(742e) Computational Studies of Absorption Refrigeration Systems Using Mixtures of Refrigerants, Ionic Liquids and Deep Eutectic Solvents

Heating and cooling buildings in the U.S. consumes an enormous amount of energy (>10 quadrillion BTU), and is responsible for adding ~1 billion metric tons of CO₂ in the earth’s atmosphere every year. Much of this energy is used as electricity in vapor-compression systems; however, this technology is mature and only evolutionary improvements are expected in the near future. Several recent studies^[1],[2],[3] have shown that several common ionic liquids (ILs) can be combined with standard fluorocarbon refrigerants for use in absorption refrigeration systems that use waste heat sources that are at relatively low temperatures (<100 °C). Furthermore, deep eutectic solvents (DESs), a relatively new class of solvents, share many of the properties of ILs while being considerable cheaper and mostly nontoxic; a recent computational study[4] suggests that mixtures of DESs and fluorocarbons can also be used in absorption refrigeration systems. A fundamental understanding of how the chemical structure of the different species affects the solubility of fluorocarbons in an IL/DES is crucial to design mixtures suitable for use in absorption refrigeration systems that use solar energy or waste heat. In this work, we used computational tools to evaluate a number of working fluid mixtures consisting of conventional fluorocarbon refrigerants, ILs and DESs. We first used the quantum chemistry-based conductor-like screening model for real solvents (COSMO-RS) to computationally screen a large mixture of standard refrigerants with ILs and DESs and other theoretical tools, to quickly predict values of several key properties (e.g., solubility, viscosity, phase equilibrium and thermochemical data), and perform thermodynamic evaluations of absorption refrigeration cycles using these working fluid mixtures. Based on this initial thermodynamic evaluation, we selected a few systems for further study using molecular dynamic simulations to understand structure-property relationships. Results from our computational studies will be described and discussed.

^[1] Shiflett, M. B.; Yokozeki, A. AIChE J. 2006, 52, 1205-1219; Shiflett, M. B.; Yokozeki, A. J. Chem. Eng. Data 2007, 52, 2007-2015.

^[2] Kim, S.; Kim, Y. J.; Joshi, Y. K.; Kohl, P. A.; Fedorov, A. G. J. Electron. Packaging 2012, 134, 031009; Kim, S.; Patel, N.; Kohl, P. A. Ind. Eng. Chem. Res. 2013, 52, 6329-6335.

^[3] Zheng, D.; Dong, L.; Huang, W.; Wu, X.; Nie, N. Renew. Sust. Energ. Rev. 2014, 37, 47-68.

[4] Abedin, R.; Heidarian, S.; Flake, J. C.; Hung, F. R. Langmuir 2017, 33, 11611-11625