Method for Estimating Activity Coefficients of Target Components in Poorly Specified Mixtures-Reference-Cited by-同舟云学术

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Method for Estimating Activity Coefficients of Target Components in Poorly Specified Mixtures

Published:2018-05-16 Issue:21 Volume:57 Page:7310-7313
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Jirasek Fabian¹^ORCID,Burger Jakob²^ORCID,Hasse Hans¹

Affiliation:

1. Laboratory of Engineering Thermodynamics (LTD), University of Kaiserslautern, Erwin-Schrödinger-Straße 44, 67663 Kaiserslautern, Germany

2. Chair of Chemical Process Engineering, Technical University of Munich, Schulgasse 16, 94315 Straubing, Germany

Funder

Horizon 2020 Framework Programme

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.8b00917

Reference16 articles.

1. Group-contribution estimation of activity coefficients in nonideal liquid mixtures

2. A modified UNIFAC model. 1. Prediction of VLE, hE, and .gamma..infin.

3. Further Development of Modified UNIFAC (Dortmund): Revision and Extension 6

4. Conductor-like Screening Model for Real Solvents: A New Approach to the Quantitative Calculation of Solvation Phenomena

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1. HybridGamma: A thermodynamically consistent framework for hybrid modelling of activity coefficients;Chemical Engineering Journal;2023-11

2. Automated nuclear magnetic resonance fingerprinting of mixtures;Magnetic Resonance in Chemistry;2023-07-29

3. Predictive Thermodynamic Modeling of Poorly Specified Mixtures and Applications in Conceptual Fluid Separation Process Design;Industrial & Engineering Chemistry Research;2023-06-28

4. Rational method for defining and quantifying pseudo-components based on NMR spectroscopy;Physical Chemistry Chemical Physics;2023

5. Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification;Journal of Chemical Information and Modeling;2021-01-06

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