Functional Resonance Analysis Method (FRAM)-based minimum spanning tree for identifying safety critical points and designing safety barriers in LNG off-loading operations

The off-loading process during the transportation of Liquefied Natural Gas (LNG) is a complex and high-risk operation. Therefore, conducting a safety analysis is critical to identify safety points and implement targeted safety measures. Standard safety analysis methods such as fault tree analysis and event tree analysis are based on a theoretical approach called Work As Imagine (WAI) mode. Specifically, the task and accident processes are often designed by people rather than reflecting actual practices, which means that WAI mode struggles to account for many unpredictable scenarios. An alternative method, the Function Resonance Analysis Method (FRAM) based on the Work-As-Done (WAD) can consider unpredictable situations by analyzing the couplings among different functions required in the task. However, due to the complexity of the tasks involved in LNG off-loading work, the FRAM model can also be complicated. Therefore, this study proposes a solution to simplify this complex model. The study first uses FRAM to construct a comprehensive network model in the LNG off-loading process. Then, Prim's algorithm is applied to provide a minimum spanning tree (MST) analysis to simplify this complex model and locate the safety critical point. In addition, as there is a lack of source data for Prim's algorithm application, the definition of risk as support to collect PSF data through two aspects of likelihood and severity, to ensure the data is explainable. The results show that this proposed method provides a better functional view for locating safety-critical points and barrier-making in a real engineering case of shipping LNG off-loading work, and effectively reduces the complexity of the LNG off-loading system. This study will help to enhance safety of LNG off-loading operations.