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 behavior 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 analyzed 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.

KEYWORDS:

• Reliability
• fault tree analysis
• ecological risk
• Bayesian network
• flare system

Additional information

Notes on contributors

Sohag Kabir

Sohag Kabir received the Ph.D. degree in computer science and the M.Sc. degree in embedded systems from the University of Hull, UK, in 2016 and 2012, respectively. He is currently working as a Lecturer (Assistant Professor) in the Department of Computer Science at the University of Bradford, UK. Prior to that, he was a research associate in the Dependable Intelligent Systems (DEIS) Research Group at the University of Hull. He has worked in EU projects on safety, including MAENAD and DEIS. His research interests include model-based safety assessment, probabilistic risk and safety analysis, fault tolerant computing, and stochastic modelling and analysis.

Mohammed Taleb-Berrouane

Mohammed Taleb-Berrouane completed his PhD at the Institute of Maintenance and Industrial Safety in collaboration with the Centre for Risk, Integrity and Safety Engineering (C-RISE) in the Faculty of Engineering & Applied Science at Memorial University of Newfoundland, St. John's NL, Canada. From 2015, he has been involved in teaching and research works at Memorial University and the C-RISE. He has several years of fieldwork experience in the process industry. His research interests focus on accident modelling, risk analysis, safety & reliability assessment.

Yiannis Papadopoulos

Yiannis Papadopoulos is a professor and leader of the Dependable Intelligent Systems research group at the University of Hull. Professor Papadopoulos has pioneered work on model-based dependability assessment and evolutionary optimisation of complex engineering systems known as Hierarchically Performed Hazard Origin and Propagation Studies (HiP-HOPS). He co-authored EAST-ADL, an emerging automotive architecture description language working with Volvo, Honda, Continental, Honeywell, and DNV-GL, among others. He is actively involved in two technical committees of IFAC (TC 1.3 & 5.1). He is also working on new metaheuristics inspired by the hunting behaviour of penguins and developing technologies for self-certification of cyber-physical and autonomous systems. He is interested in digital art and various aspects of philosophy and its interactions with science.