A new method for identifying a leaking pipe within a pressurized water distribution system is presented. This novel approach utilizes transient modeling to analyze water networks. Urban water supply networks are important infrastructure that ensures the daily water consumption of urban residents and industrial sites. The aging and deterioration of drinking water mains is the cause of frequent burst pipes, thus making the detection and localization of these bursts a top priority for water distribution companies. Here we describe a novel method based on transient modeling of the water network and produces high-resolution pressure response under various scenarios. Analyzing this data allows the prediction of the leaking pipe. The transient pressure data is classified as leaking pipes or no leak clusters using the K-nearest neighbors (K-NN) algorithm. The transient model requires a massive computation effort to simulate the network’s performance. The classification model presented good performance with an overall accuracy of 0.9 for the basic scenarios. The lowest accuracy was obtained for interpolated scenarios the model had not been trained on; in this case, the accuracy was 0.52.
The operation of water distribution systems (WDS) is an energy-intensive process, which is subject to constraints such as consumer demands, water quality, and pressure domains. As such, tracing an operation policy in which constraints are met while energy costs are minimized, is a foremost objective for water utilities. Given the inherent uncertainties in WDS operation and the importance of supply continuity, it is essential to find an operational strategy that is robust against a wide range of circumstances. One promising approach for optimization under uncertainty is robust optimization (RO), which assures a robust (feasible) solution to realizations of the uncertain parameters, within predefined bounds. This study presents an RO-based method for optimizing pump scheduling under uncertainties of consumer demands and pumping costs. The method can capture various types of correlations between the uncertain parameters, thus better reflecting the uncertain nature of WDS operation. The developed methodology is demonstrated in two case studies with different levels of complexity. The impacts of uncertainty levels and correlation coefficients are analyzed to demonstrate their implications on operation policy. The results show the advantages of using RO with tradeoffs between costs and constraints satisfaction.
<p>In many studies arises the need to generate synthetic data sets. Such data can answer different needs as data imputation, non-stationary systems analysis, Monte Carlo simulations, training of data-driven models, uncertainty analysis and more. Previous efforts to generate synthetic data focused mostly on statistical methods which did not maintain the statistical moments of the original dataset, while producing a large number of random different time series. Here, a novel method is developed, based on signal processing and discrete Fourier transform (DFT) theory. The method allows to generate synthetic time series signals with similar statistical moments of any given signal. Moreover, the method allows control on the correlation level between the original and the synthesized signals. We also provide mathematical proofs that our method maintains the first two statistical moments. The method is illustrated on two different datasets showing that also the third and fourth moments are kept. Figure 1 shows, in blue, a true water demand time-series taken from a real-life system. For this signal, 50 synthesized signals are generated with increasing correlation levels - from top, with the lowest correlation, to bottom, presenting the highest correlations between the original and synthesized signals.<br><br>&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.01b08ce21fd164239002461/sdaolpUECMynit/22UGE&app=m&a=0&c=b5eaf620402d74bd0553183f2ed143c4&ct=x&pn=gnp.elif&d=1" alt="">&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160;&#160;</p><p><strong>&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; Figure 1</strong> &#8211; Domestic water demand signal with different correlation level</p>
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