Data‐mining methods predict chlorine residuals in premise plumbing using low‐cost sensors
Data‐mining methods predict chlorine residuals in premise plumbing using low‐cost sensors
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DOI：
10.1002/aws2.1214
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Daniella Saetta;Rain Richard;Carlos Leyva;P. Westerhoff;Treavor H. Boyer
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文献类型：
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作者：
Daniella Saetta;Rain Richard;Carlos Leyva;P. Westerhoff;Treavor H. Boyer
由于公共卫生影响，建筑物内的可变水质越来越受到关注（例如，铅、嗜肺军团菌、福氏耐格里原虫、消毒副产物）。数据采集和分析的进步为监测真实的实时建筑范围内的水质变化提供了机会。因此，本研究的目标是创建一个水质传感器平台，包括数据采集，存储和挖掘方法，能够监测并最终改善建筑物内的水质。该平台仅使用传感器监测水温、pH值、电导率、氧化还原电位、溶解氧和氯。其他建筑数据基础设施，特别是居住者的Wi-Fi登录，用于估算活动率和相关用水量。一种先进的机器学习技术，梯度提升机器，比多元线性回归模型更好地预测了整个建筑管道网络中的氯残留量。最后，水质监测的成本，可扩展性，可靠性，人性化的尺寸，法规遵从性和未来的绿色建筑设计的影响被认为是。
Variable water quality within buildings is of increasing concern due to public health impacts (e.g., lead, Legionella pneumophila, Naegleria fowleri, disinfection byproducts). Advances in data acquisition and analytics provide the opportunity to monitor real‐time building‐wide water quality variability. Accordingly, the goal of this research was to create a water quality sensor platform including data acquisition, storage, and mining methods able to monitor, and ultimately improve, water quality within buildings. The platform was used to monitor water temperature, pH, conductivity, oxidation–reduction potential, dissolved oxygen, and chlorine using sensors only. Other building data infrastructure, specifically Wi‐Fi logins by occupants, were used to approximate activity rates and associated water use. An advanced machine‐learning technique, gradient boosting machines, predicted the chlorine residuals throughout the building plumbing network better than multivariate linear regression models. Finally, the implications of water quality monitoring on costs, scalability, reliability, human dimensions, regulatory compliance, and future green building designs are considered.