Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks

Leaks are present to some extent in all water-distribution systems. This paper proposes a leakage localisation method based on the pressure measurements and pressure sensitivity analysis of nodes in a network. The sensitivity analysis using analytical tools is not a trivial job in a real network because of the huge non-explicit non-linear systems of equations that describe its dynamics. Simulations of the network in the presence and the absence of leakage may provide an approximation of this sensitivity. This matrix is binarised using a threshold independent of the node. The binary matrix is assumed as a signature matrix for leakages. However, there is a trade-off between the resolution of the leakage isolation procedure and the number of available pressure sensors. In order to maximise the isolability with a reasonable number of sensors, an optimal sensor placement methodology, based on genetic algorithms, is also proposed. These methodologies have been applied to the Barcelona Network using PICCOLO simulator. The sensor placement and the leakage detection and localisation methodologies are applied to several district management areas (DMA) in simulation and in reality.     

Inline wireless mobile sensors and fog nodes placement for leakage detection in water distribution systems

Burst or leakage in drinkable water distribution system has occurred frequently in recent years, causing severe damages, economic loss, and long-lasting society impact. A viable solution is to use agile inline mobile sensors to detect and so as to mitigate the burst or leakage. Distinguishing from online fixed sensors, mobile sensors can swim freely along the piles in water distribution network, thus giving a more precise detection. To combat the low power, low computation, and low communication capability of mobile sensors, the newly emerged fog computing provides a promising means to gather and preprocess the sensing data. In practice, due to the budget limitation, we can deploy a limited number of sensors and fog nodes in the system. This introduces a challenging problem on how to deploy them in the system, ie, sensor and fog node placement. We first formulate mobile sensor placement (MSP) as a path cover problem and prove it as NP-complete, and then we propose a customized genetic algorithm and a mixed greedy algorithm to solve MSP and fog node placement, respectively. The correctness and efficiency of the proposed algorithm are illustrated by a comprehensive experiment. Moreover, some critical factors, eg, sensor battery lifetime and movement pattern, are all extensively investigated and the results show the coverage ratio is sensitive to these factors.     

Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network

Water distribution networks are large complex systems affected by leaks, which often entail high costs and may severely jeopardise the overall water distribution performance. Successful leak location is paramount in order to minimize the impact of these leaks when occurring. Sensor placement is a key issue in the leak location process, since the overall performance and success of this process highly depends on the choice of the sensors gathering data from the network. Common problems when isolating leaks in large scale highly gridded real water distribution networks include leak mislabelling and the obtention of large number of possible leak locations. This is due to similarity of leak effect in the measurements, which may be caused by topological issues and led to incomplete coverage of the whole network. The sensor placement strategy may minimize these undesired effects by setting the sensor placement optimisation problem with the appropriate assumptions (e.g. geographically cluster alike leak behaviors) and by taking into account real aspects of the practical application, such as the acceptable leak location distance. In this paper, a sensor placement methodology considering these aspects and a general sensor distribution assessment method for leak diagnosis in water distribution systems is presented and exemplified with a small illustrative case study. Finally, the proposed method is applied to two real District Metered Areas (DMAs) located within the Barcelona water distribution network.     

Learning-based tuning of supervisory model predictive control for drinking water networks

This paper presents a constrained Model Predictive Control (MPC) strategy enriched with soft-control techniques as neural networks and fuzzy logic, to incorporate self-tuning capabilities and reliability aspects for the management of drinking water networks (DWNs). The control system architecture consists in a multilayer controller with three hierarchical layers: learning and planning layer, supervision and adaptation layer, and feedback control layer. Results of applying the proposed approach to the Barcelona DWN show that the quasi-explicit nature of the proposed adaptive predictive controller leads to improve the computational time, especially when the complexity of the problem structure can vary while tuning the receding horizons.     

Mobile sensor networks for optimal leak and backflow detection and localization in municipal water networks

Leak and backflow detections are essential aspects of Water Distribution Systems (WDSs) monitoring and are commonly fulfilled using approaches that are based on static sensor networks and point measurements. Alternatively, we propose a mobile, wireless sensor network solution composed of mobile sensor nodes that travel freely inside the pipes with the water flow, collect and transmit measurements in near-realtime (called sensors) and static access points (called beacons). This study complements the tremendous progress in mobile sensor technology. We formulate the sensor and beacon optimal placement task as a Mixed Integer Nonlinear Programming (MINLP) problem to maximize localization accuracy with budget constraint. Given the high time complexity of MINLP formulation, we propose a disjoint scheme that follows the strategy of splitting the sensor and beacon placement problems and determining the respective number of sensors and beacons by exhaustive search in linear time.     

