Usage
  • 2 views
  • 1 download

Comparison of the Prediction Accuracy of Isothermal Compressibility and Isobaric Thermal Expansivity by Different Volume-Translated Equations of State

  • Author / Creator
    Guan, Jingyuan
  • Isothermal compressibility, κ_T, and isobaric thermal expansivity, α_P, are two important parameters that are used in the simulation and modeling of many petroleum and chemical processes. κ_T and α_P can be calculated by empirical correlations or thermodynamic models, but these predicting methods are not sufficiently accurate. Cubic equations of state (CEOSs) are widely used in the petroleum and chemical industry to describe the phase behavior of fluids. It is recognized that one of the deficiencies of CEOSs is their inaccuracy in predicting liquid-phase volumes. The volume translation (VT) strategy was then proposed by researchers to overcome this deficiency, which could significantly improve the performance of CEOSs in predicting volumetric properties. With recent developments in the volume-translated equations of state (VT-EOSs), κ_T and α_P should also be predicted more accurately by VT-EOSs. In this work, κ_T and α_P of two example fluids (i.e., methane and carbon dioxide) are predicted by seven different VT models: one constant VT model, two linear temperature-dependent VT models, two exponential temperature-dependent VT models, and two temperature-pressure-dependent VT models (i.e., models based on a distance function). The accuracy of each model is evaluated by comparing the predictions with the pseudo-experimental data for the liquid phase, the vapor phase, and the supercritical phase. The predicted results show that the distance-function-based temperature-pressure-dependent VT models exhibit relatively better performance in predicting κ_T and α_P than the temperature-dependent and the constant VT models. Overall, the VT-PR EOS model proposed by Abudour et al. (2012) provides the most accurate predictions of κ_T, while the VT-SRK EOS model proposed by Chen and Li (2020) provides the most accurate predictions of α_P.

  • Subjects / Keywords
  • Graduation date
    Fall 2021
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-1fmr-hn84
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.