Steam Leakage Monitoring

Steam! It is one of the very critical resources in many industries not just for generating power but also used for heating, generating pressure in several industries such as paper, fertilizer, sugar, petrochemicals, chemical and so many more. In process industries, steam is used for various applications like power heating, atomization, etc. A major concern is steam leaks and it has been observed that after a few years of installation due to aging gaskets, fittings, and steam traps, steam starts to leak from distribution pipelines. As a result of steam leaks, energy is lost, emissions are increased, production is lost and safety is compromised. It is estimated that a steam leak of 20kg/hr leads to 280 tons of coal consumption per year. Yet in most industries, steam leak monitoring is still undertaken manually.

Monitoring of steam leaks in a steam distribution system will be enhanced with digital visualization of steam pipe networks. We can take periodic or continuous photos and compare them with reference images using different technologies like IoT, Machine Learning, and video processing. By analyzing photographs, real-time data is collected, and notifications will be sent to the plant maintenance team with geographic coordinates, photographs, etc. The data will be further analyzed and used to build the dashboard with different KPIs for real-time monitoring.

Steam leakage affects and measurement

Steam leaks can have a major impact, especially on steam generation. For example, energy losses, loss of reliability, production delays, etc. In addition to safety issues, extra load on generators because of steam leaks is another major concern in the industry. In the event of delay in arresting steam leaks, steam tends to erode, and leak increases. Safety is an issue because steam leaks can lead to serious burns and even death. Stopping steam leaks on time can also reduce CO2 emissions and non-emissions.

Steam leaks are calculated in two methods. One method is based on the diameter of the leak and pressure inside and outside of the pipe. This is an accurate method, but it is expensive and needs trained people. Steam is extremely hot and measuring the diameter of the leak is very dangerous.

A second method for determining the steam leakage is by measuring the length of the plume. Although this is not an accurate method, it is a practical and economical approach to estimate steam losses. The graph below is provided by the Bureau of Energy Efficiency. It estimates the amount of steam leakage based on the length of the steam jet plume at the point of leakage. This method helps us to quantify the steam loss in kg per hour by using the length of the plume, irrespective of pressure inside the pipe.

Steam leakage monitoring

Identifying the steam leakages by visiting locations physically is a daunting task. Controlling steam pipe leaks depends on how frequently we monitor and send the information to relevant teams about where the leak is (Coordinates), the approximate leak characteristics such as a picture. We need to utilize technology to eliminate/supplement manual inspection and send communication to appropriate teams for action.

Usage of technology

We believe by using a combination of intelligent digital technologies viz; IoT, Machine Learning, Video and Image Processing, etc., we can do LIVE MONITORING of the steam pipes and check for leakages the moment they occur. Static cameras can be installed at sites of interest and should feed live into the Machine Learning algorithm for continuous feed and verification. The Machine Learning algorithm is pre-trained to detect any anomaly like detecting steam leakages, length of the plume, etc. Depending on the need and criticality, the feed can be monitored continuously or at regular intervals.

In case the incoming feed is live and is a continuous video feed, it can be run against the extraction program to get the frames/pictures from the video stream. These frames can then be run against the custom vision algorithm to detect if there is a leakage or not in the frame. If the feed is monitored at regular intervals by taking photographs (e.g. one photograph is taken every 5mins), the photograph is directly run against a custom vision algorithm to detect if there is a leakage visible in the photograph. If the leak is not detected, the image is archived immediately. If in case the leak is detected, the image will be sent to the cloud to be run against the deeper machine learning algorithm. This is where the program will identify the size, and position of the leak. Based on that, steam loss calculation will happen. Also, an alert will be sent out to the relevant person to quickly check the leak.


Convert data collected to actionable items and dashboard

Triggering notifications to relevant team with geolocation position of leak and photograph for immediate action. Further data collected can also be stored and analyzed over time to make decisions. For example, there could be a specific location that is seeing more leaks than usual. Specific analytics like these could be drawn that can help factory managers take up preventive maintenance work in specific areas and avoid the leaks altogether.

  • Date vs. steam leaks – graph
  • Comparison of steam leaks across
  • Departments
  • Target vs. actual steam leaks
  • Steam loss value in currency
  • Plan vs. targeted steam production

Different types of dashboards can be drawn to show a continuous monitoring of steam leaks to maintenance manager.


Changes need to occur imminently to achieve a leak-free steam system. The goal of a leak-free steam distribution system is achievable with continuous monitoring and immediate action. The continuous monitoring of steam pipelines is achieved through digital monitoring and developing a platform to trigger notifications. The steam leakage dashboard with predefined KPIs will help in monitoring and addressing steam leakages quickly thus avoiding major breakdowns and accidents and also saving cost.

Author Details

Nitin Mahajan

Results-oriented Leader with a proven track record of delivering exceptional performance. Equipped with over two decades of IT services and Industry experience. My expertise lies in building pioneering futuristic business architecture and solutions that focus on Industry and Technology Trends, Sustainability, Automation, Cloud, and above all, are Customer Centric.

Ravi Sankar Mathuri

Ravi Sankar Mathuri is a certified energy auditor with over 13 years of experience in Engineering and 14 years in SAP implementations. Over the past five years, he has been involved in implementation of SAP S4 HANA including advanced SCM modules like Enterprise Warehouse Management (EWM).

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