Feasibility Study of IoT-to-AI in Wastewater
Alexandrina Council wanted to consider the future possibilities of the Internet of Things (IoT) and Artificial Intelligence in their wastewater utility.
THE CHALLENGE
To be the smartest wastewater network in regional Australia?
Alexandrina Council has the 3rd largest wastewater network in South Australia. The wastewater utility is responsible for connecting, treating, storing and disposing of treated wastewater through a large infrastructure network for over 7000 connections. The assets are managed by a relatively small number of FTEs over a large geographical area and, despite best efforts, the operations are predominantly reactive and symptom based.
There was a need to consider the future possibilities surrounding the IoT that would enable operations to become more efficient and deliver insight for strategic asset planning.
“We wanted to ensure a strategic approach was well understood before considering procurement. We needed to cover the integration of existing and new technology into a future state that would deliver the necessary benefits we expect of an IoT project.”
The Sustainability Manager, Mr Gary Lyons
THE RESULTS
Alignment across the organisation
SpiralData’s strategic advice gave Alexandrina Council clear insight into where the major challenges lie, what’s needed to improve them and helped senior leaders prioritise what to do to achieve their aims using data. Specifically, some of the key IoT opportunities identified in the future state include:
- Global pump control to optimise downstream assets
- Real-time monitoring of operations for efficient alarm and response
- Machine learning for predictive maintenance and improved asset lifecycles
- Time series analytics to accurately inform upgrades and expansion
- Ongoing identification of water ingress for asset capacity planning
More generally, the advice by SpiralData was leveraged in the following ways:
Key stakeholder buy-in across the Executive and elected members
Approval for appropriate levels of resourcing
Gradual change management to a more data-driven future
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