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

Constantinescu, Dana; Gmehling, Jürgen

doi:10.1021/acs.jced.6b00136

Cited by 111 publications

(142 citation statements)

References 39 publications

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“…A histogram representation of the results is given in Figure S4 In the following, we compare the proposed MCM with one of the highly developed physical methods for predicting activity coefficients. Modified UNIFAC (Dortmund) 31,32 , referred to simply as UNIFAC in the following, is the most successful of these methods and has been considered as the gold standard for more than 30 years. In UNIFAC, the properties of a mixture With its present published parameterization, UNIFAC is able to predict the activity coefficients for 3342 of the 4094 solute-solvent combinations that are considered here.…”

mentioning

confidence: 99%

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

Jirasek

Alves

Damay

et al. 2020

J. Phys. Chem. Lett.

120

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Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes. Although experimental data on thousands of binary mixtures are available, prediction methods are needed to calculate the activity coefficients in many relevant mixtures that have not been explored to-date. In this report, we propose a probabilistic matrix factorization model for predicting the activity coefficients in arbitrary binary mixtures. Although no physical descriptors for the considered components were used, our method outperforms the state-of-the-art method that has been refined over three decades while requiring much less training effort. This opens perspectives to novel methods for predicting physico-chemical properties of binary mixtures with the potential to revolutionize modeling and simulation in chemical engineering. Activity Coefficients at Infinite Dilution   Solutes SolventsThis document is the unedited authors' version of a submitted work that was subsequently accepted for publication in TheIn this work, we describe a novel application of Machine Learning (ML) to the field of physical chemistry and thermodynamics: the prediction of physico-chemical properties of binary liquid mixtures by matrix completion. We focus on the prediction of a single property: the so-called activity coefficient, which is a measure of the non-ideality of a liquid mixture and of enormous relevance in practice. The interesting aspect of our approach is that no expert knowledge about the components that make up the mixture was used: all we needed was an incomplete, sparse data set of binary mixtures and their measured activity coefficients that our method was able to successfully complete. We show that this simple approach outperforms an established procedure that has been the state of the art for several decades.ML approaches to chemical and engineering sciences date back more than 50 years ago, but the genuine exploitation of the potential of ML in these fields has only recently begun 1 . An overview of recent advances in chemical and material sciences has, e.g., been given by Ramprasad et al. 2 and Butler et al. 3 ML has already been used to predict physico-chemical properties of mixtures, including activity coefficients 4-10 . Most of these approaches are basically quantitative structureproperty relationships (QSPR) methods 11 that use physical descriptors of the components as input data to characterize the considered mixtures and relate them to the property of interest by an ML algorithm, e.g., a neural network. However, the scope of these approaches is in general rather small.Binary mixtures are of fundamental importance in chemical engineering. The properties of mixtures can in general not be described based on properties of the pure components alone. If, however, the respective properties of the binary constituent 'sub-mixtures' of a multi-component mixture are known, the properties of the multi...

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mentioning

confidence: 99%

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

Jirasek

Alves

Damay

et al. 2020

J. Phys. Chem. Lett.

120

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“…For this reason a group to describe the whole glycerol molecule, GLY, was introduced. The same approach was used in the NIST-modified UNIFAC model [27], and in the latest extension of Dortmund UNIFAC [70,71]. The use of the GLY group improves the prediction in systems with glycerol: glycerol-alcohol, Figure 14-16, glycerol-water, Figure 17.…”

Section: Model Performancesmentioning

confidence: 99%

Systematic identification method for data analysis and phase equilibria modelling for lipids systems

Perederic

Cunico

Kalakul

et al. 2018

The Journal of Chemical Thermodynamics

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“…The UNIFAC method modifies this approach by using the functional groups in the solute and solvent to predict the model parameters, reducing the need for fitted empirical parameters for every unique pair. It is however, limited in the types of molecules for which the predictions can be made 8‐10 . In either method, one relies on temperature‐dependent data either directly worked into the binary interaction parameters or upon which the temperature‐dependent functional group predicted parameters are based 10 .…”

Section: Introductionmentioning

confidence: 99%

“…It is however, limited in the types of molecules for which the predictions can be made 8‐10 . In either method, one relies on temperature‐dependent data either directly worked into the binary interaction parameters or upon which the temperature‐dependent functional group predicted parameters are based 10 . Semiempirical correlations developed by Plyasunov and Shock 11,12 exist for predicting solvation properties across wide ranges of temperature and pressure, but their studies were limited to aqueous solution and required empirically fit parameters.…”

Section: Introductionmentioning

confidence: 99%

Temperature‐dependent vapor–liquid equilibria and solvation free energy estimation from minimal data

2020

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We present a new strategy to estimate the temperature-dependent vapor-liquid equilibria and solvation free energies of dilute neutral molecules based on only their estimated solvation energy and enthalpy at 298 K. These two pieces of information coupled with matching conditions between the functional forms developed by Japas and Levelt Sengers for near critical conditions and by Harvey for low and moderate temperature conditions allow the fitting of a piecewise function that predicts the temperature-dependent solvation energy for dilute solutes up to the critical temperature of the solvent. If the Abraham and Mintz parameters for the solvent and solute are available or can be estimated from group contributions, this method requires no experimental data and can still provide accurate estimates with an error of about 1.6 kJ/mol. This strategy, which requires minimal computational resources, is shown to compare well with other methods of temperature-dependent solvation free energy prediction. K E Y W O R D Schemical property estimation, gas solubility, phase equilibrium, solvation free energy

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

Cited by 111 publications

References 39 publications

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

Systematic identification method for data analysis and phase equilibria modelling for lipids systems

Temperature‐dependent vapor–liquid equilibria and solvation free energy estimation from minimal data

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