Excess properties of mixtures quantify the difference of the property of a real and an ideal mixture at identical temperature, pressure and composition. According to the “state-of-the-art” the excess Gibbs energy of a mixture can be extracted via partial least squares regression (PLSR) from the excess absorption spectrum of the same mixture. The correlations of excess properties of mixtures feature two disadvantages: (i) they are a function of the mixture compounds. Thus, mixtures of various “families”, such as ketone/alkane or ketone/alcohol, cannot be reflected with one and the same correlation. (ii) The desired activity coefficients can be obtained having fitted gE models to the gE values that resulted from the correlations. These disadvantages can be circumvented, if partial excess properties of the mixture compounds are regarded instead of excess properties of mixtures. Therefore, this project aims at correlating the partial excess Raman spectrum of the mixture compounds with their activity coefficients. The proposed correlation of partial excess properties of mixture compounds features two advantages compared to the “state-of-the-art”: (i) As partial excess properties are regarded, the identified correlations are supposed to be more independent from the mixture family. The degree of independence from the mixture family depends on the correlation method, because of which machine learning methods will be made use of that can regard linear (partial least squares regression) as well as non-linear (convolutional neural networks) relations. (ii) The desired activity coefficients result directly from the identified correlations and do not have to be obtained by fitting first the interaction parameters of gE models. The identified correlations of partial excess properties will enable the experimental Raman spectroscopic determination of activity coefficients of the compounds in a mixture without the need of measuring phase equilibria.

This project is open access and publicly accessible.

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