Two-Liquid Segment Activity Coefficient Model Research Articles

An ion-specific electrolyte non-random two-liquid segment activity coefficient model with improved predictive capabilities for aqueous electrolyte solutions

The accurate prediction of activity coefficients of electrolyte solutions is of great importance for many engineering applications. The electrolyte non-random two-liquid (eNRTL) model is a commonly used semi-empirical activity coefficient model for electrolyte solutions. This work presents a modified version of the eNRTL model, which aims to extend its predictive capabilities. An ion-specific parameterization scheme is developed to replace the conventionally used salt-specific parameterization scheme. Consequently, this new method allows for the prediction of the properties of electrolytes consisting of ions for which optimal ion-specific parameter values have been determined, which is inherently not possible when using salt-specific parameters. In order to capture the features of solvent-ion interactions, a segment-based local composition term is used and experimental activity data of both the salt and the solvent are utilized for parameter fitting. The modified eNRTL model with optimal ion-specific parameters is applied to aqueous electrolyte solutions and shows satisfying prediction performance.

Nonrandom Two-Liquid Segment Activity Coefficient Model with Association Theory

Association theory has been extensively applied to equations of state such as statistical associating fluid theory and cubic plus association. This work presents an integration of association theory with the nonrandom two-liquid segment activity coefficient (NRTL-SAC) model. Replacing the four conceptual segments (hydrophobic, hydrophilic, polar attractive, and polar repulsive) in the original NRTL-SAC model, the association NRTL-SAC model represents interacting molecules with two conceptual segments (nonpolar and polar) and two association site types (hydrogen bond acceptor and donor). Given the molecule-specific parameters, the model shows much improved predictive capability than the original NRTL-SAC model for phase equilibria of chemical mixtures in which associations take place.

Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model

An improved framework has been developed for solubility prediction of different forms of a medium-sized antibiotic (i.e., nonelectrolyte, electrolyte, and solvate) in single and mixed solvents using a symmetrically reformulated electrolyte nonrandom two-liquid segment activity coefficient (eNRTL-SAC) model. The methodology incorporates key features of the symmetric eNRTL-SAC model structure to reduce the number of parameters and uses a hybrid of global search algorithms for parameter estimation. Moreover, a design of experiments is included in the methodology to generate and use experimental data appropriately for model parameter regression and model validation. Because of the semipredictive nature of the symmetric eNRTL-SAC model, the segment parameter regression is a critical step for solubility prediction accuracy. A particle swarm optimization algorithm is incorporated to preregress conceptual segment parameters of solutes. The preregressed segment parameters were used as initial guesses for further s...

Systematic and practical solvent system selection strategy based on the nonrandom two-liquid segment activity coefficient model for real-life counter-current chromatography separation

Solvent system selection is the first step toward a successful counter-current chromatography (CCC) separation. This paper introduces a systematic and practical solvent system selection strategy based on the nonrandom two-liquid segment activity coefficient (NRTL-SAC) model, which is efficient in predicting the solute partition coefficient. Firstly, the application of the NRTL-SAC method was extended to the ethyl acetate/n-butanol/water and chloroform/methanol/water solvent system families. Moreover, the versatility and predictive capability of the NRTL-SAC method were investigated. The results indicate that the solute molecular parameters identified from hexane/ethyl acetate/methanol/water solvent system family are capable of predicting a large number of partition coefficients in several other different solvent system families. The NRTL-SAC strategy was further validated by successfully separating five components from Salvia plebeian R.Br. We therefore propose that NRTL-SAC is a promising high throughput method for rapid solvent system selection and highly adaptable to screen suitable solvent system for real-life CCC separation.

Correlation and prediction of partition coefficient using nonrandom two-liquid segment activity coefficient model for solvent system selection in counter-current chromatography separation

Selection of a suitable solvent system is the first and foremost step for a successful counter-current chromatography (CCC) separation. In this paper, a thermodynamic model, nonrandom two-liquid segment activity coefficient model (NRTL-SAC) which uses four types of conceptual segments to describe the effective surface interactions for each solvent and solute molecule, was employed to correlate and predict the partition coefficients (K) of a given compound in a specific solvent system. Then a suitable solvent system was selected according to the predicted partition coefficients. Three solvent system families, heptane/methanol/water, heptane/ethyl acetate/methanol/water (Arizona) and hexane/ethyl acetate/methanol/water, and several solutes were selected to investigate the effectiveness of the NRTL-SAC model for predicting the partition coefficients. Comparison between experimental results and predicted results showed that the NRTL-SAC model is of potential for estimating the K value of a given compound. Also a practical separation case on magnolol and honokiol suggests the NRTL-SAC model is effective, reliable and practical for the purpose of predicting partition coefficients and selecting a suitable solvent system for CCC separation.

