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January 21, 2021 Thursday 05:23:26 PM IST

AI algorithm that can detect leaks instantly in gas pipeline networks

Science Innovations

A sensor network powered by an artificial intelligence (AI) algorithm developed by scientists from Nanyang Technological University, Singapore (NTU Singapore) can accurately detect, in real-time, gas leaks and unwanted water seepage into gas pipeline networks.  Successful in field trials conducted on Singapore’s gas pipeline networks, the algorithm has been patented and spun off into a start-up named Vigti, which is now commercialising the technology. It has recently raised early start-up funding from Artesian Capital and Brinc, Hong Kong.

The NTU start-up is incubated by the University’s EcoLabs Centre of Innovation for Energy, a national centre launched in April 2019 to help small and medium-sized enterprises (SMEs) and start-ups innovate, grow and thrive in the energy sector.

A smart warning system that can detect gas leaks and broken gas pipes in real-time has been a long-term goal for the public utility industry, as the current industry best practice for inspecting pipes is for workers to undertake manual surveillance at regular intervals.

While big leaks can be easily detected via conventional sensors as the gas volume and pressure differences will fluctuate sharply in the pipe networks, small leaks are much harder to detect.

In 2014, the Energy Market Authority of Singapore (EMA) awarded a grant to NTU researchers led by Dr Justin Dauwels, then an associate professor at the School of Electrical & Electronic Engineering, to develop an anomaly identification software for low-pressure pipeline networks.

Over a four-year period starting from 2015, the NTU researchers developed, deployed and tested their AI solution on certain segments of the local city gas network in Singapore over six months, which was shown to be successful in detecting all tested types of anomalies.

The EMA funded project concluded in 2019 after the successful field trials and Vigti was then formed to continue developing the innovation and bring it to the global market.

While within a typical gas network there are sensors installed at regulator points that can detect major fluctuation in the network and calculate the Unaccounted-for-Gas (UFG) loss, small leaks and cracks can escape notice and thus must be manually detected.

With the conventional threshold-based approach, leaks can only be detected if the pressure drop due to the leak is higher than the pressure variation of the network during normal operation. If it is lower than the pressure variation, the leaks will be very hard to detect unless the pipes are inspected manually.

The cumulative loss of all the small leaks for major companies across the world is estimated between 1.5 to 3 per cent of total gas consumption.

Total natural gas consumption worldwide is estimated to be 3.9 trillion cubic metres as of 2019, thus even a 1 per cent loss would mean some 39 billion cubic metres globally (10 times the total consumption of natural gas of Singapore in 2017).

To tackle these issues, the NTU team performed various computational simulations to understand the leak and water ingress phenomena in the city’s natural gas distribution networks.

A variety of sensors that can measure pressure, flow, temperature and vibration, were deployed and the resulting signals associated with the anomalies in the network’s pipes were analysed. This process established unique ‘signatures’ within the sensor data for each anomaly.

Using machine learning and AI, the team then developed a software algorithm that is extremely sensitive in detecting anomalies by matching these unique signatures within the sensor data that is routinely monitored.  

During the field trial, a total of 16 pressure sensors and 4 flow sensors of various types were deployed at the riser, service line and main line, across three different locations. Data was then analysed at each location and leak and water ingress tests were also performed at these sites.

At the end of the project, a test was done to establish the effectiveness of NTU’s AI comprising 13 different anomaly tests. All 13 were successfully identified by the algorithm as leaks, along with the nearest sensor location and the time duration of these leaks.


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