Date of Award
Master of Science (MS)
Mechanical and Civil Engineering
Peng-Robinson cubic equations of state (PR-EoSs) as one of the most popular two-parameter cubic equations of state (2P-EoSs) are widely used to calculate thermodynamic properties of pure substances and their mixtures. However, the prediction accuracy of 2P-EoSs varies significantly among different substances due to its intrinsic limitation. To this end, many modifications have focused on changing the dependence structure of 𝛼 function with temperature for PR-EoS to enhance prediction accuracy. In this paper, we propose a Bayesian framework to calibrate a new 𝛼 function, which is a bias-corrected parametrized model form for the PR-EoS. The developed PR-EoS with the calibrated 𝛼 function is applied to evaluate the thermodynamic properties of representative substances, including oxygen, carbon dioxide, and n-decane. Results show that the new developed PR EoS significantly improves the prediction accuracy of densities for the representative substances when compared to the original PR EoS.
Chen, Wei, "A Modified Peng-Robinson Cubic Equation of State Based on Bayesian Framework" (2020). Theses and Dissertations. 1003.