Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results.

ABSTRACT: A major challenge in bioprocess simulation is the lack of physical and chemical property databases for biochemicals. A Python-based algorithm was developed for estimating the nonrandom two-liquid (NRTL) model parameters of aqueous binary systems in a straightforward manner from simplified molecular-input line-entry specification (SMILES) strings of substances in a system. This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evo

SUBMITTER: Jo SH 

PROVIDER: S-EPMC11780414 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature