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HAZOP, FMECA, monitoring algorithm, and Bayesian network integrated approach for an exhaustive risk assessment and real‐time safety analysis: Case study

Journal article

Naima Nehal, Mokhtaria Mekkakia‐Mehdi, Zakia Lounis, I. H. Guetarni, Z. Lounis
Process safety progress, 2024
Semantic Scholar DOI
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APA   Click to copy
Nehal, N., Mekkakia‐Mehdi, M., Lounis, Z., Guetarni, I. H., & Lounis, Z. (2024). HAZOP, FMECA, monitoring algorithm, and Bayesian network integrated approach for an exhaustive risk assessment and real‐time safety analysis: Case study. Process Safety Progress.

Chicago/Turabian   Click to copy
Nehal, Naima, Mokhtaria Mekkakia‐Mehdi, Zakia Lounis, I. H. Guetarni, and Z. Lounis. “HAZOP, FMECA, Monitoring Algorithm, and Bayesian Network Integrated Approach for an Exhaustive Risk Assessment and Real‐Time Safety Analysis: Case Study.” Process safety progress (2024).

MLA   Click to copy
Nehal, Naima, et al. “HAZOP, FMECA, Monitoring Algorithm, and Bayesian Network Integrated Approach for an Exhaustive Risk Assessment and Real‐Time Safety Analysis: Case Study.” Process Safety Progress, 2024.

BibTeX   Click to copy 

@article{naima2024a,
  title = {HAZOP, FMECA, monitoring algorithm, and Bayesian network integrated approach for an exhaustive risk assessment and real‐time safety analysis: Case study},
  year = {2024},
  journal = {Process safety progress},
  author = {Nehal, Naima and Mekkakia‐Mehdi, Mokhtaria and Lounis, Zakia and Guetarni, I. H. and Lounis, Z.}
}

Abstract

Hazard studies are essential in the petrochemical industry to ensure safe operations. This article provides an in‐depth analysis of the hazards associated with a vacuum distillation unit furnace. This study aims to identify probable hazard scenarios related to furnace operation, assess the associated risks, and provide prevention and mitigation strategies. A comprehensive strategy was employed to achieve these objectives, combining two analysis methods: HAZard OPerability (HAZOP) and Failure Modes, Effects, and Criticality Analysis (FMECA). This integrated approach enables a comprehensive risk assessment to be carried out and appropriate preventive measures to be taken to maintain safe operations, including renovation work. Then, depending on the results of the two methods, it is essential to constantly evaluate equipment safety, taking into account parameters such as furnace efficiency, tube temperature, and fume temperature. Therefore, a monitoring program has been created in Python, which enables real‐time examination of the furnace's safety with these critical parameters. If safety conditions are compromised, alarms are sent to mitigate risks, particularly in case of a failure. A Bayesian model is also developed to evaluate the algorithm's results and determine renovation and failure case scenarios. This comprehensive approach improves risk assessment's reliability, precision, maintains safe and efficient industrial operations.