(16g) Molecular Modeling to Aid the Separation of Azeotropic Mixtures of High Global Warming Potential Hydrofluorocarbon Refrigerants Using Ionic Liquids
Molecular simulations will be used to rapidly generate and screen IL and HFC thermophysical properties, but accurately predicting HFC properties via molecular simulations remains challenging. This is because molecular simulations require accurate force field parameters, yet, published force field parameters have not been optimized for all HFCs of interest or their mixtures, especially with ILs. To this end, we propose a semi-autonomous Bayesian optimization strategy to systematically determine force field parameters for HFCs. This strategy involves training a Gaussian process surrogate model on results of a Latin hypercube search over force field parameter space to compute pure and mixture thermophysical properties of HFCs . Regions of force field model parameters which provide accurate thermophysical property calculations can be identified and narrowed based on accuracy of properties including liquid and vapor densities, vapor pressure, and enthalpy of vaporization until optimal force field parameters for an HFC are determined. Results show optimized force field parameters for HFCs including the high GWP HFC, R-125, and low GWP HFC, R-32, which comprise the ubiquitous refrigerant, R-410a, can be used to accurately predict pure and mixture vapor-liquid equilibrium and an HFCâs solubility in ionic liquids. With the tools to develop accurate force fields for HFCs, the necessary molecular properties of pure HFCs and their mixtures are rapidly computed and subsequently used within process modeling of HFC separation processes and compared with process modeling results generated from experimental data.
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