University of Sheffield researchers have developed a new artificial intelligence that can predict sewer blockages. This can be used to reduce overflow of polluted waters into rivers thereby reducing river pollution by warning companies to clear potential problems.
Developed as part of a collaboration between the University of Sheffield, Siemens and Yorkshire water, researchers have developed a pioneering artificial intelligence that can predict blockages in sewers to help cut pollution incidents and improve the health of rivers.
When sewers are blocked, alternative combined sewer overflows (CSOs) are adopted to reduce pressure on the sewage system. These divert wastewater (grey water from homes and businesses, and excess heavy rainfall) into rivers and waterways, polluting and destroying delicate freshwater ecosystems in the process.
This AI technology, which collects water level data and feeds into the SIWA Blockage Predictor, identifies potential problems such as blockages in the sewage system. These can then be investigated by Yorkshire water and solved before the system is overwhelmed.
This technology is already being rolled out across 2000 sites to reduce pollution risks, giving two weeks notice of blockages and identifying nine out of ten potential issues .
Heather Sheffield, Integrated Planning and Central Control Manager at Yorkshire Water, said
“Reducing intermittent discharges from CSOs is a key priority for us and our partnership with Siemens and the University of Sheffield illustrates Yorkshire Water’s commitment to investing in cutting-edge technology to reduce pollution incidents by 50%, a key goal of our Pollution Incident Reduction Plan 2020-2025.”
Dr Will Shepherd, Principal Investigator from the University of Sheffield’s Department of Civil and Structural Engineering, said:
“This project with Siemens and Yorkshire Water has been a great example of commercialising university research to provide a tool which will reduce environmental impacts from our sewer networks by rapidly identifying blockages and enabling targeted maintenance”
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