What if automation and AI could reduce leakage and predict and prevent outages, saving millions of pounds per year?
Large UK Water Utility
Utilities / Water
One of the UK’s largest private water utilities sought to consolidate multiple independent data sources used to determine the level of risk in the water system. Operators in the control room had no singular view of the network, which spans more than 31,000 km of infrastructure, instead having to derive the state of risk by looking at various sources aggregated in different ways and by different tools.
To better service their 10+ million customers and generate insights from the thousands of sensors across the network, we helped the utility to implement the Hydraulic Network Risk Tool, or HNRT, platform. The solution would present the various data sources as a single, consolidated visualisation overlaid on a map that encompassed more than 500 sensors and CPPs. In-built and automated analytics would alert operators to risks across the network, enabling proactive maintenance and reduced interruptions.
The HNRT uses the network’s pressure and flow data from district meters and CPPs. Those inputs are combined with customer contacts – when a customer reports an issue or submits a complaint – to show all relevant assets on a map, including valves, reservoirs, pipes, meters, and digital assets. HNRT also ingests data from the utility’s operational platform, which provides the states of actuated items like whether, for example, a valve is open or closed. Customer property data from yet another system is also included and shows details like the number of properties in a DMA.
These data sources are ingested at 15-minute intervals from the thousands of meters and CPPs across the network. HNRT performs preset analytics on the data to alert operators to any risks. For example, large pressure differentials across valves which are supposed to be open, or low reservoir levels. HNRT is used by network operators, operational control managers, and system operators.
The map on which the asset and status data is overlaid uses a RAG (red-amber-green) system to colour the pipes according to any of pressure, flow, or supply. Supply data is pulled from another system we developed called SDSR. At the highest level, operators can view the overall hydraulic system. They can drill down, in turn, to FMZ level, DMA level, including PMAs, and finally to the pipe level.
The net effect is a consolidated, near-real-time view of a vast and complex water system, enabling operators to be better informed and with greater lead times.
“They worked collaboratively with us to create groundbreaking new data science-driven capabilities which have won awards and helped us significantly improve business performance. The team has worked alongside us, to challenge and steer us and help us develop our own data science skills.”