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dc.contributor.advisorWang, Xingjian
dc.contributor.authorChen, Wei 2020
dc.descriptionThesis (M.S.) - Florida Institute of Technology, 2020.en_US
dc.description.abstractPeng-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 𝛼function 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.en_US
dc.rightsCC BY NC 4.0en_US
dc.titleA Modified Peng-Robinson Cubic Equation of State Based on Bayesian Frameworken_US
dc.typeThesisen_US of Science in Mechanical Engineeringen_US Engineeringen_US and Civil Engineeringen_US Institute of Technologyen_US

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CC BY NC 4.0
Except where otherwise noted, this item's license is described as CC BY NC 4.0