JOURNAL ARTICLE
IoT based Boiler Health Monitoring for Sugar Industries.
Published In: Grenze International Journal of Engineering & Technology (GIJET), 2024, v. 10, n. 2,Part 4. P. 5178 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Sayyad Liyakat, Kazi Kutubuddin 3 of 3
Abstract
Steam boilers are essential to the drying and crystallization processes in the sugar industry. Boilers in the sugar industry primarily use three fuels: bagasse, coal, and biomass. Fuels like coal and biomass can be burned efficiently in boilers with moving grates, reducing the need for additional fuel. The boiler system is controlled by a number of mechanisms to ensure its reliable operation. The system's outputs include blow water, flue gases, electrical power, steam pressure and temperature, and heat loss. This article discusses the potential use of IoT in sugar factory boiler health monitoring. Internet of Things (IoT) technology has the potential to completely change the manufacturing process in factories and reduce maintenance costs. Boilers are essential to the production of sugar and require regular maintenance to ensure optimal performance. Boiler health monitoring powered by the Internet of Things (IoT) can help identify and fix problems fast. The article discusses the advantages of using IoT for boiler health monitoring, which include reduced maintenance costs, increased efficiency, and real-time boiler performance monitoring. All things considered, this paper provides a fascinating look at the possible advantages of IoT-based boiler health monitoring in sugar factories, such as higher output and cheaper maintenance. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Grenze International Journal of Engineering & Technology (GIJET). 2024/06, Vol. 10, Issue 2,Part 4, p5178
- Document Type:Article
- Subject Area:Engineering
- Publication Date:2024
- ISSN:23955287
- Accession Number:181715120
- Copyright Statement:Copyright of Grenze International Journal of Engineering & Technology (GIJET) is the property of GRENZE Scientific Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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