Guaranteed Conservative Fixed Width Confidence Intervals via Monte Carlo Sampling

The influence of stochastic water demand on model-based leak localizationGlobally, water demand is rising and resources are diminishing. In the context of climate change and a growing world population, a further increase in water scarcity seems inevitable. Aiming towards a sustainable future, water should be used as efficient as possible by minimizing water losses, which can be higher than 50% in some drinking water networks.3 To minimize losses it is crucial to detect, localize and repair leaks as soon as possible.Leaks cause changes in flow and pressure. By monitoring the network with pressure and flow sensors and coupling these measurements with hydraulic computer models, leaks can be detected and located. The success of this so-called model-based leak localization depends heavily on our knowledge of water demand, since every water consumption affects the pressure and flow in the network as well. Nowadays, demand is modelled based on water billing information and the network&#8217;s inflow. This study proposes a new strategy by modelling stochastic demands. Realistic residential demands are generated in high spatial and temporal resolution based on Dutch water use statistics with SIMDEUM4. Subsequently, the stochastic demands are used within hydraulic simulations. The influence of demand fluctuations on pressure in the system is analyzed using Monte-Carlo sampling and the corresponding effects on model-based leak detection and localization are investigated.The proposed method is applied on a real Dutch water distribution network, containing inflow and six pressure measurements. Statistical information like the number of residents, households and annual billing information in the area is known. The corresponding hydraulic model is calibrated on pipe roughness by minimizing the mean squared error of the modelled and measured pressure at the sensor locations. Pressure driven simulations are performed and the resulting pressure changes at the sensors are simulated. Through the stochastic simulations in combination with Monte-Carlo sampling, confidence intervals for pressure changes at the sensor locations are determined and compared with the real measurements. The performance of leak detectability and localization is subsequently examined. &#160;This study shows that stochastic water demand simulations provide a better understanding on the reliability of model-based leak localization. By using these simulations, confidence intervals of demand related pressure changes at the sensor locations can be determined which affect the performance of leak detectability and localization under the variability of water demand. A better grip on the reliability of leak localization yields in a more efficient quest for leaks.&#160;3EurEau 2017, Europe&#8217;s water in figures, an overview of European drinking water and waste water sectors, The European Federation of National Associations of Water Services4Blokker, E. J. M. (2010), Stochastic water demand modelling for a better understanding of hydraulics in water distribution networks, PhD thesis, Delft University of Technology&#160;

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