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Perturbed-Chain SAFT: An Equation of State Based on a Perturbation Theory for Chain Molecules
Abstract
A modified SAFT equation of state is developed by applying the perturbation theory of Barker and Henderson to a hard-chain reference fluid. With conventional one-fluid mixing rules, the equation of state is applicable to mixtures of small spherical molecules such as gases, nonspherical solvents, and chainlike polymers. The three pure-component parameters required for nonassociating molecules were identified for 78 substances by correlating vapor pressures and liquid volumes. The equation of state gives good fits to these properties and agrees well with caloric properties. When applied to vapor−liquid equilibria of mixtures, the equation of state shows substantial predictive capabilities and good precision for correlating mixtures. Comparisons to the SAFT version of Huang and Radosz reveal a clear improvement of the proposed model. A brief comparison with the Peng−Robinson model is also given for vapor−liquid equilibria of binary systems, confirming the good performance of the suggested equation of state. The applicability of the proposed model to polymer systems was demonstrated for high-pressure liquid−liquid equilibria of a polyethylene mixture. The pure-component parameters of polyethylene were obtained by extrapolating pure-component parameters of the n -alkane series to high molecular weights.
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Cited by
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Tunable Hydrophobic Eutectic Solvents Based on Terpenes and Monocarboxylic Acids
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Metadata
- Authors
- Gross, J., & Sadowski, G.
- Venue
- Industrial & Engineering Chemistry Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2001-01-18
- Volume
- 40
- Issue
- 4
- Pages
- 1244-1260
- ISSN
- 0888-5885 (ISSN-L)
- 1520-5045
- OpenAlex
- W2085237940
- Cited by
- 3,541
- References
- 0
- FWCI
- 22.19
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gross, J., & Sadowski, G. (2001). Perturbed-Chain SAFT: An Equation of State Based on a Perturbation Theory for Chain Molecules. Industrial & Engineering Chemistry Research. https://doi.org/10.1021/ie0003887
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.