In Physical chemistry chemical physics : PCCP
Group contribution (GC) methods are widely used for predicting the thermodynamic properties of mixtures by dividing components into structural groups. These structural groups can be combined freely so that the applicability of a GC method is only limited by the availability of its parameters for the groups of interest. For describing mixtures, pairwise interaction parameters between the groups are of prime importance. Finding suitable numbers for these parameters is often impeded by a lack of suitable experimental data. Here, we address this problem by using matrix completion methods (MCMs) from machine learning to predict missing group-interaction parameters. This new approach is applied to UNIFAC, an established group contribution method for predicting activity coefficients in mixtures. The developed MCM yields a complete set of parameters for the first 50 main groups of UNIFAC, which substantially extends the scope and applicability of UNIFAC. The quality of the predicted parameter set is evaluated using vapor-liquid equilibrium data of binary mixtures from the Dortmund Data Bank. This evaluation reveals that our approach gives prediction accuracies comparable with UNIFAC for data sets to which UNIFAC was fitted, and only slightly lower accuracies for data sets to which UNIFAC is not applicable.
Jirasek Fabian, Hayer Nicolas, Abbas Rima, Schmid Bastian, Hasse Hans
2022-Dec-20
