Sewer Monitoring and Management in the Digital Era

溢流、淹水、腐蚀和异味排放是公用事业管理下水道的长期问题。目前的下水道维护是被动的，侧重于解决当地网络的问题，尽管最佳解决方案需要全系统的方法。利用物联网传感器、无线传输和机器学习的最新发展，该多学科项目旨在开发数字孪生支持的数据分析，用于主动下水道管理，包括全网实时控制。该项目的目的是产生显著的社会、环境和经济效益，使公用事业公司能够更好地保护公众和环境健康，减少下水道气味和温室气体排放，延长下水道资产的使用寿命。

Overflow, flooding, corrosion, and odorous emissions are persistent issues for utilities managing sewers. Current sewer maintenance is reactive, and focuses on solving problems in local networks, despite that optimal solutions require a system-wide approach. Capitalising on recent development in IoT sensors, wireless transmission, and machine learning, this multidisciplinary project aims to develop digital-twin supported data analytics for proactive sewer management including network-wide real-time control. The project aims to generate significant social, environmental and economic benefits by enabling utilities to better protect public and environmental health, reduce sewer odour and greenhouse gas emissions, and extend sewer asset life.