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An extension of cubic equations of state to vapor-liquid equilibria in polymer-solvent mixtures
Institution:1. Institute of Applied Physics, Jiangxi Academy of Sciences, 330012 Nanchang, China;2. Institute of Nanotechnology, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany;3. Herbert Gleiter Institute of Nanoscience, Nanjing University of Science and Technology, 210094 Nanjing, China;4. KIT-TUD Joint Research Laboratory Nanomaterials, Technische Universität Darmstadt, 64287 Darmstadt, Germany;1. School of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran;2. School of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
Abstract:Cubic equations of state (EOS) are extended to describe polymer-solvent vapor-liquid equilibria (VLE). The solvents are described the conventional way using critical parameters. To describe the pure polymers, only the weight-average molecular weight is necessary, though number-average molecular weight, polydispersity and melt density can be incorporated if desired. To extend the model to mixtures, a mixing rule that combines EOS with excess energy models is used. In this formulation, the excess Gibbs energy term is considered in two parts: the classical Flory term for the entropic contributions and a residual term that takes care of specific interactions between the solvent and the polymer. For athermal mixtures that exhibit no such interactions, the residual term drops out and the model becomes completely predictive. Otherwise, for residual contributions, depending upon the complexity of specific molecular interactions anticipated in the mixture, either a single parameter Flory expression or a two-parameter NRTL equation can be used. We conclude that the simple cubic EOS approach presented here is easy to use, yet competes successfully with more sophisticated EOS models developed particularly for polymer solutions. Moreover, it offers more flexibility if one or more parameters are to be tuned to the process data.
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