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Since its inception, the non-equilibrium lattice fluid (NELF) model has become a vital tool in correlating and predicting the gas solubility behaviour in glassy polymeric membranes. But like its equilibrium variant, the NELF model is highly constrained by the availability of the pure polymer characteristic parameters, which are not always convenient to obtain as the need arises. In this study, we provide a proof-of-concept for building a machine learning-based group contribution method (ML-GC) for the Sanchez–Lacombe equation of state (EoS) pure polymer parameters. The ML-GC model was built using a modified version of the Marrero and Gani's method, which incorporates machine learning regression into the GC parameterisation process. The final model was capable of reproducing the parameters of a randomly selected test set of polymers, with a diverse range of chemical structures. The resultant average AARD % of the predicted densities in this set is 5.59%, with no polymer exceeding 15%. Moreover, to test the model's capabilities in estimating the parameters of high glass transition temperature polymers, we predicted a priori the characteristic parameters of 6 polyimides from the knowledge of their molecular structure. The ML-GC parameters were also incorporated into the NELF model to predict the infinite dilution solubility coefficients (S 0) of some of these polymers and the results were validated against experimental data. Furthermore, the ML-GC-NELF model was also used for the first time to represent effectively the gas solubility isotherms in PIM-PI-SBI and PIM-PI-EA with relatively small magnitudes of the binary interaction parameters (k i j). Despite the small data-set used herein, the model performance was satisfactory, however, as more data are being published in literature, the proposed ML-GC model has the potential of providing even more accurate predictions for a wider range of polymers, ultimately leading to lesser reliance on experimental data for modelling the gas sorption. [Display omitted] • A machine-learning group contribution (ML-GC) model for the Sanchez–Lacombe Equation of state (SL-EoS) was built. • The ML-GC model was capable of predicting the pure polymer parameters. • The ML-GC model reproduced the experimental pure polymer and solubility data satisfactorily. • The ML-GC-NELF model was used to calculate the gas solubility isotherms of PIMs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ternary mixed-gas sorption (C 2 H 6 /CO 2 /CH 4) was measured using a pressure decay apparatus. • Ethane has a strong exclusion power over CH 4 and CO 2 limiting their solubility in the mixed-gas case. • Binary and ternary sorption tests were systematically compared to analyze 3rd component effects. • The NELF model predicts binary and ternary mixed-gas sorption using only pure-gas data as input. • Leveraging high mixed-gas solubility-selectivity strongly enhances separation performance. In this work, a new experimental protocol was designed to measure sorption of ternary gas mixtures in polymer membranes for the first time. We measured the sorption isotherms of binary and ternary mixtures of C 2 H 6 , CO 2 , and CH 4 in PIM-1, using a pressure decay apparatus, working at constant composition and variable pressure of the gas phase. The results show competitive sorption effects in all gas mixtures analyzed, affecting each species in proportion to the total sorbed concentration of the other components. It was found that the presence of C 2 H 6 in the mixture reduces the sorption of both CH 4 and CO 2 to a similar extent, so that the CO 2 /CH 4 solubility-selectivity is not markedly affected. The experimental data were used to validate predictions of the Non-Equilibrium Lattice Fluid (NELF) model of binary and ternary sorption isotherms. The NELF model is generally in close agreement, both in the prediction of pressure and mixture composition effects, proving to be a reliable tool to assess mixed-gas sorption, even in complex scenarios. [ABSTRACT FROM AUTHOR]