Using AI to Detect Early Blockage Formations in Wastewater Networks

Wessex Water and StormHarvester Case Study

AUTHOR

Related Topics

CSO Monitoring

Artificial Intelligence

BACKGROUND

Wessex Water provides water and sewerage services to 2.8million people in the South West of England with 35,000km of sewers, clearing approximately 13,000 blockages a year at a cost of £5m annually.

In May 2020, amongst strong international competition, StormHarvester became a successful finalist in a 3 month smart sewer trial with Wessex Water in the city of Bath wastewater catchment. Bath consists of approx. 3,500km of sewers representing c.10% of the Wessex Water total. Across this network there were 98 sensored assets, 89 of these were at combined sewer overflow (CSO) locations and the remainder at pumping (lift) stations.

One of the biggest problems we have serving our customers is not knowing where and when blockages will occur, or are likely to occur, in the wastewater network.

-Jody Knight, Asset Technology Manager, Wessex Water

THE CHALLENGE

If left unchecked, early blockage formations can lead to service failures i.e. pollution or flooding events. However, if spotted early enough, blockage formations can be cleared and therefore costly service failures avoided. During wet weather it is difficult to differentiate expected high sewer levels caused by heavy rainfall volumes from those higher than usual levels arising from restrictions in the network i.e. by partial or total blockages

Wessex Water’s goal was to use latest technologies to gain additional insights from their existing network of wastewater sensors. Specifically, the company wanted to test the ability of AI (machine learning) to:

Accurately Detect Early Blockage Formations

Create Smarter (Control Room) Alarms

If AI could differentiate between these different events, then both an improvement in alarm quality along with alarm rationalisation could be possible. During Spring 2020, Wessex Water ran a challenge with 16 entrants to demonstrate the value of applying AI (machine-learning) to it’s wastewater network with the following objectives:

  1. Predicting early blockage formations before they become service failures (i.e. pollution or flooding incidents)
  2. Viability of condition based maintenance
  3. Ability to differentiate genuine control room alarms from those triggered simply because of high volumes of rainfall

THE SOLUTION

Across the Summer of 2020 StormHarvester deployed its Intelligent Sewer Suite product to provide real-time level predictions and alerts on early blockage formations for the sewer network of the city of Bath. These alerts were used to identify potential noncompliant out-of-sewer pollution events before they occurred so that maintenance crews could proactively remedy issues before they resulted in service failures (i.e. pollution or flooding incidents).

The Intelligent Sewer Suite’s proprietary AI (machine learning) algorithms and predictive analysis tools were used on both CSO and pumping station sensor data with corresponding hyperlocal rainfall forecast data, to predict network levels and detect potential blockage formations in real-time. Only existing sensors were used for this purpose and no new sensor installations were required.

The StormHarvester system took only 3 weeks to set-up before it started developing usable results. The process included the extraction of historic sewer level data and historic rainfall levels in a 1.5km squared grid for each of the 98 assets, and the undertaking of tens of millions of iterative machine learning calculations in order to ‘learn’ sewer asset behaviour in both dry and wet weather periods.

The safe operating window or thresholds are predicted based on a number of factors including time of day, day of week, hyperlocal rainfall, local river/borehole levels, etc. These dynamic thresholds are predicted for 6 hours into the future and are updated every 15 minutes on an asset level. This is one of the keys to such accurate forecasting. The solution did not require or utilise any hydraulic models which was key to its quick set up and accuracy.

The StormHarvester system used machine learning to set safe
operating windows or thresholds for each asset. Each time these had a significant breach, we received alerts, which in turn were passed to the Operations team so that they could respond.

-Edmund Willatts, Asset Reliability Engineer, Wessex Water

THE RESULTS

In 3 months, StormHarvester’s Intelligent Sewer Suite detected over 60 early blockage formations in real time, at least 2 of which Wessex Water told us were likely to have caused significant pollution incidents (CAT 3 or worse) if it was not for these alerts. Over 60 telemetry and sensor faults were also detected in real time.

Wessex Water considered the alerts provided by StormHarvester a major improvement on the status quo where operational staff were regularly overwhelmed by the large number of high-level and overflow alarms occurring in the control room during periods of heavy rainfall.

Based on the value brought by the StormHarvester alerts Wessex Water decided to keep the alerting system running on the Bath catchment after the initial POC.

During the trial, StormHarvester were able to identify sewer blockages very early on and we were therefore able to get the Operation teams to proactively intervene. This significantly increased our chances of making it quicker and easier to spot spillages.

-Jody Knight, Asset Technology Manager, Wessex Water
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