Incorporating CREAM and MCS into fault tree analysis of LNG carrier spill accidents

Abstract Liquefied Natural Gas (LNG) is a clean, efficient and economic energy, which is mainly transported by LNG carriers in the marine industry. Possible spilling of cryogenic LNG puts safety of crew in danger due to fire and explosion hazards. Taking into account various uncertainties, a modified fault tree model for LNG spill accident during LNG carriers’ handling operations is constructed in this study. Human factor analysis is introduced with a goal to predict human errors in LNG carriers’ handling operation. Finally, the results of the Fault Tree Analysis (FTA) and Human Reliability Analysis (HRA) are combined, so that risks can be assessed using Monte Carlo Simulation (MCS). Comparing the results of risk assessment with the traditional FTA and corresponding criterion set, an important reference for LNG carrier management is provided for administration.

[1]  Zaili Yang,et al.  Adoption of new advanced computational techniques to hazards ranking in LNG carrier operations , 2013 .

[2]  C. H. Lie,et al.  Fault Tree Analysis, Methods, and Applications ߝ A Review , 1985, IEEE Transactions on Reliability.

[3]  Jin Wang,et al.  Application of genetic algorithm to risk-based maintenance operations of liquefied natural gas carrier systems , 2011 .

[4]  Takehisa Kohda,et al.  A simple method to derive minimal cut sets for a non-coherent fault tree , 2006, Int. J. Autom. Comput..

[5]  Jun Liu,et al.  Analysis of subway station capacity with the use of queueing theory , 2014 .

[6]  Joaquim Casal,et al.  A dispersion safety factor for LNG vapor clouds. , 2013, Journal of hazardous materials.

[7]  P. Nekså,et al.  Development and analysis of a natural gas reliquefaction plant for small gas carriers , 2010 .

[8]  Daejun Chang,et al.  Fuzzy reliability analysis of dual-fuel steam turbine propulsion system in LNG carriers considering data uncertainty , 2015 .

[9]  Robin Pitblado.  Potential for BLEVE associated with marine LNG vessel fires. , 2007, Journal of hazardous materials.

[10]  Erik Hollnagel,et al.  Cognitive reliability and error analysis method : CREAM , 1998 .

[11]  Witold Gierusz.  Simulation model of the LNG carrier with podded propulsion Part 1: Forces generated by pods , 2015 .

[12]  P. Zalewski.  Risk assessment of LNG carrier systems failure using fuzzy logic , 2011 .

[13]  Pedro Antão,et al.  Analysing the risk of LNG carrier operations , 2008, Reliab. Eng. Syst. Saf..

[14]  Yan Li.  Human reliability analysis in ship collision avoidance , 2003 .

[15]  Anay Luketa-Hanlin,et al.  A review of large-scale LNG spills: experiments and modeling. , 2005, Journal of hazardous materials.

[16]  Daejun Chang,et al.  Fire risk comparison of fuel gas supply systems for LNG fuelled ships , 2015 .

[17]  Tarek A. Elsayed,et al.  Risk assessment of liquefied natural gas carriers using fuzzy TOPSIS , 2014 .

[18]  M.R. Zoolfakar,et al.  Holistic Study of Liquefied Natural Gas Carrier Systems , 2014, Complex Adaptive Systems.

[19]  Roberto Bubbico,et al.  Preliminary risk analysis for LNG tankers approaching a maritime terminal , 2009 .

[20]  Dedy Ng,et al.  Numerical simulations of LNG vapor dispersion in Brayton Fire Training Field tests with ANSYS CFX. , 2010, Journal of hazardous materials.

[21]  T. G. Theofanous,et al.  Risk assessment of membrane type LNG storage tanks in Korea-based on fault tree analysis , 2005 .

[22]  Jinshu Lu,et al.  Numerical prediction of temperature field for cargo containment system (CCS) of LNG carriers during pre-cooling operations , 2016 .