Clean waterways with artificial intelligence and IoT

Natural water systems carry a high value. They support a diverse ecosystem for flora and fauna; they keep people healthy by preventing water-borne diseases; and they protect and create tourism and recreation opportunities.

 

However, in cities all over the world – and particularly in older cities – stormwater runoff and household sewage are transported to water treatment plants in the same piping network. An advantage of these so-called combined sewage systems (CSS) is that runoff water, which could be polluted with oil, pesticides, fertilizer, and more, is purified before it is released into nature. That’s good news for the environment.

 

There is a downside, however: During heavy rainfall or snowmelt, a CSS piping network can be stretched to the limits and untreated water may escape into waterways via the combined sewer outlet (CSO). But the chances of this happening are greatly diminished when blockages in the CSO are removed in time. Siemens, Yorkshire Water, and the University of Sheffield have joined forces to develop a system that employs artificial intelligence (AI) and the Internet of Things (IoT) to locate blockages before overspills can occur.

 

The British water company Yorkshire Water operates 55,000 km of sewers. In times of intense rainfall, CSOs are designed to release excess water and sewage into rivers to prevent flooding in public areas. Of course, these incidents need to be kept to an absolute minimum. In the framework of Yorkshire Water’s Pollution Incident Reduction Plan 2020-2025, the goal is to cut pollution incidents by 50 percent. Key to attaining this objective is to remove CSO blockages and thereby minimize the probability of a release.

 

For some time now, around 2,000 sensors on CSOs have been monitoring sewage levels and issuing alerts when an overspill has happened. But wouldn’t it be better to know where blockages are most likely to occur so that debris can be removed before an overspill has a chance of developing? Until now, experts at Yorkshire Water have been trying to make predictions by evaluating data from the sensors with statistical methods, but that often led to false alarms and late detections.

The analytical challenge is how to account for the personal character of every CSO. Each one responds differently to rainfall, so it is difficult to know if the observed changes in level are normal or not.

 

Initially, an AI system was trained with sensor data to learn the normal behaviour for a CSO when it rains. Now, a new AI model has been trained for each site to learn its unique pattern of behaviour in response to rainfall. Fuzzy logic technology is then employed to automatically interpret the data to detect any significant differences in behaviour. When an issue is found, a response team at Yorkshire Water receives a notification to visit the asset and remove the blockage or forming blockage. Because SIWA Blockage Predictor is embedded within a web application, users can access it on mobile devices and PCs.

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