Liquid–Liquid Extraction Thermodynamic Parameter Estimator (LLEPE) for Multicomponent Separation Systems

Gibbs energy minimization is a powerful tool for modeling liquid–liquid equilibrium (LLE) in multicomponent systems, but it requires key thermodynamic parameters that may not be readily available. Our earlier work demonstrated an approach for estimating these properties via regression to equilibrium isotherm data but identified limitations regarding applicability to higher-concentration systems, sensitivity to experimental data, and ease of implementation. To extend the accessibility and applicability of Gibbs energy minimization for modeling LLE, this study presents the open-source, liquid–liquid equilibrium parameter estimator (LLEPE) package for estimating thermodynamic parameters. LLEPE extends parameter estimation to include aqueous-phase Pitzer coefficients that capture the effects of ionic interactions and offers analysis and visualization tools that assess experimental data quality and model prediction accuracy. We apply LLEPE to a rare earth extraction case study where we incorporate data filtering and Pitzer parameter fitting to reduce the effect of outlier data, capture the contribution of ion interactions, and ultimately improve overall prediction accuracy for multicomponent data. Our results show that filtering data decreases the prediction error by removing outliers, and fitting Pitzer parameters increases overall prediction accuracy, especially for high-concentration systems. Iteratively fitting to a combined dataset increases prediction accuracy for multicomponent data for low to medium feed concentrations.