Safety risk modeling and major accidents analysis of hydrogen and natural gas releases: A comprehensive risk analysis framework

Mohammadfam, Iraj; Zarei, Esmaeil

doi:10.1016/j.ijhydene.2015.07.117

Cited by 112 publications

(46 citation statements)

References 26 publications

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“…Another lethal effect of flash fire is the depletion of oxygen in the atmosphere. Flash fires can also do significant damage to the facilities .…”

Section: Resultsmentioning

confidence: 99%

“…PHAST is a professional simulator used for CM in chemical process risk assessments . More description of CM that is used in this work may be find in previous work of the authors . At the end, risk has been estimated by multiply of occurrence probability and severity (lethality numbers in this work) of a given scenario.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…High pressure in the flare system 2.00E-03 2.00E-03 2.04E-03 X 2 Inlet temperature higher than normal 1.00E-03 1.00E-03 1.02E-03 X 3 V-3 closed 3.00E-03 3.00E-03 3.06E-03 X 4 PV-1 remains closed 2.35E-02 2.35E-02 2.40E-02 X 5 Human failure 5.00E-02 5.00E-02 9.79E-01 X 6 Valve PHAST is a professional simulator used for CM in chemical process risk assessments [23][24][25][26]. More description of CM that is used in this work may be find in previous work of the authors [6,9,13,27]. At the end, risk has been estimated by multiply of occurrence probability and severity (lethality numbers in this work) of a given scenario.…”

Section: Dynamic Accident Scenario Modelingmentioning

confidence: 99%

“…Another lethal effect of flash fire is the depletion of oxygen in the atmosphere. Flash fires can also do significant damage to the facilities [13,29]. Figures 6c and 6d show explosion radii and maximum overpressure caused by the catastrophic rupture scenario of the system.…”

Section: Consequence Modeling and Evaluationmentioning

confidence: 99%

“…Many authors have investigated different techniques to analyze risk of potential accident scenarios. Quantitative risk analysis (QRA) is one of the most popular and effective approaches to risk control and management and accident scenario modeling in the complex process systems [9][10][11][12][13]. However, despite the fact that QRA has proven to be an effective approach to the management of major accident hazard, it has some limitations and disadvantages, such as: (1) being incapable of handling data scarcity and data uncertainties, (2) inability to consider conditional dependencies among basic events, and (3) having an intrinsically static nature and then unable to update the probability of events and the consequent risk, which are very important issues in safety and risk analysis in process plants [4,8,12,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Another lethal effect of flash fire is the depletion of oxygen in the atmosphere. Flash fires can also do significant damage to the facilities .…”

Section: Resultsmentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

Section: Dynamic Accident Scenario Modelingmentioning

confidence: 99%

Section: Consequence Modeling and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic safety risk modeling of process systems using bayesian network

Zarei

Azadeh

Aliabadi

et al. 2017

Process Safety Progress

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Risk Identification and Safety Technology for Hydrogen Production from Natural Gas Reforming

Feng,

Gu,

Pang

et al. 2024

ChemBioEng Reviews

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The hydrogen production from natural gas has advantages of low investment, low carbon emission, and high hydrogen production rate. This paper briefly describes the technical overview of hydrogen production from natural gas reforming and identifies its risk factors. According to the dangerous characteristics of high reaction temperature, easy leakage of reaction medium, flammability, and explosion in the process, the intrinsic safety of the process is discussed in combination with relevant research and industrial experience. The safety requirements of key equipment and materials are introduced in detail, followed by the optimization methods of process safety that can be taken in the engineering process. Besides, the accident prevention measures for emergency shutdown and fire explosion are summarized. Finally, the future research demands are put forward from the perspective of research and development, which is instructive for the safe hydrogen production from natural gas in the future.

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

et al. 2015

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Process industries have a potential for the occurrence of major accidents. These accidents can have severe adverse effects on human health and the environment and they can cause extensive damage to equipment and buildings. During major process upsets, central control rooms are among the most stressful workplaces in the world. Therefore, Human Reliability Analysis and Dynamic Decision‐Making Styles (DDMSs) play an important role in safety management in these industries. This study employs the intelligent Adaptive Neuro Fuzzy Inference System model associated with two questionnaires along with Cognitive Reliability Error Analysis Method to analyze the Human Reliability Influencing Factors (HRIFs) and DDMSs of the control room operators and to determine the efficiency of operators as well as their dominant and efficient decision‐making styles. Nine influencing factors on human reliability and five DDMSs are evaluated and the correlation between the HRIFs is investigated. Efficiency of the operators, according to the HRIFs, is determined and they are ranked. Next, the most dominant and efficient of the DDMSs among the operators was identified. Finally, an intelligent algorithm for determining the efficiency of a control room's operators is developed. © 2015 American Institute of Chemical Engineers Process Saf Prog 35: 192–199, 2016

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Safety risk modeling and major accidents analysis of hydrogen and natural gas releases: A comprehensive risk analysis framework

Cited by 112 publications

References 26 publications

Dynamic safety risk modeling of process systems using bayesian network

Dynamic safety risk modeling of process systems using bayesian network

Risk Identification and Safety Technology for Hydrogen Production from Natural Gas Reforming

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

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