Sewer Monitoring and Management in the Digital Era

Overflow, flooding, corrosion, and odorous emissions are persistent issues in the management of sewers. Current sewer maintenance is still largely reactive, and due to practical limitations must generally focus on problem solving in local networks in response to incidents. To proactively optimise sewer operation would require a system-wide (network) approach. To achieve this, efficient data science techniques are required that pair Digital Twins with soft sensor networks and data analytics and predictive control algorithms to identify and resolve real time anomalies across sewer networks. 

Outcomes:
1.    New mechanistic and efficient data-driven digital twins as needed to describe sewer hydraulics and interactions with the environment, including groundwater
2.    Novel approaches that incorporate these digital twins into data acquisition (sensing) in support of smart monitoring, data analytics.
3.    Mechanisms for short and long-term decision making, as well as real time control.

Collaborators:
UQ Environmental Engineering and computer Science
UQ Civil Engineering
South East Water
Water Corporation
Hunter Water
Melbourne Water
Urban Utilities
IWN, Goulburn Valley Water
Envirosuite
Water Research Australia
University of Exeter
Detection Solutions