pipe flow rates that correspond to specified nodal demands. This paper investigates the use of data parallel high performance computing (HPC) techniques to accelerate demand-driven hydraulic solvers. The sequential code of the solver implemented in the CWSNet library is analysed to understand which computational blocks contribute the most to the total computation time of a hydraulic simulation. The results obtained show that, contrary to popular belief, the linear solver is not the code block with the highest impact on the simulation time, but the pipe head loss computation. Two data parallel HPC techniques, single instruction multiple data (SIMD) operations and general purpose computation on graphics processing units (GPGPU), are used to accelerate the pipe head loss computation and linear algebra operations in new implementations of the hydraulic solver of CWSNet library. The results obtained on different network models show that the use of this techniques can improve significantly the performance of a demand-driven hydraulic solver.

This paper describes the proportional demand method and the target demand method, two techniques for adjusting estimated demands in hydraulic models of water distribution networks to produce solutions that are consistent with available supervisory control and data acquisition (SCADA) data. The two techniques assume that pipe resistances and SCADA data are accurate and that the combination of SCADA data and demand estimates produce overdetermined problems. Nodal demands are regarded as stochastic variables which fluctuate about an estimated mean value. The method of weighted least squares is used to obtain solutions that satisfy all of the constraints imposed by SCADA data with adjusted nodal demands that most closely resemble the estimates. The methods are intended for use in real-time modeling but are limited to quasi-steady state flow. The paper demonstrates the methods on two example problems.

Residential water demand is one of the most difficult parameters to determine when modelling drinking water distribution networks. It has been proven to be a stochastic process which can be characterised as a series of rectangular pulses having set intensity, duration and frequency. Such parameters can be determined using stochastic models such as the Neyman-Scott rectangular pulse model (NSRPM). NSRPM is based on resolving a non-linear optimisation problem involving theoretical moments of the synthetic demand series (equiprobable) and of the observed moments (field measurements) statistically establishing the measured demand series. NSRPM has been applied to generating local residential demand. However, this model has not been validated for a real distribution network with residential demand aggregation, or compared to traditional methods (which is dealt with here). This paper compares the results of synthetic stochastic demand series (calculated using NSRPM applied to determining pressure and flow rate) to results obtained using traditional simulation methods using the curve of hourly variation in demand and to actual pressure and flow rate measurements. The Humaya sector of Culiacan, Sinaloa, Mexico, was used as study area.

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.