Volume 10, Issue 1 (Spring 2023)
johe 2023, 10(1): 17-24 |
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Laal F, Pouyakian M, Jafari M J. The Reliability Assessment of Firefighting Systems Using Fuzzy Bayesian Network in the Floating Roof Tanks of a Petrochemical Company. johe 2023; 10 (1) : 3
URL: http://johe.umsha.ac.ir/article-1-744-en.html
URL: http://johe.umsha.ac.ir/article-1-744-en.html
1- Social Determinants of Health Research Center, Department of Occupational Health Engineering, Birjand University of Medical Sciences, Birjand, Iran
2- Department of Occupational Health and Safety Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran ,pouyakian@sbmu.ac.ir
3- Department of Occupational Health and Safety Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2- Department of Occupational Health and Safety Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran ,
3- Department of Occupational Health and Safety Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract: (1921 Views)
Background and Objective: This study aimed to provide a risk assessment method based on a Fuzzy Bayesian Network (FBN) to prevent the failure of firefighting systems (FSs).
Materials and Methods: In this research, the fault tree structure was validated, and the failure factors were identified. Triangular fuzzy numbers and the combined CoA/Sum-Product method were used to calculate the failure rate. Subsequently, deductive and inductive reasoning, as well as sensitivity analysis were performed using fuzzy logic and fault tree transmission in a Bayesian Network (BN).
Results: The results of a case study on methanol storage tanks showed that the combination of FBN and validation of structures can be presented as a suitable method to evaluate the reliability of FSs. The content validity index of the final basic events was over 0.79. The highest and lowest failure rates were related to the foam system and valve, respectively. Moreover, the failure rate for the failure of FSs was 5.7×10-6 based on the fuzzy fault tree (FFT). After updating with BN, the previous rate of failure of FSs with FBN was calculated to be 0.022978, which was greater than the value of the FFT. The reliability of the system was equal to 0.77022.
Conclusion: The present approach can help the decision-making process of managers and analysts of the petrochemical industry to prevent the failure of FSs in tanks due to changes in systems.
Materials and Methods: In this research, the fault tree structure was validated, and the failure factors were identified. Triangular fuzzy numbers and the combined CoA/Sum-Product method were used to calculate the failure rate. Subsequently, deductive and inductive reasoning, as well as sensitivity analysis were performed using fuzzy logic and fault tree transmission in a Bayesian Network (BN).
Results: The results of a case study on methanol storage tanks showed that the combination of FBN and validation of structures can be presented as a suitable method to evaluate the reliability of FSs. The content validity index of the final basic events was over 0.79. The highest and lowest failure rates were related to the foam system and valve, respectively. Moreover, the failure rate for the failure of FSs was 5.7×10-6 based on the fuzzy fault tree (FFT). After updating with BN, the previous rate of failure of FSs with FBN was calculated to be 0.022978, which was greater than the value of the FFT. The reliability of the system was equal to 0.77022.
Conclusion: The present approach can help the decision-making process of managers and analysts of the petrochemical industry to prevent the failure of FSs in tanks due to changes in systems.
Article number: 3
Keywords: Bayesian network, Firefighting system, Fuzzy bayesian network, Petrochemical industry, Reliability, Uncertainty
Type of Study: Research Article |
Subject:
Safety
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