Monitoring and Prediction of Boiler Refractory Failure by Remote Refractory Failure Detection and Temperature Pressure Monitoring System an Artificial Intelligence based System Using Machine Learning

International Journal of Mechanical Engineering

© 2025 by SSRG - IJME Journal

Volume 12 Issue 4

Year of Publication : 2025

Authors : Meeta P Chauhan, Jeetendra A Vadher

10.14445/23488360/IJME-V12I4P106

How to Cite?

Meeta P Chauhan, Jeetendra A Vadher, "Monitoring and Prediction of Boiler Refractory Failure by Remote Refractory Failure Detection and Temperature Pressure Monitoring System an Artificial Intelligence based System Using Machine Learning," SSRG International Journal of Mechanical Engineering, vol. 12,  no. 4, pp. 61-69, 2025. Crossref, https://doi.org/10.14445/23488360/IJME-V12I4P106

Abstract:

Energy management is a crucial issue nowadays because the global energy demand is increasing day by day, and there are limited resources of energy available worldwide. The study utilized Internet of Things (IoT) devices to investigate cracks and hot spots of failed structures to understand and address the problem. Refractory materials constitute an integral element in industries involving high-temperature applications, such as furnaces, kilns, and reactors. Mechanical failure of these refractories in the said applications could result in large-scale disasters, including personnel hazards, costly downtime, and economic inefficiency in operations. This work constitutes research into the integration of various techniques in IoT applied to the early detection of refractory material failure. We have made the Smart system, which helps small-scale industries to identify energy loss and early detection of any fault in refractory. The system is based on the concept of artificial intelligence machine learning. It detects any fault in the boiler furnace, and we can also identify any significant heat/energy loss in the furnace. We used an artificial intelligence-based supervised machine learning algorithm and Python program to detect defects in refractory materials. The supervised data was collected over 21 months from the Thermax boiler furnace. This expert system will also reduce the inspection of periodic and frequent boiler plant shutdowns due to periodic checking and inspections. This smart system also avoids major boiler-exploding incidents and accidents in small-scale industrial plants.

Keywords:

Industry Innovation & Infrastructure, Energy loss, Refractory failure, Artificial intelligence based supervised Machine Learning, Energy management using the Internet of Things.

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