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- W4384789716 abstract "When selecting candidate solvents for extractive distillation, activity coefficients at infinite dilution are commonly employed. With them, the selectivity of different solvents for a given mixture can be estimated, and the solvents can be ranked accordingly. However, given the large chemical space of potential solvents and the limited experimental data for activity coefficients at infinite dilution, predictive methods become necessary to perform a solvent pre-selection process across a broad solvent space. In this work, a method for selecting solvents using the Gibbs-Helmholtz Graph Neural Network as predictive model for infinite dilution activity coefficients is presented. Different case-studies are given to illustrate the efficacy of this methodology ranging from aliphatic-aromatic separations to the separation of olefin-paraffin mixtures. The extended Margules equation is employed to estimate the vapor-liquid equilibria of the system of interest allowing the minimum solvent-to-feed ratio to be estimated. The results show that industrially relevant solvents are selected for different type of mixtures while exploring a much larger solvent space compared to the one delimited by the available experimental data. Also, it is shown that the pre-selection of solvents based on infinite dilution conditions differs from the one performed using the minimum solvent-to-feed ratio criterion. The latter is recommended because it approximates the conditions usually encountered in a real extractive distillation column." @default.
- W4384789716 created "2023-07-20" @default.
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- W4384789716 date "2023-01-01" @default.
- W4384789716 modified "2023-09-26" @default.
- W4384789716 title "Solvent pre-selection for extractive distillation using Gibbs-Helmholtz Graph Neural Networks" @default.
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- W4384789716 doi "https://doi.org/10.1016/b978-0-443-15274-0.50324-3" @default.
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