Symmetric Nonrandom Two-Liquid Segment Activity Coefficient Model for Electrolytes

The nonrandom two-liquid segment activity coefficient model (NRTL-SAC) [Chen, C.-C.; Song, Y. Solubility Modeling with a Nonrandom Two-Liquid Segment Activity Coefficient Model. Ind. Eng. Chem. Res. 2004, 43, 8354−8362] has been shown to be a simple and practical thermodynamic model for correlating and predicting phase behavior for complex pharmaceutical molecules. The model was later extended for electrolytes [Chen, C.-C.; Song, Y. Extension of Non-Random Two-Liquid Segment Activity Coefficient Model for Electrolytes. Ind. Eng. Chem. Res. 2005, 44, 8909−8921]. However, the electrolyte model was presented as an unsymmetric activity coefficient model with aqueous phase infinite dilution reference state. Such an unsymmetric model becomes rather cumbersome to use when dealing with nonaqueous solvents. In this work, we present a symmetric electrolyte NRTL-SAC model which simplifies the long-range interaction term. We further examine the utility of the model in predicting salt solubilities in nonaqueous solven...

Correlation and Prediction of Phase Behavior of Organic Compounds in Ionic Liquids Using the Nonrandom Two-Liquid Segment Activity Coefficient Model

Room-temperature ionic liquids have shown great potential as media for reactions and separations. Information on how organic compounds interact with these ionic liquids is crucial in assessing their usefulness. Here, the nonrandom two-liquid segment activity coefficient (NRTL-SAC) model is used first to correlate values of infinite-dilution activity coefficients for organic compounds in ionic liquids and then to predict the phase behavior of various mixtures involving these ionic liquids. NRTL-SAC provides a robust, qualitative predictive model based on four molecular descriptors that are designed to capture molecular surface interaction characteristics: hydrophobicity, hydrophilicity, polarity, and solvation strength.

Extension of Nonrandom Two-Liquid Segment Activity Coefficient Model for Electrolytes

The nonrandom two-liquid segment activity coefficient model of Chen and Song (Ind. Eng. Chem. Res. 2004, 43, 8354) has shown to be a simple and practical tool for chemists and engineers to correlate and estimate solubilities of organic nonelectrolytes in support of chemical and pharmaceutical process design. In this paper, the model is extended for the computation of ionic activity coefficients and solubilities of electrolytes, organic and inorganic, in common solvents and solvent mixtures. In addition to the three types of molecular parameters defined for organic nonelectrolytes, i.e., hydrophobicity X, polarity Y, and hydrophilicity Z, an electrolyte parameter, E, is introduced to characterize both local and long-range ion−ion and ion−molecule interactions attributed to ionized segments of electrolytes. Successful representations of mean ionic activity coefficients and solubilities of electrolytes, inorganic and organic, in aqueous and nonaqueous solvents are presented.

Solubility Modeling with a Nonrandom Two-Liquid Segment Activity Coefficient Model

A segment contribution activity coefficient model, derived from the polymer nonrandom two-liquid model, is proposed for fast, qualitative estimation of the solubilities of organic nonelectrolytes in common solvents. Conceptually, the approach suggests that one account for the liquid nonideality of mixtures of complex pharmaceutical molecules and small solvent molecules in terms of interactions between three pairwise interacting conceptual segments: hydrophobic segment, hydrophilic segment, and polar segment. In practice, these conceptual segments become the molecular descriptors used to represent the molecular surface characteristics of each solute and solvent molecule. The treatment results in component-specific molecular parameters: hydrophobicity X, polarity Y, and hydrophilicity Z. Once the molecular parameters are identified from experimental data for common solvents and solute molecules, the model offers a simple and practical thermodynamic framework to estimate solubilities and to perform other phase equilibrium calculations in support of pharmaceutical process design.