A new approach to chemicals warehouse risk analysis using computational fluid dynamics simulation and fuzzy Bayesian network

Jafari, Mohammad Javad; Pouyakian, Mostafa; Mozaffari, Parvaneh; Laal, Fereydoon; Mohamadi, Heidar; Pour, Masoud Taheri; Hanifi, Saber Moradi

doi:10.1016/j.heliyon.2022.e12520

Cited by 6 publications

(6 citation statements)

References 61 publications

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“…Static structure, lack of flexibility to combine new knowledge and evidence, lack of updating the probabilities of basic events, lack of ability to reduce data uncertainty and use rare information, and the inability to consider the conditional dependence of the basic events are among the shortcomings of the Bow-Tie method [ 14 , 22 , 58 ] that should be fixed. Through the combination of the FBN with quantitative or qualitative risk assessment techniques, a higher level of precision and effectiveness can be achieved in process safety assessment studies while mitigating uncertainties [ 44 , 59 ]. Currently, the existing approaches in risk assessment should consider this capability due to the dynamics of variables affecting process accidents.…”

Section: Discussionmentioning

confidence: 99%

“…The FBN has been widely acknowledged as a reliable methodology for assessing safety across various industrial and process incidents [ 1 , 14 , 16 , 44 ]. The FBN can leverage Bayes theory as a means to continually reassess the likelihood of fundamental events transpiring based on the acquisition of fresh evidence, such as incident occurrence statistics, real-time process monitoring data, and near-misses.…”

Section: Methodsmentioning

confidence: 99%

“…The FBN can leverage Bayes theory as a means to continually reassess the likelihood of fundamental events transpiring based on the acquisition of fresh evidence, such as incident occurrence statistics, real-time process monitoring data, and near-misses. This enables the calculation of updated probabilities [ 44 , 45 ].…”

Section: Methodsmentioning

confidence: 99%

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Providing a comprehensive approach to oil well blowout risk assessment

Satiarvand,

Orak,

Varshosaz

et al. 2023

PLoS ONE

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Drilling is one of the most dangerous activities in the oil and gas process industries. Therefore, a holistic approach was presented to prevent and control risks and reduce the uncertainty of blowouts, kick scenarios, and control layers based on the Fuzzy Bayesian Network (FBN). Four independent protection layers (IPLs) were used to evaluate the blowout outcome, and the failure probabilities of IPL1 and IPL2 were calculated with Fault Tree Analysis (FTA). Then, different events were transferred to the Bayesian Network (BN) in GeNIe software, and updated the probabilities. The Fuzzy Fault Tree (FFT) results reveal that the failure probabilities for IPL1 and IPL2 amount to 8.94×10−4 and 4.97×10−21, respectively. Also, the blowout probability results based on FFT were larger than FBN. According to FBN, the probability of the kick event was equal to 6.60×10−34. Sensitivity analysis showed that X1 (Mud volume/flow change) had the highest degree of importance in the blowout of oil wells. The results of this study can be used in both a preventive and reactive approach. Deductive and inductive reasoning, the dynamic nature and conditional dependencies, and causal relationships between events can make the model more realistic.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Providing a comprehensive approach to oil well blowout risk assessment

Satiarvand,

Orak,

Varshosaz

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

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“…References [15–17] presented risk assessment methodologies to identify, analyze, and evaluate the risk associated with different consequences of gas process facilities or pipelines. By mapping BT to BN, Reference [18] proposed a risk assessment model for storage tank leakage. Reference [19] proposed a novel risk assessment method for the petrochemical industry.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of liquid ammonia tanks based on Bayesian network and Probit model

Zhang,

Wang,

Chen

et al. 2023

Process Safety Progress

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This paper presents a quantitative risk assessment method to predict the potential risks of liquid ammonia tanks. The method combines probabilistic prediction and consequence assessment: the former is predicted by Bayesian networks derived from bow‐tie mapping, in which the conditional probabilities are determined by analytic hierarchy process, the latter is calculated based on numerical simulation of accident consequences and Probit probability model. The application results show that the method can be used for quantitative risk assessment of liquid ammonia storage tank, and the Bayesian network model is efficient and stable, which can realize risk prediction, accident causes analysis and risk mitigation analysis. Human errors during production or maintenance and corrosion are the main causes of liquid ammonia leakage. Toxicity has the greatest effect on the risk of liquid ammonia tanks. Risk mitigation measures can significantly reduce individual risk and societal risk. The obtained results provide meaningful guidance for the prevention and risk mitigation strategies of liquid ammonia leakage accidents, so as to improve the safety level of liquid ammonia during storage.

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“…To conduct a safety risk assessment, key risk factors affecting laboratory safety should be identified and analyzed. The main methods of risk identification and analysis are the Questionnaire Survey method (QA) [4], Failure Mode and Effect Analysis (FMEA) [5], Failure Analysis Tree (FAT) [6], Fuzzy Bayesian Network (FBN) [7], Bow-Tie technique (BT) [8], Human Factors and Classification System (HFACS) [9] and so on. Among them, methods such as QA, HFACS, FAT, and FMEA need to organize different participants to conduct causal analysis together when applying them and require higher expertise.…”

Section: Introductionmentioning

confidence: 99%

Laboratory Risk Assessment Based on SHELL-HACCP-Cloud Model

Zhao,

Wei,

Gao

et al. 2023

Sustainability

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With the increasing demand and expanding scale of laboratories in colleges and universities, laboratory accidents frequently occur, seriously impacting personal health, schools, society, and the environment. Scientific and effective risk assessment is crucial to prevent accidents. Therefore, in order to effectively reduce the risk of chemical laboratories and minimize the frequency of accidents. This study employs the combination of the SHELL model and HACCP system to analyze the potential sources of hazards in hazardous chemical laboratories and establish a risk assessment index system. Based on the Cloud model, a dynamic risk assessment model for the laboratory is established to quantitatively evaluate the risk level of the evaluation results. In order to ensure the rationality of the assessment results, the subjective and objective weights are combined by the principle of minimizing information entropy. Case analysis proves the scientific validity of the evaluation results of the model, which can assist laboratory managers in formulating emergency plans and risk management mechanisms to reduce or eliminate the occurrence of experimental accidents. This approach ensures the safe and sustainable development of schools and laboratories, which is conducive to the progress of researchers’ scientific research results.

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A new approach to chemicals warehouse risk analysis using computational fluid dynamics simulation and fuzzy Bayesian network

Cited by 6 publications

References 61 publications

Providing a comprehensive approach to oil well blowout risk assessment

Providing a comprehensive approach to oil well blowout risk assessment

Risk assessment of liquid ammonia tanks based on Bayesian network and Probit model

Laboratory Risk Assessment Based on SHELL-HACCP-Cloud Model

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