4.7 Article

Evaluating cubic equations of state for predictions of solid-fluid equilibrium in liquefied natural gas production

Journal

FUEL
Volume 314, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2021.123033

Keywords

Cubic equation of state; Liquefied natural gas; Solid fluid equilibrium

Funding

  1. Australian Research Council through the Industrial Transformation Training Centre for LNG Futures [IC150100019]
  2. Australian Government
  3. American Australian Association

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By adjusting the thermodynamic model, solid formation and deposition in LNG can be accurately predicted using cubic equations of state. Peng-Robinson (PR) equation performed the best in representing the solid-fluid equilibrium temperatures for most mixtures. The solubility of various impurities in methane at different temperatures can be studied using this model.
Solid formation and deposition in liquefied natural gas (LNG) production is a process hazard avoidable by careful determination of impurity solubility in the process stream. Accurate freeze-out predictions for LNG require robust thermodynamic models tuned to high-quality experimental data. Cubic equations of state (EoS) can be used to calculate solid-fluid equilibria (SFE) for LNG-relevant binary mixtures with the necessary accuracy, while retaining the computational efficiency ideal for process simulations. A comprehensive literature survey of the available experimental SFE data of LNG-relevant binary mixtures was carried out and used to determine the optimal binary interaction parameters for a range of commonly-used cubic EoS. Overall, the Peng-Robinson (PR) EoS performed best representing the SFE temperatures with a standard uncertainty of less than 2 K for most of the examined mixtures; the Soave-Redlich-Kwong (SRK), Peng-Robinson-Stryjek-Vera (PRSV) and Patel-Teja-Valderrama (PTV) equations produced similar results. The solubility of various impurities in methane was investigated with the optimized EoS across a range of temperatures, and various retrograde behaviors were examined. The EoS models and the best-fit parameters obtained in this work have been incorporated into the ThermoFAST desktop application and a new free online implementation ThermoFAST Web.

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