Determination of optimal actuator forces and positions in smart structures using adjoint method

The problem of optimal design of structures with active support is analyzed in the paper. The sensitivity expressions with respect to the generalized force and the position of actuator are derived by the adjoint structure approach. Next, the optimality conditions are formulated by means of an introduced Lagrangian function. The problem of introduction of a new actuator is also considered and the condition of modification is expressed by means of the topological derivative. The obtained sensitivity formula, optimality conditions and modification conditions are applied in the optimization algorithm with respect to the number, positions and generalized forces of the actuators. Numerical examples of optimal control of beams illustrate the procedure proposed in the paper.     

里下河水网地区饮用水源地污染特征及控制研究

江苏里下河地区是典型的"锅底洼"平原洼地,在主汛期受点、面源汇入影响,水环境质量常迅速下降,溶解氧低至1.2 mg/L,高锰酸盐指数、氨氮、总磷分别高达12,2.7,0.634 mg/L,饮用水源地水质指数介于32.9～71.8之间,水质等级为3～4级,大部分属于已受到污染水质。针对饮用水源地容易出现水质持续超标现象,分析其成因,主要为地势低洼、河流比降小、水流极为缓慢且流向往返不定、水体循环条件差、自净能力弱、清污混杂,围绕提高饮用水源地安全保障程度,提出针对性的对策措施。     

An integrated planning tool for design of recycled water distribution networks

Pipeline design of urban recycled water networks involves thousands of decisions to ensure delivery of water to multiple use locations with pipelines and pump stations correctly located, optimally sized, and compatible with existing infrastructure. Here, we introduce PRODOT, Pipeline ROuting and Design Optimization Tool, software that identifies near-minimum-cost pipeline routes; accounts for existing configurations, legal, environmental or safety concerns, and trade-offs in pipeline length, pipe installation methods, traffic congestion during construction; optimizes pump station locations, pumping energy, pipe diameters and pressure classes; and includes theoretical additional capacity of each pipe, facilitating future expansion. We illustrate the utility of PRODOT with a case study for a local utility comparing PRODOT-generated configurations to a configuration proposed by an experienced consulting firm. The comparison shows that PRODOT produces pipeline configurations similar to the consulting firm's proposal with improvements by effectively and more broadly incorporating options the consultant may not have considered.     

Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager

In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll-a (chl-a) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl-a concentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl-a concentrations in lakes (R² value of 0.66; root mean square error value of 8.9 µg l^?1). Using this model, we forecast the spatial and temporal variability of chl-a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.     

A software framework for assessing the resilience of drinking water systems to disasters with an example earthquake case study

Water utilities are vulnerable to a wide variety of human-caused and natural disasters. The Water Network Tool for Resilience (WNTR) is a new open source Python™ package designed to help water utilities investigate resilience of water distribution systems to hazards and evaluate resilience-enhancing actions. In this paper, the WNTR modeling framework is presented and a case study is described that uses WNTR to simulate the effects of an earthquake on a water distribution system. The case study illustrates that the severity of damage is not only a function of system integrity and earthquake magnitude, but also of the available resources and repair strategies used to return the system to normal operating conditions. While earthquakes are particularly concerning since buried water distribution pipelines are highly susceptible to damage, the software framework can be applied to other types of hazards, including power outages and contamination incidents.     

