Modified UNIFAC 2.0 -- A Group-Contribution Method Completed with Machine Learning

TL;DR

Modified UNIFAC 2.0 enhances thermodynamic property predictions by integrating machine learning for comprehensive pair-interaction parameters, significantly improving accuracy and scope over previous models, and allowing easy updates and implementation.

Contribution

It introduces a hybrid model combining group-contribution methods with machine learning to predict missing interaction parameters in thermodynamic models.

Findings

Achieves higher accuracy than previous UNIFAC versions.

Expands predictive capabilities with extensive experimental data.

Allows easy updates with new data or customizations.

Abstract

Predicting thermodynamic properties of mixtures is a cornerstone of chemical engineering, yet conventional group-contribution (GC) methods like modified UNIFAC (Dortmund) remain limited by incomplete tables of pair-interaction parameters. To address this, we present modified UNIFAC 2.0, a hybrid model that integrates a matrix completion method from machine learning into the GC framework, allowing for the simultaneous training of all pair-interaction parameters, including the prediction of parameters that cannot be fitted due to missing data. Utilizing an extensive training set of more than 500,000 experimental data for activity coefficients and excess enthalpies from the Dortmund Data Bank, modified UNIFAC 2.0 achieves improved accuracy compared to the latest published version of modified UNIFAC (Dortmund) while significantly expanding the predictive scope. Its flexible design allows updates with new experimental data or customizations for specific applications. The new model can easily be implemented in established simulation software with complete parameter tables readily available.