Dynamic reliability assessment of flare systems by combining fault tree analysis and Bayesian networks

Abstract

Flaring is a combustion process commonly used in the oil and gas industry to dispose flammable waste gases. Flare flameout occurs when these gases escape unburnt from the flare tip causing the discharge of flammable and/or toxic vapor clouds. The toxic gases released during this process have the potential to initiate safety hazards and cause serious harm to the ecosystem and human health. Flare flameout could be caused by environmental conditions, equipment failure and human error. However, to better understand the causes of flare flameout, a rigorous analysis of the behaviour of flare systems under failure conditions is required. In this article, we used fault tree analysis (FTA) and the dynamic Bayesian network (DBN) to assess the reliability of flare systems. In this study, we analysed 40 different combinations of basic events that can cause flare flameout to determine the event with the highest impact on system failure. In the quantitative analysis, we use both constant and time-dependent failure rates of system components. The results show that combining these two approaches allows for robust probabilistic reasoning on flare system reliability, which can help improving the safety and asset integrity of process facilities. The proposed DBN model constitutes a significant step to improve the safety and reliability of flare systems in the oil and gas industry.

Fault Diagnosis of Train Network Control Management System Based on Dynamic Fault Tree and Bayesian Network

“…Halabi et al [22] presented the BN of reliability and risk model, which is modeled in the GeNIe version 2.0. Kabir et al [26] built the DBN of the FT model for the flare system, which is based on GeNIe software. Rahman et al [28] built the modified BN model for marine offshore logistics operation, which is based on GeNIe software, to address the interdependencies and conditional relationships among the critical factors.…”

“…Bobbio et al [25] have introduced the basic inference techniques of FT mapped into a BN, which can obtain some additional power both at the modeling and at the analysis level. Kabir et al [26] have applied the fault tree analysis and the dynamic Bayesian network to assess the reliability of flare systems. Khakzad et al [27] have presented the application of BNs in safety analysis and introduced the similarity between FTs and BNs.…”

A review on the realization methods of dynamic fault tree

“…In addition, it can be combined with fuzzy theory, expert knowledge and FTA methods to realize the risk assessment of complex systems with fuzzy failure data. BN can solve the deficiency of DFT traditional method very well 90,91 …”

“…BN can solve the deficiency of DFT traditional method very well. 90,91 The main advantage of BN lies in the probability mechanism of network belief dissemination and update. This capability is usually based on expert knowledge or additional information that becomes accessible during the process life cycle, including events, near misses, accidents, accidents, and so on.…”

A Novel Approach Based on Stochastic Hybrid Fault Tree to Compare Alternative Flare Gas Recovery Systems

“…The application of hybrid approaches for the probability analysis of safety and risk of hazardous industries is a significantly hot topic of literature and, as stated in [40], they are becoming more meaningful because can combine both qualitative and quantitative knowledge. Although literature presents works on the dependability analysis of flare gas recovery systems [41], [42] and on the simulation of the working conditions [43], [44], to the authors' knowledge, the combination of these two matters has never been studied. Especially, the studies have only been conducted for the FGRS itself and not for the corresponding systems, one of which being a gas control system (GCS).…”