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A Geometric Approach for the Calculation of the Nonrandomness Factor Using Computational Chemistry
Pedro Velho1,2, Leonor R Barroca1,2, Eugénia A Macedo1,2
1LSRE-LCM-Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.
Journal of Chemical and Engineering Data
|October 17, 2024
Summary
This study introduces Porto's approach, a new method using computational chemistry to calculate molecular nonrandomness factors (α). This enhances thermodynamic models like NRTL and UNIQUAC for better phase equilibria prediction in mixtures.
Area of Science:
- Thermodynamics and Physical Chemistry
- Computational Chemistry
- Chemical Engineering
Background:
- The nonrandomness factor (α) is crucial for thermodynamic models like Nonrandom Two-Liquid (NRTL) and Universal Quasi-chemical (UNIQUAC) but is difficult to estimate accurately.
- Fixed nonrandomness factors can lead to inaccuracies and convergence issues in phase equilibria correlations, particularly for complex mixtures.
Purpose of the Study:
- To develop and apply a novel geometric methodology (Porto's approach) for calculating component-specific nonrandomness factors (α).
- To evaluate the impact of component-specific nonrandomness factors on the accuracy of NRTL and UNIQUAC models for liquid-liquid equilibria (LLE) data correlation.
Main Methods:
- Utilized computational chemistry (Density Functional Theory, DFT) to optimize molecular energy and determine stable configurations in a solvent.
- Calculated molecular dipole moments using Natural Bond Orbital (NBO) population analysis to derive nonrandomness factors.
- Applied the component-specific nonrandomness factors in NRTL and UNIQUAC models for correlating LLE data of ternary systems.
Main Results:
- Successfully calculated nonrandomness factors for 50 pure components using Porto's approach.
- Correlated LLE data for 15 ternary systems with NRTL and UNIQUAC models using both classical and component-specific nonrandomness factors.
- Achieved comparable standard deviations between classical and component-specific nonrandomness factor approaches, indicating improved model applicability.
Conclusions:
- Porto's approach provides a physically meaningful method for estimating nonrandomness factors, enhancing the application of excess free Gibbs energy models.
- This methodology represents a significant advancement for accurate thermodynamic modeling of nonideal mixtures, including those with electrolytes.
- The study paves the way for more precise phase equilibria predictions by improving the physical basis of thermodynamic models.
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