An Adaptive ML Framework to Predict PC-SAFT Parameters for Mixtures

The application of perturbed-chain statistical associating fluid theory (PC-SAFT) to complex mixtures requires fitting pure-component parameters and determining binary interaction parameters for all molecular pairs. Since these binary parameters depend on the pure-component sets, an integrated machine learning (ML) framework was developed to predict them directly from pure-component inputs. This allows flexible variation of component parameters while maintaining consistency. A neural network ensemble was trained on 7300 binary interaction parameters from 6301 systems, achieving an average mean absolute error (MAE) of 0.0096 for k ij predictions. Using ML-predicted k ij values, vapor–liquid (VLE) and liquid–liquid equilibria (LLE) for binary and ternary systems were successfully reproduced. The results demonstrate that the proposed ML framework enables efficient and accurate PC-SAFT predictions with minimal experimental input, providing a powerful tool for early stage process development, where reliable thermodynamic modeling is needed without extensive data acquisition.