Efficiency prediction of control room operators based on human reliability analysis and dynamic decision-making style in the process industry

Zarei, Esmaeil; Mohammadfam, Iraj; Aliabadi, Mostafa Mirzaei; Jamshidi, Ali Ashraf; Ghasemi, Fakhradin

doi:10.1002/prs.11782

Cited by 15 publications

(8 citation statements)

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“…As it can be seen, the probability of the basic event X 5 , which is human failure, shows the largest increase (RoV = 19), thus, representing the most critical basic event contributing to the failure of FLSS due to overpressure. This is in line with studies that have highlighted the role of human error in 70–90% of accidents in the chemical and process industries .…”

Section: Resultssupporting

confidence: 91%

Dynamic safety risk modeling of process systems using bayesian network

Zarei

Azadeh

Aliabadi

et al. 2017

Process Safety Progress

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Process complex systems in particular oil and gas plants due to dealing with hazardous materials at severe process conditions are much prone to catastrophic accidents. In this context, safety risk analysis is a crucial tool to develop effective strategies to prevent accident and provide mitigative measures. Dynamic risk analysis (DRA) is one of the most practical approaches for risk analysis that helps provide safer operations of complex process systems. The present work is aimed at demonstrating the application of an integrated DRA approach to comprehensive quantitative modeling and analysis of the both aspects of risk, that is, probability and consequence assessments. In this approach, first, the worst case scenario is identified and then a robust tool is developed for dynamic accident scenario modeling and risk assessment by means of Bayesian Network. This approach is applied to risk analysis of a flammable liquid storage system at a gas refinery. The work provides valuable information on the identification and comprehensive analysis of worst case accident scenarios, their main consequences, critical basic events, and minimal cut sets which lead to accident scenarios and also for dynamic updating of probabilities and risk. The obtained results are more appropriate and rigorous to developing preventive and mitigative strategies for potential accident scenarios and thus increase the safety level in the complex process systems. © 2017 American Institute of Chemical Engineers Process Saf Prog 36: 399–407, 2017

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Section: Resultssupporting

confidence: 91%

Dynamic safety risk modeling of process systems using bayesian network

Zarei

Azadeh

Aliabadi

et al. 2017

Process Safety Progress

Self Cite

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“…This, in turn, emphasizes the critical role of human error among the others in the failure of the regulator system. This is in agreement with studies that have highlighted the role of human error in 70% to 90% of accidents in the chemical and process industries [32][33][34]. The most probable consequences also are C1 and C2 with occurrence probabilities of 0.67 and 0.14, respectively.…”

Section: Bayesian Network Modelingsupporting

confidence: 91%

Dynamic safety assessment of natural gas stations using Bayesian network

Zarei

Azadeh

Khakzad

et al. 2017

Journal of Hazardous Materials

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Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks.

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“…Based on the BT model, the TE probability (isooctane spill) was calculated 2.387819E−02; moreover, the most probability (2.38711E−03) was identified for pool fire as a final ( Table 8 ). Although the capabilities of the BT model have been proven in event modeling ( Chen and Wang, 2019 ), studies have pointed out the limitations and shortcomings of this method in DRA ( Zarei et al., 2016 ). The present study used the BN method to remove the BT method shortcomings and limitations.…”

Section: Discussionmentioning

confidence: 99%