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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. 

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. 

We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that addresses two important challenges for multi-robot SLAM: i) that of enabling information exchange "on-demand" via Active Rendezvous without using a map or the robot's location, and ii) that of rejecting outlying measurements. Our key insight is to exploit relative position data present in the communication channel between robots to improve groundtruth accuracy of PGO. We develop an algorithmic and experimental framework for integrating Channel State Information (CSI) with multi-robot PGO; it is distributed, and applicable in low-lighting or featureless environments where traditional sensors often fail. We present extensive experimental results on actual robots and observe that using Active Rendezvous results in a 64% reduction in ground truth pose error and that using CSI observations to aid outlier rejection reduces ground truth pose error by 32%. These results show the potential of integrating communication as a novel sensor for SLAM.