(387ci) Bridging Chemical Theory and Engineering Practice: The Association-NRTL Model for Complex Mixtures

Accurate modeling of associative mixtures remains a challenge in process simulation due to strong self- and cross association effects, which significantly impact thermophysical properties and phase behavior¹. Existing thermodynamic models, such as the NRTL² and Polar PC-SAFT equations of state (EoS)³, either oversimplify associative interactions or require extensive parameterization, limiting their applicability in process simulators. Recent developments, including activity coefficient models like the NRTL-based association models by Hao and Chen^4,5 and the SAFT-based SAFT-AC⁶ and SAFT-SAC⁷, have improved the treatment of associating species but still face challenges, particularly in predicting excess enthalpy and in parameter generalization. This study builds on Hao and Chen’s association model by introducing a novel binary association parameter (K_AiDj) derived from chemical theory to replace association strength (Δ^AiDj) from Wertheim’s theory^8-11, resolving constraints of molecular-specific association assumptions. The revised association model, combined with the classical NRTL framework for non-associative interactions, forms the Association NRTL model. It simplifies parameter regression and improves prediction of vapor-liquid equilibrium (VLE), liquid-liquid equilibrium (LLE), and excess enthalpy (HXS) data for mixtures exhibiting various associative effects. The model is validated using binary and ternary systems containing methanol or ethanol with solvents such as water, acetone, chloroform, hexane, and cyclohexane. Results demonstrate improved accuracy and predictive capability compared to the classical NRTL model, highlighting the model’s potential for broader industrial application.

Research Interests:

My research centers on advancing thermodynamic modeling for complex chemical systems, particularly those involving strong self- and cross-associating interactions. I co-developed a novel Association-NRTL model that introduces a new association term derived entirely from chemical theory and constructed from first thermodynamic principles. This term captures association strength directly, eliminating the need for molecule-specific empirical assumptions. The model significantly enhances the accuracy of vapor-liquid and liquid-liquid equilibrium predictions, as well as excess enthalpy, across a wide range of associating systems such as alcohols, water, and their mixtures with hydrocarbons. In addition to improving predictive capability, the model simplifies parameter estimation and is currently being integrated into Aspen Plus, making it broadly accessible for industrial process simulation and design.

Complementing this theoretical work, I also explore scalable separation strategies for recovering bio-derived carboxylic acids such as acetic, lactic, and butyric acid. These acids are important platform chemicals, but their recovery from fermentation broths is challenging due to their dilute concentrations and high solubility. I have conducted pilot-scale experiments using both adsorption (with IRN-78 resin) and membrane separation (using commercial nanofiltration cartridges such as NF90-2540). These studies, performed under realistic fermentation conditions, provide valuable data and insights into transport phenomena and performance of these separation technologies. In addition, I am currently leading the development of Aspen-based process simulation models to simulate these pilot scale experiments for both adsorption and membrane separation. These modeling efforts serve to validate the generalized BET isotherm model for organic acid adsorption equilibria and the solution-diffusion model for reverse osmosis membrane separation.

Together, these two lines of research aim to bridge fundamental thermodynamic theory with applied process development. By integrating chemically grounded models into commercial simulation tools and validating separation performance at pilot scale, my work supports the creation of more accurate, efficient, and sustainable chemical processes—especially in emerging biomanufacturing applications where robust modeling and scalable recovery are critical.

Reference:

1. Chapman, W. G.; Gubbins, K. E.; Jackson, G.; Radosz, M. New Reference Equation of State for Associating Liquids. Ind. Eng. Chem. Res. 1990, 29 (8), 1709–1721.
2. Renon, H.; Prausnitz, J. M. Local Compositions in Thermodynamic Excess Functions for Liquid Mixtures. AIChE J. 1968, 14 (1), 135–144.
3. Chapman, W. G.; Gubbins, K. E.; Jackson, G.; Radosz, M. SAFT: Equation-of-State Solution Model for Associating Fluids. Fluid Phase Equilibria 1989, 52, 31–38.
4. Hao, Y.; Chen, C.-C. Nonrandom Two-Liquid Segment Activity Coefficient Model with Association Theory. Ind. Eng. Chem. Res. 2019, 58 (28), 12773–12786.
5. Hao, Y.; Chen, C. Nonrandom Two‐liquid Activity Coefficient Model with Association Theory. AIChE J. 2021, 67 (1), e17061.
6. Chapman, Walter G., and Wael A. Fouad. "Activity coefficients from an equation of state: novel approach for fast phase equilibrium calculations." Industrial & Engineering Chemistry Research 60.48 (2021): 17733-17744.
7. Chapman, Walter G., and Wael A. Fouad. "Beyond Flory–Huggins: Activity coefficients from perturbation theory for polar, polarizable, and associating solvents to polymers." Industrial & Engineering Chemistry Research 61.48 (2022): 17644-17664.
8. Wertheim, Michael S. "Fluids with highly directional attractive forces. I. Statistical thermodynamics." Journal of statistical physics 35.1 (1984): 19-34.
9. Wertheim, Michael S. "Fluids with highly directional attractive forces. II. Thermodynamic perturbation theory and integral equations." Journal of statistical physics 35.1 (1984): 35-47.
10. Wertheim, Michael S. "Fluids with highly directional attractive forces. III. Multiple attraction sites." Journal of statistical physics 42.3 (1986): 459-476.
11. Wertheim, Michael S. "Fluids with highly directional attractive forces. IV. Equilibrium polymerization." Journal of statistical physics 42.3 (1986): 477-492.