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Separating azeotropic mixtures of hydrofluorocarbons (HFCs) for reuse and recycle is environmentally and economically imperative. While ionic liquid (IL)-enabled HFC separations show promise, Edisonian trial-and-error screening for the optimal IL entrainer is intractable and expensive. Here, we propose an open-source, equation-oriented modeling framework to rapidly translate HFC/IL solubility data into regressed thermodynamic models which can be used for process design under uncertainty and rapid IL screening. Moreover, we use data science and process systems engineering tools to contemplate which data are the most valuable for IL screening. We find that binary solubility data collected at multiple temperatures is adequate for separation process design, and newly available ternary solubility measurements should be reserved for validation. Additionally, we use uncertainty quantification analyses to show up to 10% experimental error is acceptable for IL screening decisions. Informed by these results, we recommend a multistep workflow for IL screening. 

Separating azeotropic mixtures of hydrofluorocarbons (HFCs) for reuse and recycle is environmentally and economically imperative. While ionic liquid (IL)-enabled HFC separations show promise, Edisonian trial-and-error screening for the optimal IL entrainer is intractable and expensive. Here, we propose an open-source, equation-oriented modeling framework to rapidly translate HFC/IL solubility data into regressed thermodynamic models which can be used for process design under uncertainty and rapid IL screening. Moreover, we use data science and process systems engineering tools to contemplate which data are the most valuable for IL screening. We find that binary solubility data collected at multiple temperatures is adequate for separation process design, and newly available ternary solubility measurements should be reserved for validation. Additionally, we use uncertainty quantification analyses to show up to 10% experimental error is acceptable for IL screening decisions. Informed by these results, we recommend a multistep workflow for IL screening. 

To help slow climate change, international efforts have begun to mandate the phase-out of high global warming potential (GWP) hydrofluorocarbons (HFCs) throughout the next decade. Most HFC refrigerant mixtures form azeotropes, complicating separation into the individual HFC components for reuse and recycling. In this paper, we design and analyze ionic liquid (IL)-enabled extractive distillation processes for ternary HFC separations using AspenPlus. Specifically, we design processes to separate three commercially important HFC refrigerant mixtures (R-404A, R-407C, and R-410A) into high purity HFC streams. We find added value of the separation of R-410A of 0.58 $/kg with current market conditions, specifically laboratory-scale IL manufacturing costs (1000 $/kg of IL) and a low-price differential of 1.00 $/kg between raw materials and separated products. If the IL purchase cost decreases 90 % due to mass production, consistent with prior adoption of ILs for niche separations, the added value increases to 0.76 $/kg. Moreover, under proposed reductions in HFC manufacturing, the price of recovered products may dramatically increase in the future. For example, if the price of R-32 increases by 50 %, the added value would reach 3.08 $/kg. In summary, we find IL-based recycling of HFCs is economically viable based on simple technoeconomic analysis. Moreover, this paper reports capital and operation cost curves and a general analysis framework to analyze evolving market conditions. 

In this work surrogate assisted optimization is utilized to calibrate predictive molecular models, called force fields, used in molecular simulations to reproduce the liquid density of a hydrofluorocarbon refrigerant molecule. A previous calibration workflow which relied on Gaussian process regression models and large Latin hypercube samples to screen force field parameter space is extended to include Bayesian optimization methods to efficiently guide the search for force field parameters. In comparison to the previous work, the Bayesian-based calibration workflow finds a parameter set which results in a lower objective function value than the original workflow after evaluating approximately 50% fewer parameter sets. It is envisioned that this updated workflow will facilitate rapid force field optimization enabling screening of vast molecular design space.