Supervisor – Professor Zhiguo Yuan
Sewer systems are among the most critical infrastructure assets for modern urban societies. Urban sewer networks collect and transport domestic and industrial wastewaters to wastewater treatment plants for pollutant removal prior to reuse or environmental discharge, thus protecting both public and environmental health.
Effective sewer management has been to date hindered by the scarcity of sewer data, particularly real-time data. The rapid development of IoT sensors enable their wide deployment in sewer networks. Consequently, the use of IoT sensors allows the collection of much larger volumes of data, orders of magnitude larger compared to traditional sensors. Such large data sets enable machine learning or data-driven modelling, to provide support to online control or off-line decision making.
Led by Prof Zhiguo Yuan AM, an ARC Australian Laureate Fellow, the sewer research team at ACWEB is undertaking research on the digitalisation of sewer management. The research focuses on on-line sensing, data analytics and on-line control.
Sensors are often subject to noise, disturbances, and faults, leading to erroneous data. An opportunity exists for an outstanding PhD candidate to develop advanced algorithms for data quality assurance. This will involve the use of machine learning with support from digital twins. The candidate will be jointly supervised by sewer experts and data scientists. Ample opportunities exist for the student to work with water utilities.
A working knowledge of urban water systems would be of benefit to someone working on this project.