With the aid of ardent technologies, various innovations take place on a daily basis to bridge the gaps and address the challenges in varied sectors. In this row, Artificial Intelligence and machine learning may soon be used in order to develop indigenous technology for the prediction and control of vapor explosion-induced accidents in boilers so as to prevent such mishaps. It is pertinent to note that a staggering 23,000 boiler accidents have been recorded worldwide over the past 10 years, wherein India alone accounts for the 34% of global deaths.
With the view to address this challenge, Swarnajayanti fellow and an Associate Professor in the Department of Mechanical Engineering, IIT Patna, Rishi Raj is working on a novel technology which thereof will take aid from Artificial Intelligence & Machine Learning to develop prognostic tools for advanced prediction and control of vapor explosion induced accidents in boilers.
How will it benefit?
Before getting into how novel technology and tools will help in controlling vapor explosions, it is important to understand what is boiler explosion and what are the reasons that cause boiler explosions!
A boiler explosion is considered a catastrophic failure of a boiler. There can be many different causes for a boiler to explode including failure of the safety valve, corrosion of critical parts of the boiler, or subsequent low water level. Tracing the genesis, corrosion along the edges of lap joints was a common cause of early boiler explosions. The technology on which Swarnajayanti fellow and associate professor is working will help improve the health, efficiency, and economy of boilers used in key industrial and strategic applications. It bridges the gap between the fundamental knowledge of bubble dynamics on a heated substrate and about how boiling actually occurs in large-scale systems used in chemical, thermal, nuclear, petroleum, space-based, and manufacturing applications.
In order to get to the root of the problem and explain the physics of the phenomena, Prof Raj demonstrated that the acoustic fingerprint associated with bubbles may be instrumental in decoding the science of boiling. Boiling crisis or very vigorous boiling or formation of bubbles is dangerous for applications due to sudden increases in pressure and temperature. Professor Raj now proposes that with the support of the Swarnajayanti fellowship, he will visualize the bubble behavior in terms of size, number density, and frequency of bubble formation and couple it with sound and thermal (temperature) data. The objective of the research is to identify the dominant bubble activity features to develop a physics-informed tool for the advanced prediction of boiling regimes using a single acoustic sensor (hydrophone). Such a tool may then be deployed to invoke pre-emptive control of vapor explosion-induced accidents in industrial boilers.
As part of this project, the progress made is not only anticipated to advance the science of boiling but is also expected to accelerate the implementation of modern prognostics and health management tools in existing boilers.
In order to address important sustainability challenges, emphasis is given on key issues including:
– Improved safety due to the modernization of boilers
– Higher productivity due to reduction in downtime
– Optimal usage of energy & water resources
