Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM60491

Intelligent AI + IoT Safety System for Worker Protection in Cracker Factories

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM60491

Volume:

10 |

4 |

April 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem60491

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Share this article:

Authors

MK

Madesh K

AI

Artificial Intelligence

C

Coimbatore

NRJ

Navin Raja J

LS

Logeshwaran S

Author Services & Downloads

Full Articles

Abstract

The cracker manufacturing industry involves highly hazardous working environments where workers are exposed to risks such as gas leakage, fire outbreaks, smoke formation, and abnormal temperature rise. These hazards can lead to serious industrial accidents if they are not detected at an early stage. Traditional safety systems in many factories rely on manual monitoring or basic alarm systems that cannot continuously observe environmental conditions or provide intelligent analysis of potential risks. Therefore, there is a need for an advanced safety monitoring system that can detect dangerous situations in real time and ensure worker protection. This project proposes an Intelligent Artificial Intelligence and Internet of Things based safety system designed specifically for worker protection in cracker factories. The system uses multiple environmental sensors including gas sensor, fire sensor, smoke sensor, and DHT11 temperature sensor to continuously monitor the surrounding environment. These sensors detect hazardous conditions such as gas leakage, fire presence, smoke generation, and temperature changes. The sensor data is collected and processed using the ESP32 microcontroller integrated with the INDUS Board Coin. The processed information is displayed locally on an LCD screen so that workers can easily observe the environmental conditions inside the factory. In addition, the ESP32 transmits the collected data to an IoT cloud platform using wireless communication. The cloud platform allows supervisors to remotely monitor the factory environment and receive alerts when unsafe conditions occur. Artificial Intelligence techniques can further analyze the sensor data to identify abnormal patterns and predict potential hazards before they become serious accidents. The proposed system provides real-time monitoring, early hazard detection, and intelligent safety alerts. By integrating sensors, IoT technology, and AI- based analysis, the system improves workplace safety and helps prevent accidents in cracker manufacturing industries. This solution offers an efficient, low-cost, and reliable approach to protect workers and enhance industrial safety management.

KEYWORDS

Artificial Intelligence Internet of Things (IoT) Industrial Safety Monitoring ESP32 Microcontroller Gas Leakage Detection Fire Detection Smoke Monitoring Temperature Monitoring Worker Safety Smart Industrial Systems

Cite This Article

Madesh K, Artificial Intelligence, Coimbatore, Navin Raja J, Logeshwaran S, "Intelligent AI + IoT Safety System for Worker Protection in Cracker Factories," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, April 2026. https://doi.org/10.55041/ijsrem60491

Full Article/Conclusion

References

Editorial & Review Transparency

Publication Date

4/20/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

0

Total views since publication

PDF Downloads

0

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...