A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments

Excessive pollutant discharge from multi-pollution resources can lead to a rise in downriver contaminant concentration in river segments. A multi-pollution source water quality model (MPSWQM) was integrated with Bayesian statistics to develop a robust method for supporting load (I) reduction and effective water quality management in the Harbin City Reach of the Songhua River system in northeastern China. The monthly water quality data observed during the period 2005–2010 was analyzed and compared, using ammonia as the study variable. The decay rate (k) was considered a key factor in the MPSWQM, and the distribution curve of k was estimated for the whole year. The distribution curves indicated small differences between the marginal distribution of k of each period and that water quality management strategies can be designed seasonally. From the curves, decision makers could pick up key posterior values of k in each month to attain the water quality goal at any specified time. Such flexibility is an effective way to improve the robustness of water quality management. For understanding the potential collinearity of k and I, a sensitivity test of k for I_2i (loadings in segment 2 of the study river) was done under certain water quality goals. It indicated that the posterior distributions of I_2i show seasonal variation and are sensitive to the marginal posteriors of k. Thus, the seasonal posteriors of k were selected according to the marginal distributions and used to estimate I_2i in next water quality management. All kinds of pollutant sources, including polluted branches, point and non-point source, can be identified for multiple scenarios. The analysis enables decision makers to assess the influence of each loading and how best to manage water quality targets in each period. Decision makers can also visualize potential load reductions under different water quality goals. The results show that the proposed method is robust for management of multi-pollutant loadings under different water quality goals to help ensure that the water quality of river segments meets targeted goals.     

A new approach for instantaneous pole placement with recurrent neural networks and its application in control of nonlinear time-varying systems

In this paper, we first show that online computation of feedback gain used for pole placement of nonlinear systems in recent years is not reliable, and then we present a new approach for instantaneous pole placement and apply it with dynamical recurrent neural networks for online computation of feedback gain. Because of high-speed convergence of neural network to feedback gain, we can apply this method for pole placement of nonlinear time-varying systems. One strategy for realization of this method is instantaneous linearization, as we do here by simulation. The advantage of the proposed method is a global uniform asymptotical exponential stability (GUAES) of closed-loop system around the equilibrium point.     

Optimum placement of active power conditioner in distribution systems using improved discrete firefly algorithm for power quality enhancement

This paper presents an improved solution for optimal placement and sizing of active power conditioner (APC) to enhance power quality in distribution systems using the improved discrete firefly algorithm (IDFA). A multi-objective optimization problem is formulated to improve voltage profile, minimize voltage total harmonic distortion and minimize total investment cost. The performance of the proposed algorithm is validated on the IEEE 16- and 69-bus test systems using the Matlab software. The obtained results are compared with the conventional discrete firefly algorithm, genetic algorithm and discrete particle swarm optimization. The comparison of results showed that the proposed IDFA is the most effective method among others in determining optimum location and size of APC in distribution systems.     

Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling

The application of Artificial Neural Networks (ANNs) in the field of environmental and water resources modelling has become increasingly popular since early 1990s. Despite the recognition of the need for a consistent approach to the development of ANN models and the importance of providing adequate details of the model development process, there is no systematic protocol for the development and documentation of ANN models. In order to address this shortcoming, such a protocol is introduced in this paper. In addition, the protocol is used to critically review the quality of the ANN model development and reporting processes employed in 81 journal papers since 2000 in which ANNs have been used for drinking water quality modelling. The results show that model architecture selection is the best implemented step, while greater focus should be given to input selection considering input independence and model validation considering replicative and structural validity.     

Application of a fractional step method for the numerical solution of the shallow water waves in a rotating rectangular basin

A numerical method is applied to the problem of an incompressible fluid in a slowly rotating rectangular basin for the simulation of wave propagation in shallow water. The present work is a complete study of the wave motion through evaluation of the wave height and the velocity components. The results are found by the application of a fractional step method and illustrated graphically. The technique is applied by splitting the shallow water equations and successive integration in every direction along the characteristics using the Riemann invariants associated with cubic spline interpolation. It has the advantage of reducing the multidimensional matrix inversion problem into an equivalent one-dimensional problem. Numerical results are represented in three dimensions for the velocity components at different times. The distribution of temperature and concentration are also calculated and plotted.     

Improvement of numerical instabilities in topology optimization using the SLP method

In this paper, we present a method for preventing numerical instabilities such as checkerboards, mesh-dependencies and local minima occurring in the topology optimization which is formulated by the homogenization design method and in which the SLP method is used as optimizer. In the present method, a function based on the concept of gravity (which we named “the gravity control function”) is added to the objective function. The density distribution of the topology is concentrated by maximizing this function, and as a result, checkerboards and intermediate densities are eliminated. Some techniques are introduced in the optimization procedure for preventing the local minima. The validity of the present method is demonstrated by numerical examples of both the short cantilever beam and the MBB beam. Received February 23, 1999     

Performance comparison of reduced models for leak detection in water distribution networks

