Application of the PC‐SAFT Equation of State to the Prediction of Vapor Solubility in Semicrystalline Polyethylenes

This paper builds on the theoretical framework of the previous articles on the solubility of vapors in semicrystalline polyethylenes produced in the gas phase process. The present article clarifies the theoretical basis for an activity coefficient approach, which results from a constraint on the amo...

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Veröffentlicht in:Macromolecular reaction engineering 2022-12, Vol.16 (6), p.n/a
Hauptverfasser: Moebus, Joseph A., Greenhalgh, Brian R.
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Sprache:eng
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Zusammenfassung:This paper builds on the theoretical framework of the previous articles on the solubility of vapors in semicrystalline polyethylenes produced in the gas phase process. The present article clarifies the theoretical basis for an activity coefficient approach, which results from a constraint on the amorphous phase within semicrystalline polymers. This concept is coupled to an advanced equation of state for use in polyolefin reaction engineering, and presented in a modular way the procedure for computing the requisite thermodynamic quantities. A temperature dependence on polymer crystallinity is also introduced. In the interest of developing a more predictive model for solubility, the model using single pure gas isotherms are parameterized. The results demonstrate the ability to predict single and mixed gas absorption, including new data published herein. The validity of the model is further demonstrated through comparisons with literature studies on batch scale ethylene polymerization. Finally, how a simple correlation to standard polymer characteristics yields accurate predictions in the absence of measured data for parametrization is demonstrated. In this article a previously reported activity coefficient description of the amorphous phase is coupled to an advanced equation of state and a temperature dependent polymer crystallinity term. Parameterization to pure gas isotherms yields accurate predictions of pure and mixed gas absorption. Finally, the use of a simple correlation yielding accurate predictions in the absence of measured data is demonstrated.
ISSN:1862-832X
1862-8338
DOI:10.1002/mren.202200017