(62c) From Data to Process Design: Modeling Azeotropic Separation of High Global Warming Potential Hydrofluorocarbon Refrigerants Using Ionic Liquids
Ionic liquids are known as "designer solvents" because of the wide variety of cations and anions that can be paired to form ILs with different properties. Previous studies have shown ILs can exhibit high HFC solubility and desirable solvent properties [2, 3, 4, 5, 6]. Shiflett and colleagues  performed AspenPlus simulations showing that ILs can function as entrainers for the separation of HFC azeotropic mixtures. While experimentation proved the feasibility of IL use for azeotropic HFC separation, this technology must be applicable for various HFC refrigerant systems, which requires consideration of millions of potential IL cation/anion pairs and corresponding process designs. This necessitates leveraging computational tools, especially accurate thermophysical property prediction methods, and rigorous process optimization models, to rapidly screen and optimize candidate ILs and process designs.
In this talk, we describe how we model ternary phase behavior of hydrofluorocarbon (HFC) and ionic liquid (IL) mixtures from binary and ternary data. We developed a process design framework based on open-source IDAES-PSE Python modeling framework and Pyomo to analyze an IL's feasibility as a separating agent using computational and experimental data. Here, we answer the question given a mixture of HFCs, which ionic liquid is ideal for the separation and the process design? We have estimated ternary diagrams from binary data with a 10% error in the compositions. We have also been able to calculate the phase behavior of six different ILs qualitatively. We make a direct comparison between the Peng-Robinson cubic equation of state (EoS) and previously published ternary diagrams predicted from the more sophisticated soft-SAFT EoS . We verify our framework by calibrating with soon-to-be-published first-of-a-kind ternary data. Additionally, we qualitatively explore the feasibility of new ILs as separation agents. We have created a framework that allows us to carry out process design from binary and ternary data. The framework is fast enough to rapidly screen possible entrainers for HFC separation in a matter of seconds.
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