TY - JOUR
T1 - Process accident prediction using Bayesian network based on IT2Fs and Z-number
T2 - a case study of spherical tanks
AU - Aliabadi, Mostafa Mirzaei
AU - Abbassi, Rouzbeh
AU - Kalatpour, Omid
AU - Ahmadi, Omran
AU - Moshiran, Vahid Ahmadi
N1 - Copyright the Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2024/8/29
Y1 - 2024/8/29
N2 - This study aimed to propose a novel method for dynamic risk assessment using a Bayesian network (BN) based on fuzzy data to decrease uncertainty compared to traditional methods by integrating Interval Type-2 Fuzzy Sets (IT2FS) and Z-numbers. A bow-tie diagram was constructed by employing the System Hazard Identification, Prediction, and Prevention (SHIPP) approach, the Top Event Fault Tree, and the Barriers Failure Fault Tree. The experts then provided their opinions and confidence levels on the prior probabilities of the basic events, which were then quantified utilizing the IT2FS and combined using the Z-number to reduce the uncertainty of the prior probability. The posterior probability of the critical basic events (CBEs) was obtained using the beta distribution based on recorded data on their requirements and failure rates over five years. This information was then fed into the BN. Updating the BN allowed calculating the posterior probability of barrier failure and consequences. Spherical tanks were used as a case study to demonstrate and confirm the significant benefits of the methodology. The results indicated that the overall posterior probability of Consequences after the failure probability of barriers displayed an upward trend over the 5-year period. This rise in IT2FS-Z calculation outcomes exhibited a shallower slope compared to the IT2FS mode, attributed to the impact of experts' confidence levels in the IT2FS-Z mode. These differences became more evident by considering the 10-4 variance compared to the 10-5. This study offers industry managers a more comprehensive and reliable understanding of achieving the most effective accident prevention performance.
AB - This study aimed to propose a novel method for dynamic risk assessment using a Bayesian network (BN) based on fuzzy data to decrease uncertainty compared to traditional methods by integrating Interval Type-2 Fuzzy Sets (IT2FS) and Z-numbers. A bow-tie diagram was constructed by employing the System Hazard Identification, Prediction, and Prevention (SHIPP) approach, the Top Event Fault Tree, and the Barriers Failure Fault Tree. The experts then provided their opinions and confidence levels on the prior probabilities of the basic events, which were then quantified utilizing the IT2FS and combined using the Z-number to reduce the uncertainty of the prior probability. The posterior probability of the critical basic events (CBEs) was obtained using the beta distribution based on recorded data on their requirements and failure rates over five years. This information was then fed into the BN. Updating the BN allowed calculating the posterior probability of barrier failure and consequences. Spherical tanks were used as a case study to demonstrate and confirm the significant benefits of the methodology. The results indicated that the overall posterior probability of Consequences after the failure probability of barriers displayed an upward trend over the 5-year period. This rise in IT2FS-Z calculation outcomes exhibited a shallower slope compared to the IT2FS mode, attributed to the impact of experts' confidence levels in the IT2FS-Z mode. These differences became more evident by considering the 10-4 variance compared to the 10-5. This study offers industry managers a more comprehensive and reliable understanding of achieving the most effective accident prevention performance.
UR - http://www.scopus.com/inward/record.url?scp=85202799368&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0307883
DO - 10.1371/journal.pone.0307883
M3 - Article
C2 - 39208318
AN - SCOPUS:85202799368
SN - 1932-6203
VL - 19
SP - 1
EP - 40
JO - PLoS ONE
JF - PLoS ONE
IS - 8
M1 - e0307883
ER -