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Review Article

AI-Driven Automation for Safety Monitoring in the Chemical Industry: A Review of Hazard Detection, Anomaly Detection, and Predictive Risk Assessment

  • Paper ID 1207
  • Volume 7, Issue 6
  • Pages 14-33
  • Published Online: November-December 2026
Open Access Peer Reviewed Crossref DOI

Authors

  • Sumit Powar1
  • Vivek Kodag2
  • Dr. P. R. Gavali3

1 2 3 Department of Computer Science and Engineering (CSE), D. Y. Patil College of Engineering and Technology, Kolhapur, Maharashtra, India.

Article Information

Published Online
November-December 2026
Pages
14-33
Article Type
Review Article
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References

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10. Hartung et al., “Deep anomaly detection on Tennessee Eastman Process data,” Chemie Ingenieur Technik, 2023, doi: 10.1002/cite.202200238.
11. S. Umer, N. Ullah, and J.-M. Kim, “Attention mechanism-based CNN-LSTM hybrid deep learning model for industrial pipeline leak detection,” Scientific Reports, vol. 16, Art. no. 22763, 2026, doi: 10.1038/s41598-026-53776-x.
12. J. H. Lee, Y. Kim, A. Rehman, I. Kim, J. Lee, and H. Yun, “Development of an AI-based image/ultrasonic convergence camera system for accurate gas leak detection in petrochemical plants,” Heliyon, vol. 10, no. 7, Art. no. e28905, 2024, doi: 10.1016/j.heliyon.2024.e28905.
13. P. Salunkhe, A. Tilak, M. Shirole, and N. Mehendale, “Autonomous gas leak detection in hazardous environments using gradient-guided depth-first search algorithm,” Automation, vol. 7, no. 1, Art. no. 13, 2026, doi: 10.3390/automation7010013.
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