(16g) Molecular Modeling to Aid the Separation of Azeotropic Mixtures of High Global Warming Potential Hydrofluorocarbon Refrigerants Using Ionic Liquids | AIChE

(16g) Molecular Modeling to Aid the Separation of Azeotropic Mixtures of High Global Warming Potential Hydrofluorocarbon Refrigerants Using Ionic Liquids


Befort, B. - Presenter, University of Notre Dame
Maginn, E. - Presenter, University of Notre Dame
DeFever, R., Clemson University
Dowling, A., University of Notre Dame
In this work, we use molecular simulations to enhance the discovery of new ionic liquid (IL) separating agents for azeotropic separation of hydrofluorocarbon (HFC) mixtures. Mandated by the 1987 Montreal Protocol, chlorofluorocarbon (CFC) refrigerants have been gradually replaced by HFCs to prevent ozone depletion. Many of these second-generation HFC mixtures, however, have a high global warming potential (GWP) and the 2016 Kigali agreement ordered their gradual phase-out [1]. Due to the often azeotropic compositions of these HFC refrigerants, existing separation methods for removing the high GWP components from low GWP HFCs are currently infeasible or not practical, but it is wasteful to incinerate HFC mixtures as some HFC components have low GWPs and can be recycled. We hypothesize that custom ILs can be designed to remove low GWP HFC components from specific HFC mixtures [2][3]. Millions of potential IL separating agents exist [4], though, making trial-and-error molecular discovery intractable. Instead, we are creating a molecular design framework which integrates molecular simulations, experimental measurements, and process optimization to concurrently design novel separation agents and processes for HFC azeotropic mixtures.

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 [5]. 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 HFC’s 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.


[1]United Nations Environment Programme. Ozone Secretariat. (2006). Handbook for the Montreal protocol on substances that deplete the ozone layer. UNEP/Earthprint.

[2]Plechkova, N. V., & Seddon, K. R. (2008). Applications of ionic liquids in the chemical industry. Chemical Society Reviews, 37(1), 123-150.

[3]Chávez-Islas, L. M., Vasquez-Medrano, R., & Flores-Tlacuahuac, A. (2011). Optimal molecular design of ionic liquids for high-purity bioethanol production. Industrial & Engineering Chemistry Research, 50(9), 5153-5168.

[4]Holbrey, J. D., & Seddon, K. R. (1999). Ionic liquids. Clean products and processes, 1(4), 223-236.

[5]Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. In Advances in neural information processing systems (pp. 2951-2959).

[6]McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239-245.