Process safety analysis using operational data and Bayesian network

A coupling model based on hazard and operability analysis (HAZOP) and fault tree analysis is proposed and used to analyze the failure of a low‐temperature distillation station (LTDS). It is found that the formation of scale in the deamination tower in the LTDS is the most significant cause for the failure. The scale was mainly composed of SrSO4, CaCO₃, CaSO4, and MgCO₃, which were determined by various techniques from the gas‐field water. Dynamic weight loss experiments were conducted to estimate the corrosion rate of 304 steel with different cleaning solutions. The corrosion behaviors of the metal in the deamination tower were further investigated in three types of cleaning solution via potentiodynamic polarization method. The cleaning solution composed of 6% sulfamic acid, LAN826, JFC, and a defoaming agent could safely and quickly remove the scale covering the LTDS at 50°C. In addition, the optimal cleaning time was found to be 6 h.

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“…The posterior probability is calculated by the following equations 25 :

P (B_{i}| A_{j}) = \frac{P (B_{i}) P (A_{j}| B_{i})}{{false\sum}_{i, j = 1}^{n} P (B_{i}) P (A_{j}| B_{i})},

where A and B represent the two events in the event set.…”

Section: Methodsmentioning

confidence: 99%

Failure analysis and quick recovery of a low‐temperature distillation station

Xiao

Huang

et al. 2023

Process Safety Progress

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“…For example, principal component analysis is used for fault detection and diagnosis (FDD), while Bayesian networks determine the probability of a system failure once a fault is detected. 23 Similarly, Markov chains, simulations (e.g., Monte Carlo), Petri nets, and so on, have been applied to the field to address the limitations of traditional methods. 24 The major methods mentioned above and their characteristics are shown in Table 1.…”

Section: Khan and Abbasimentioning

confidence: 99%

“…Along with that, the BN approach has wide use for accident causation analysis, with more combination with other methods. For example, principal component analysis is used for fault detection and diagnosis (FDD), while Bayesian networks determine the probability of a system failure once a fault is detected 23 . Similarly, Markov chains, simulations (e.g., Monte Carlo), Petri nets, and so on, have been applied to the field to address the limitations of traditional methods 24 .…”

Section: Literature Reviewmentioning

confidence: 99%

Significance ranking and correlation identification of accident causes in process industry based on system thinking and statistical analysis

Zhang

Zhong

Shi

et al. 2023

Process Safety Progress

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Accidents in process industry occur frequently with serious casualties and property losses. This paper builds an accident causation model of process industry based on system thinking by dividing the accident causation system into 4 subsystems and 22 factors. A combination of grey relational analysis and correspondence analysis is conducted to carry out a structured analysis of the collected data. The research contains three main parts: (1) Grey relational analysis is used to obtain the significance ranking of 22 cause factors in process industry, and three critical cause factors are identified as “Security inspection,” “Risk identification,” and “Security awareness.” (2) Through correspondence analysis, the correlations between three sets of variables are analyzed and the cause factors requiring focused attention are identified as “Electric spark,” “Temperature,” “Raw material control,” “Punching phenomenon,” “Equipment clogging,” and “Combustible gases.” (3) An intelligent monitoring scheme is developed for the critical factors of each subsystem, which aims to achieve real‐time monitoring and early warning by means of video surveillance and sensor placement for the human, equipment, and environment subsystems. The conclusions obtained from this study can be used to enhance the efficiency of safety management and reduce the probability of accident occurrence in the process industry.

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“…The dynamic risk assessment (DRA) method is regarded as an important tool for assessing the risk of abnormal situations in chemical processes [100]. Some researchers established dynamic BN by introducing time variables to evaluate the risks of dynamic systems [101,102]. Some other researchers have proposed DRA methods based on Petri nets, representing the dynamic behavior of complex systems by introducing time constraints in transitions.…”

Section: Dynamic Risk Assessmentmentioning

confidence: 99%

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

et al. 2023

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In the chemical process, abnormal situations are precursor events of incidents and accidents. Abnormal situation management (ASM) can effectively identify abnormalities and prevent them from evolving into incidents or accidents, ensuring the safe and smooth operation of chemical plants. In recent years, ASM has attracted extensive attention from the process industry and from academia, and a lot of research work has been conducted. However, the intelligence level of ASM in actual chemical plants is still relatively low, and industrial applications still face many difficulties and challenges. This review first summarizes the concepts and involved in the contents of ASM. Then, the latest research progress in various aspects of ASM is systematically reviewed. Finally, the challenges and future research directions of ASM are analyzed based on the perspective of industrial application. This review aims to provide the most cutting-edge reference for follow-up research on ASM, and to promote the intelligent development and practical industrial application of ASM in the chemical process.

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Process safety analysis using operational data and Bayesian network

Cited by 24 publications

References 39 publications

Failure analysis and quick recovery of a low‐temperature distillation station

Failure analysis and quick recovery of a low‐temperature distillation station

Significance ranking and correlation identification of accident causes in process industry based on system thinking and statistical analysis

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

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