Improving the Accuracy of Activity Coefficient Estimation in Specialty Chemistry Using Local Estimators

Abstract

The pharmaceutical and specialty industries require the rapid development of molecules and their production processes to meet market demands. Computational tools can accelerate process development by screening potential alternatives. However, accurate parameter inputs are necessary for these tools to calculate processes correctly. In this study, we aim to improve the prediction of activity coefficients in mixtures involving complex molecules using the UNIQUAC functional-group activity coefficients (UNIFAC) and UNIQUAC segment activity coefficients (UNI-SAC) models. We propose adding a new group contribution model to represent the complex core structure of the target molecule, enabling the models to mimic the steric effects induced by the intricate core structure. By regressing the parameters for the new group contribution model using data from a similar molecule, we can accurately depict the properties of the complex structure. We also plan to explore a similar approach for COnductor-like Screening MOdel Segment Activity Coefficients (COSMO-SAC), adjusting the global parameters to account for the structural deviations. Our results show that the accuracy of the models with local estimators is comparable to vanilla models for vapor−liquid equilibrium (VLE) data sets involving simple molecules. The adjusted group contribution-based models outperform the vanilla models for solid−liquid equilibrium (SLE) data sets based on steroid solutions. These findings suggest that the local estimator approach enhances group contribution-based models for complex molecules without sacrificing accuracy. Reliable extrapolation was not observed for the local estimator based on COSMO-SAC. The local estimator approach improves group contribution-based physical property estimation in mixtures of complex molecules during early process development, enhancing downstream process modeling capabilities in specialty chemistry.