Journal of Health and Safety at Work

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Introduction: Process industries, often work with hazardous and operational chemical units with high temperature and pressure conditions, such as reactors and storage tanks. Thus, probabilities of incidence such as explosions, and fire are extremely high, The purpose of this study was to present a comprehensive and efficient method for the quantitative risk assessment of fire and explosion in the process units.

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Material and Method: The proposed method in this study is known as the QRA and includes seven steps. After determination of study objectives and perfect identification of study process, first, qualitative methods are used to screen and identify hazard points and the possible scenarios appropriate are identified and prioritized. Then, estimation of frequency rate are done using past records and statistics or Fault Tree Analysis along whit Event Tree. PAHST professional software and probit equations are used in order to consequence modeling and consequence evaluation, respectively. In the last step by combination of consequence and frequency of each scenario, individual and social risk and overall risk of process or under study unit was calculated.

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Result: Applying the proposed method showed that the jet fire, flash fire and explosion are most dangerous consequence of hydrogen generation unit. Results showed that social risk of the both fire and explosion caused by full bore rupture in Desulphrizing reactor (Scenari3), Reformer (scenario 9) and Hydrogen purification absorbers are unacceptable. All of the hydrogen generation unit fall in ARARP zone of fire individual risk (FIR) and FIR up to 160 m of boundary limit unit is unacceptable. This distance is not only beyond of hydrogen generation unit boundary limit, but also beyond of complex boundary limit. Desulphurization Reactor (75%) and Reformer (34%) had the highest role in explosion individual risk in the control room and their risks are unacceptable.

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Conclusion: Since the proposed method is applicable in all phases of process or system design, and estimates the risk of fire and explosion by a quantitative, comprehensive and mathematical-based equations approach. It can be used as an alternative method instead of qualitative and semi quantitative methods.

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Introduction: In process industries, some of the primary events may result in secondary events in an industrial unit called the domino effect. Since refinery storage tanks are always at risk of fire and explosion, quantitative risk assessment is important in determining the severity and outcome of an accident, taking into account the effects of dominoes on the main industry, neighbors, and society and can play an important role in risk management. Therefore, the purpose of this study was to quantitatively evaluate the risk of condensate storage tanks taking into account the domino effect.
Material and Methods: The technique used in this study was Quantitative Risk Assessment (QRA), the analysis of the consequences of which was performed using PHAST (7.22) after setting goals, studying the process, identifying hazards and scenarios. Then, to determine the extent of the domino effects of the escalation vectors were matched against the threshold, and after screening, the overall vulnerability of the repositories for mapping individual risk levels was calculated.
Results: In the leakage scenario, after considering the domino effects, the risk contour 10-4 to about 250 meters and the risk contour 10-5 to about 400 meters increased. Also in the catastrophic rupture scenario, the radius of risk contour of the 10-5 increased to100 m after considering the domino effects up to around damage tank.
Conclusion: As can be deduced from the results, using this method can give a clear picture of the consequences of chain events and the probability of damage to nearby employees, equipment and neighbors, which is very important in risk, emergency and crisis management.

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Introduction: Pipelines are widely used to transport large volumes of oil and gas over long distances. Risk assessment can help identify risk factors and create an appropriate action plan and strategy to reduce or eliminate them. The main goal of this research is to provide a method for assessing the risk of pipelines based on the Fuzzy Inference System (FIS), creating a systematic format that is expected to be a more effective, accurate, and reliable model for controlling risks related to oil and gas pipelines.
Material and Methods: In this article, fuzzy logic is used to model uncertainty and present a model for assessing pipeline risk. The Muhlbauer method, one of the most common risk assessment methods for oil and gas pipelines, has been employed to determine critical factors affecting the lines. This method has been implemented using the Mamdani algorithm and based on expert knowledge in the fuzzy logic toolbox of MATLAB software. To validate the results of the proposed model, data from the interphase pipelines of the fifth refinery of the South Pars Gas Field have been used as a study sample.
Results: The findings from the implementation of the model created in South Pars Phases 9-10 pipelines (on shore) show that the studied pipelines are divided into three parts (A, B, and C) based on indicators such as population density and equipment deployment. Part C of the pipeline has the highest risk, with third-party damage and design being the most important factors affecting it. Part B has the lowest level of risk and results in the fewest consequences for human accidents. It was also observed that corrosion is essential in increasing leakage and risk in all three pipeline parts.
Conclusion: To verify the developed model, the inter-phase shore pipe of phase 9-10 refinery in the South Pars Gas Field was considered as a case study. The findings indicate that the proposed method provides more accurate and reliable results than traditional methods. Factors such as improper operation, dispersion, receptors, leakage volume, and product risk, which are other factors affecting pipeline risk, were not considered in traditional methods. Therefore, the risk level of oil and gas pipelines can be calculated using this model as a comprehensive and intelligent tool.