Advancing thermodynamic group-contribution methods by machine learning: UNIFAC 2.0

Hayer, Nicolas; Wendel, Thorsten; Mandt, Stephan; Hasse, Hans; Jirasek, Fabian

doi:10.1016/j.cej.2024.158667

Advancing thermodynamic group-contribution methods by machine learning: UNIFAC 2.0

Journal Article · Wed Jan 01 00:00:00 EST 2025 · Chemical Engineering Journal

DOI:https://doi.org/10.1016/j.cej.2024.158667· OSTI ID:2483702

; Wendel, Thorsten; ; ;

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Sponsoring Organization:: USDOE

OSTI ID:: 2483702

Journal Information:: Chemical Engineering Journal, Journal Name: Chemical Engineering Journal Journal Issue: C Vol. 504; ISSN 1385-8947

Publisher:: ElsevierCopyright Statement

Country of Publication:: Switzerland

Language:: English

References (19)

Prediction of Henry's law constants by matrix completion Hayer, Nicolas; Jirasek, Fabian; Hasse, Hans AIChE Journal, Vol. 68, Issue 9 https://doi.org/10.1002/aic.17753	journal	June 2022
Group-contribution estimation of activity coefficients in nonideal liquid mixtures Fredenslund, Aage; Jones, Russell L.; Prausnitz, John M. AIChE Journal, Vol. 21, Issue 6 https://doi.org/10.1002/aic.690210607	journal	November 1975
Predicting Activity Coefficients at Infinite Dilution for Varying Temperatures by Matrix Completion Damay, Julie; Jirasek, Fabian; Kloft, Marius Industrial & Engineering Chemistry Research, Vol. 60, Issue 40 https://doi.org/10.1021/acs.iecr.1c02039	journal	September 2021
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion Jirasek, Fabian; Alves, Rodrigo A. S.; Damay, Julie The Journal of Physical Chemistry Letters, Vol. 11, Issue 3 https://doi.org/10.1021/acs.jpclett.9b03657	journal	January 2020
UNIFAC parameter table for prediction of liquid-liquid equilibriums Magnussen, Thomas; Rasmussen, Peter; Fredenslund, Aage Industrial & Engineering Chemistry Process Design and Development, Vol. 20, Issue 2 https://doi.org/10.1021/i200013a024	journal	April 1981
Vapor-liquid equilibriums by UNIFAC group contribution. Revision and extension. 2 Gmehling, Juergen; Rasmussen, Peter; Fredenslund, Aage Industrial & Engineering Chemistry Process Design and Development, Vol. 21, Issue 1 https://doi.org/10.1021/i200016a021	journal	January 1982
Vapor-liquid equilibriums by UNIFAC group contribution. Revision and extension. 3 Macedo, Eugenia Almeida; Weidlich, Ulrich; Gmehling, Juergen Industrial & Engineering Chemistry Process Design and Development, Vol. 22, Issue 4 https://doi.org/10.1021/i200023a023	journal	October 1983
Vapor-Liquid Equilibria by UNIFAC Group Contribution. Revision and Extension Skjold-Jorgensen, Steen; Kolbe, Barbel; Gmehling, Jurgen Industrial & Engineering Chemistry Process Design and Development, Vol. 18, Issue 4 https://doi.org/10.1021/i260072a024	journal	October 1979
Vapor-liquid equilibria by UNIFAC group contribution. 5. Revision and extension Hansen, Henrik K.; Rasmussen, Peter; Fredenslund, Aage Industrial & Engineering Chemistry Research, Vol. 30, Issue 10 https://doi.org/10.1021/ie00058a017	journal	October 1991
Vapor-liquid equilibria by UNIFAC group contribution. 4. Revision and extension Tiegs, Detlef; Rasmussen, Peter; Gmehling, Juergen Industrial & Engineering Chemistry Research, Vol. 26, Issue 1 https://doi.org/10.1021/ie00061a030	journal	January 1987
Vapor−Liquid Equilibria by UNIFAC Group Contribution. 6. Revision and Extension Wittig, Roland; Lohmann, Jürgen; Gmehling, Jürgen Industrial & Engineering Chemistry Research, Vol. 42, Issue 1 https://doi.org/10.1021/ie020506l	journal	January 2003
Hybridizing physical and data-driven prediction methods for physicochemical properties Jirasek, Fabian; Bamler, Robert; Mandt, Stephan Chemical Communications, Vol. 56, Issue 82 https://doi.org/10.1039/D0CC05258B	journal	January 2020
Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions Jirasek, Fabian; Bamler, Robert; Fellenz, Sophie Chemical Science, Vol. 13, Issue 17 https://doi.org/10.1039/D1SC07210B	journal	January 2022
Prediction of parameters of group contribution models of mixtures by matrix completion Jirasek, Fabian; Hayer, Nicolas; Abbas, Rima Physical Chemistry Chemical Physics, Vol. 25, Issue 2 https://doi.org/10.1039/D2CP04478A	journal	January 2023
Database for liquid phase diffusion coefficients at infinite dilution at 298 K and matrix completion methods for their prediction Großmann, Oliver; Bellaire, Daniel; Hayer, Nicolas Digital Discovery, Vol. 1, Issue 6 https://doi.org/10.1039/D2DD00073C	journal	January 2022
HANNA: hard-constraint neural network for consistent activity coefficient prediction Specht, Thomas; Nagda, Mayank; Fellenz, Sophie Chemical Science, Vol. 15, Issue 47 https://doi.org/10.1039/D4SC05115G	journal	January 2024
Variational Inference: A Review for Statisticians Blei, David M.; Kucukelbir, Alp; McAuliffe, Jon D. Journal of the American Statistical Association, Vol. 112, Issue 518 https://doi.org/10.1080/01621459.2017.1285773	journal	July 2016
Matrix Factorization Techniques for Recommender Systems Koren, Yehuda; Bell, Robert; Volinsky, Chris Computer, Vol. 42, Issue 8 https://doi.org/10.1109/MC.2009.263	journal	August 2009
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo Salakhutdinov, Ruslan; Mnih, Andriy Proceedings of the 25th international conference on Machine learning - ICML '08 https://doi.org/10.1145/1390156.1390267	conference	January 2008

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