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Advanced boil-off gas studies for liquefied natural gas

Applied Thermal Engineering, ISSN: 1359-4311, Vol: 189, Page: 116735
2021
  • 34
    Citations
  • 0
    Usage
  • 59
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    34
    • Citation Indexes
      34
  • Captures
    59

Article Description

Current methods of estimating boil-off gas (BOG) rates for large-scale liquefied natural gas (LNG) storage tanks are largely empirical and based on limited available experimental data. More accurate models would be extremely valuable for estimating the potential for excessive BOG generation during LNG storage and transportation scenarios as well as providing critical inputs into the design of BOG re-liquefaction systems. This study reports a series of experiments that have been conducted for LNG-like binary mixtures of methane and ethane to measure the BOG production and resultant pressure change under various industrially relevant conditions. Experimental data and observations made in this work are compared with both the available literature and with the predictions of a new non-equilibrium model that uses the GERG-2008 equation of state to calculate relevant LNG and BOG properties. The data reveal three distinct stages of BOG evolution, here labelled as self-pressurisation, transient, and homogenous. It is observed that, in the self-pressurisation stage, the thickness of a thermally stratified layer adjacent to the liquid–vapor interface increases with time. The transient stage is defined to commence when the system reaches the specified relief pressure and the homogeneous stage is reached upon the effective elimination of thermal stratification in the LNG. Good agreement exists between this new model and the experimental and literature data acquired during the self-pressurisation and homogeneous stages. In the transient stage, the model does not accurately quantify the BOG rate indicating a need to incorporate the effects liquid thermal stratification in future model development.

Bibliographic Details

Saif Z.S. Al Ghafri; Fernando Perez; Ki Heum Park; Liam Gallagher; Liam Warr; Aaron Stroda; Arman Siahvashi; Yonghee Ryu; Sungwoo Kim; Sung Gyu Kim; Yutaek Seo; Michael L. Johns; Eric F. May

Elsevier BV

Energy; Engineering

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