Sewer Monitoring and Management in the Digital Era. 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 proa ....Sewer Monitoring and Management in the Digital Era. 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.Read moreRead less
Developing a truly intelligent water meter through advanced data analytics. Developing a truly intelligent water meter through advanced data analytics. This project aims to develop intelligent pattern recognition algorithms using international data sets to autonomously categorise household water consumption data into end-uses (e.g. showers, leaks). Despite intelligent meters, big data chokes rather than enables decision making for customers and utilities. This project will resolve information sy ....Developing a truly intelligent water meter through advanced data analytics. Developing a truly intelligent water meter through advanced data analytics. This project aims to develop intelligent pattern recognition algorithms using international data sets to autonomously categorise household water consumption data into end-uses (e.g. showers, leaks). Despite intelligent meters, big data chokes rather than enables decision making for customers and utilities. This project will resolve information synthesis concerns using a combination of non-linear blind source separation techniques adapted from the pattern recognition, signal processing and decision science fields. Expected outcomes are that utilities will be leaders of sustainable water use in the information age, and that customers can use phones to access real-time data of water consumption.Read moreRead less