Korean scientists work to prevent water leaks from district heating pipes

The Korea Institute of Civil Engineering and Building Technology (KICT) has developed a new technology for diagnosing faults to prevent water leaks from district heating pipes, which supply energy in an eco-friendly and economical way.

KICT conducted research of measuring abnormal signals of the pipelines by using both acoustic emission (AE) sensors and accelerometer sensors and classifying the signals using machine learning to increase detection accuracy and diagnose various abnormalities including a water leak.

Preventing such leaks could drastically increase public safety. District heating pipelines supply hot water at temperatures of as high as 120℃ at constant pressure from a District Heating Hot Water Production Facility plant to the point of demand.

When a leak occurs in one of these pipes – particularly if it takes place near a road – it can have fatal consequences. This was the case in 2018, when an underground hot water pipeline ruptured near Baekseok Subway Station in Ilsan. The leak caused the death of one person and injured an additional 56. 

Pre-Insulated Pipes (PIP) used for district heating pipelines consist of a carrier pipe made of mild steel that transports hot water, an insulation material that maintains temperature, and a casing pipe made of High-density polyethylene that protects the insulation material.

Compared with uncovered pipes used for waterworks or liquified natural gas, it is more difficult to diagnose the PIPs due to the high temperature and pressure.

Conventional methods to identify leaks use wires and thermal imaging cameras, which can only accurately detect a water leak when it has volume and the pipe is close to the surface.  

In contrast, the research carried out by KICT scientists demonstrated that it is possible to diagnose abnormalities of PIPs by AE and accelerometer sensors. 

“The detection method proposed in the research may be the only feasible way of preventing water leaks because the levels of accuracy of water leak detection wire, a thermal imaging camera, and GPR are low. Also, because they can only detect a water leak,” said Dr Lee Hongcheol, a scientist involved in the project. 

“However, validation in the actual field is necessary because there are many variables showing abnormalities and their severity in addition to usual noise and vibration.”

In order to test the technology, the team created a pilot plant equipped with a circulating district heating system where two 259.6m long straight pipelines were installed both on the ground and underground, featuring four types of piping arrangements with straight, corrugated, branched, and swaged pipes. 

In order to test for all major types of abnormalities in pipelines, water leaks, corrosion, and cracks were reproduced. Testing was conducted on pipelines both on the ground and underground. The team then measured signals and used them to train a Support Vector Machine (SVM), one of the means of machine learning classification.

The test results showed that the accuracy for sensor installation, the abnormalities, and piping arrangements was 97 per cent on average, a level of accuracy similar to that of waterworks and gas pipelines.