This paper presents a methodology for comparing the performance of model-reduction strategies to be used with a diagnostic methodology for leak detection in water distribution networks. The goal is to find reduction strategies that are suitable for error-domain model falsification, a model based data interpretation methodology. Twelve reduction strategies are derived from five strategy categories. Categories differ according to the manner in which nodes are selected for deletion. A node is selected for deletion according to: (1) the diameter of the pipes; (2) the number of pipes linked to a node; (3) the angle of the pipes in the case of two-pipe nodes; (4) the distribution of the water demand; and, (5) a pair-wise combination of some categories.The methodology is illustrated using part of a real network. Performance is evaluated first by judging the equivalency of the reduced network with the initial network (before the application of any reduction procedure) and secondly, by assessing the compatibility with the diagnostic methodology. The results show that for each reduction strategy the equivalency of networks is verified. Computational time can be reduced to less than 20% of the non-reduced network in the best case. Results of diagnostic performance show that the performance decreases when using reduced networks. The reduction strategy with the best diagnostic performance is that based on the angle of two-pipe nodes, with an angle threshold of 165°. In addition, the sensitivity of the performance of the reduced networks to variation in leak intensity is evaluated. Results show that the reduction strategies where the number of nodes is significantly reduced are the most sensitive.Finally this paper describes a Pareto analysis that is used to select the reduction strategy that is a good compromise between reduction of computational time and performance of the diagnosis. In this context, the extension strategy is the most attractive.     

A better estimation of wave arrival time in water distribution networks using WAvelet kNEe (WANE)

A transient pressure wave is a sudden pressure change that occurs in a short time, which can be induced by sudden changes in valve and pump operation, and pipe bursts in a Water Distribution Network (WDN). An accurate estimation of a transient wave arrival time is crucial because it facilitates pipe condition assessment, hydraulic model calibration, and accurate localization of pipe burst events. Due to the noisy and highly fluctuating nature of the pressure signals, estimating an accurate transient pressure wave arrival time is not a trivial task. Among many methodologies proposed for detecting abrupt pressure changes, Discrete Wavelet Transform (DWT) and Cumulative Sum (CUSUM) were the two most popular approaches. However, several limitations involved with these two approaches can easily lead to unsatisfactory results. Moreover, some of the existing methodologies were only tested on either a single pipeline, engineered events, or a small sample size of events, making these methodologies suitable and accurate only for a limited number of scenarios. Driven by these limitations, a novel approach is proposed to estimate the wave arrival time in water distribution networks (WDNs). The backbone of this approach is the integration of wavelet decomposition and a knee point detection algorithm, thus gaining the name WAvelet kNEe (WANE). Through a comparative study against the other methodologies using 90 recorded transient events detected in a real WDN, WANE is found to provide the best wave arrival time estimation, with a Root Mean Square Error (RMSE) of 0.4 s. Based on the result, our estimation error is at least 15 s lesser than the other methodologies. With an improved wave arrival time estimation, WANE has the potential to minimize the response time of repair crews, service disruption time, as well as the associated water losses due to a pipe break.     

循环冷却水系统成垢过程软件预测

循环冷却水系统中无机垢的形成预测是一个相当复杂的研究课题,涉及到包括热力学、动力学、流体力学、晶体生长和表面化学等方面的知识。到目前为止,人们已经提出许多理论来试图解决此难题。但不足在于这些理论对成垢过程影响因素的讨论很不全面。本文几乎考虑了冷却水中常见的所有成垢阳、阴离子和非成垢离子,详细讨论了水溶液中所存在的各种平衡,并考察了温度、pH值、离子强度等因素对成垢过程的影响。所建立的模型通过实验数据进行了验证。结果显示,模型的计算结果与实验数据大体一致。利用Visual Basic6．0语言开发循环冷却水系统成垢预测专家系统软件。该软件可在windows 9X操作平台下独立运行,具有界面友好、操作方便、运行可靠稳定等优点。     

A method for diagnosis of current faults in antenna arrays using neural networks

A method for detection of faulty elements in antenna arrays from far‐field radiation pattern is presented. The proposed technique finds variation of current from correct values in the faulty elements. A step wise approach is proposed to determine magnitude and phase of current excitation and location of faulty element using neural networks. The results with radial basis function neural network and probabilistic neural network are compared. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.