Showing 3 results for Bayesian Network
Tahereh Eskandari, Iraj Mohammadfam, Mostafa Mirzaei Aliabadi,
Volume 9, Issue 4 (12-2019)
Abstract
Introduction: The safety of CNG stations is important because of their location in urban areas, as well as to prevent accidents and to protect the safety of personnel, property, and environment. An event occurrence analysis with probability updating is the key to dynamic safety analysis.
Methods and materials: In this study, the Failure Modes and Effects Analysis (FMEA) technique was used to determine the hazards of the study unit, the method of analyzing. After determining the hazards with high risk, the Bayesian fault tree analysis (BFTA) method was used to determine the effective causes of events occurrence and the type of possible relationships among them.
Results: First, the phase of hazards identification, 16 Hazardous equipment were identified. Then the Risk Priority Number for the identified equipment was calculated. The results showed that the dispenser system had the highest risk priority number and was identified as the most critical equipment. According to this, the dispenser gas leakage (as the top event) was selected in this study. Then, the analysis of the dispenser gas leakage, using BFTA method identified 56 main causes, including 17 intermediate events and 39 basic events. Finally, cracking and corrosion of the dispenser hose were determined the most effective factor in the occurrence of the top event. The probability of occurrence of the top event based on FTA and BFTA analysis was calculated 9.67×10-2 and 9.11 × 10-2, respectively.
Conclusion: The result of the study that by employing the Bayesian Network, can create a useful guideline to determine the relationship between the occurrence causes of the top event. This provides an assessment of the effectiveness of preventive measures before using them.
Maryam Feiz-Arefi, Fakhradin Ghasemi, Omid Kalatpour,
Volume 12, Issue 3 (9-2022)
Abstract
Introduction: Oxygen-generating central plays a vital role in the continuous performance of hospitals. Any leakage or failure in this section can not only endanger the health and safety of patients but also cause fire and explosion. Probabilistic risk assessment is a useful tool for identifying the main root causes of leakage in oxygen-generating central. This study aimed at risk assessment of an oxygen-generating central in a hospital in Hamadan using fuzzy sets theory and Bayesian networks.
Material and Methods: First, all root causes supposed to contribute to oxygen leakage from any part of the oxygen-generating central were identified, and based on them a fault tree analysis (FTA) was constructed. Then, the FTA was mapped in a BN. The failure probability of root causes was calculated using fuzzy sets theory and experts’ opinions. Belief updating based on BN was utilized for subsequent analyses.
Results: According to this study, ignorance of labels on the oxygen generation and distribution system is the most important root cause leading to oxygen leakage. Moreover, removing masks from patient’s faces is the main cause of oxygen leakage in patient rooms. Once leakage occurred, the presence of an ignition source can lead to fire or explosion.
Conclusion: oxygen leakage can create considerable risks in hospitals. All staff should be provided with sufficient training regarding hazards of oxygen-generating and distributing systems and oxygen leakage. Particular attention should be paid to such leakages and their adverse consequence in emergency planning and hospital crisis management.
Zahra Khodabakhsh, Leila Omidi, Khadijeh Mostafaee Dolatabad, Matin Aleahmad, Hossein Joveini,
Volume 14, Issue 3 (10-2024)
Abstract
Introduction: Domino effects are a chain of low-probability and high-consequence accidents in which a primary event (fire or explosion) in one unit causes secondary events in adjacent units. Bayesian networks have been used to model the propagation patterns of domino effects and to estimate the probability of these effects at different levels. The unique modeling and flexible structure provided by Bayesian networks allow the analysis of domino effects through a probabilistic framework, taking synergistic effects into account.
Material and Methods: Firstly, collecting the basic information related to the location of the storage tanks and determining the scenario of the accidents were done. Furthermore, the values of the heat radiation as escalation vectors in case of a fire in one tank were determined using ALOHA software. The received heat flux values were compared with the heat radiation threshold of 15 kw/m2 and the escalation probability of the primary unit and the propagation of the initial scenario to nearby storage tanks were determined using Bayesian networks.
Results: The analysis of the heat flux values showed that among the 8 studied storage tanks, two storage tanks had the highest potential for spreading domino effects due to their location in a tank farm. Also, the implementation of Bayesian networks in GeNIe revealed that, compared to other storage tanks, the probability of domino effects propagating to other nodes is higher when a primary fire accident occurs in the two mentioned tanks, while considered as primary units.
Conclusion: Domino effect modeling and appropriate preventative measures can decrease the escalation probability in the process industries. Consideration of the synergistic effects of events at different levels by taking the escalation vectors into account leads to proper risk management and the determination of emergency response measures in storage tank farms.
