Smart Water System and Internet of Things

Kian Hariri Asli, K. Asli
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Abstract

Objective: Today, Internet of Things (IoT) technology has a significant impact on overcoming the challenges of water loss. The IoT also helps the reducing of these challenges. This study aims to show how can by reducing the water working pressure, water saving happened through IoT. Methods: In this work, water loss in the distribution network was investigated for smart water. Smart water systems can be achieved by advanced pressure management. The water pressure changes during the day and night can lead to water losses. In this work, the network pressure variations were recorded by IoT through geospatial modeling. The difference between the present work and the previous works was the emphasis on advanced techniques including networked sensors, remote sensing, geographic information systems (GIS), and the IoT. The pressure changes analysis was carried out by WaterGEMS software and ArcGIS-ArcMap software. Results: The results showed the average water saving in 6 days after the completion of the work. The water saving was reported as 527.66m3 per day equivalent to 1.22L/h noticeably more than half 41.83% of the water which was stored during the time of extra pressure. It happened especially from 4:00 midnight to 6:00 a.m. when Minimum Night Flow decreased to 405 cubic meters per hour. Conclusion: The amount of water entrance to the network and the saving percentage were assumed as dependent and independent variables for regression analysis. The results showed the power function had a suitable correlation due to the scatter diagram and P-value.
智能水系统和物联网
今天,物联网(IoT)技术对克服水资源流失的挑战产生了重大影响。物联网也有助于减少这些挑战。本研究旨在展示如何通过物联网降低水的工作压力,实现节水。方法:在本工作中,对智能供水配电网中的失水进行了调查。智能水系统可以通过先进的压力管理来实现。昼夜水压的变化会导致水分流失。在这项工作中,物联网通过地理空间建模记录网络压力变化。目前的工作与以前的工作的不同之处在于强调先进技术,包括网络化传感器、遥感、地理信息系统(GIS)和物联网。采用WaterGEMS软件和ArcGIS-ArcMap软件进行压力变化分析。结果:工作完成后6天平均节水。节水527.66m3 /天,相当于1.22L/h,超过了加压期间储水量的一半(41.83%)。特别是在午夜4点到早上6点,最小夜流量下降到每小时405立方米。结论:取管网进水量和节水率为因变量和自变量进行回归分析。结果表明,从散点图和p值来看,幂函数具有较好的相关性。
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