(635a) Managing Model and Data Alternatives within the Design of Ionic Liquid Enabled Separations of High Global Warming Potential Hydrofluorocarbon Refrigerants | AIChE

(635a) Managing Model and Data Alternatives within the Design of Ionic Liquid Enabled Separations of High Global Warming Potential Hydrofluorocarbon Refrigerants


Befort, B. - Presenter, University of Notre Dame
Garciadiego, A., University of Notre Dame
Franco, G., University of Notre Dame
Maginn, E., University of Notre Dame
Dowling, A., University of Notre Dame
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 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]. Moreover, thermodynamic and process modeling can accelerate screening and design of optimal ILs for azeotropic HFC separation [4,5]. To date, many thermodynamic model families including activity coefficient models (Margules, Wilson, and nonrandom two liquid), cubic equations of state (van der Waals, Peng-Robinson, Soave-Redlich-Kwong), and soft-SAFT [6-11] have been proposed to predict HFC solubilities in ILs.

In this talk, we apply uncertainty quantification and statistical model selection to thermodynamic modeling of HFC-IL mixtures. Our ultimate goal is to answer several open modeling questions including (a) what level of model complexity is justified by experimental solubility data? (b) how can model overparameterization be avoided? (c) what is the impact of model choice and uncertainty on process design and operations decisions? (d) and, what type of data are most valuable to discriminate between models and reduce uncertainty in process scale decisions? We explore these questions in the context of separating R-410a, a mixture of difluoromethane (HFC-32) and pentafluoroethane (HFC-125). We determined that a van der Waals EoS with five fitted parameters is able to capture solubilities of HFC-32 and HFC-125 in six ILs, but uncertainty analysis showed four of the five parameters were correlated, indicating over-parameterization, and that another parameter was sloppy, indicating the model was insensitive to its value [5]. Additionally, we investigated the use of the Margules and nonrandom two liquid activity coefficient models to predict solubilities for these two HFCs in six ILs, and determined that these activity coefficient models provided poorer fits than the van der Waals EoS. We now explore and compare three cubic EoS (van der Waals, Peng-Robinson, Soave-Redlich-Kwong) combined with a variety of mixing rules to identify which accurate, but not over-complicated, model can best be used in a separation process model of R-410a. We compare frequentist and Bayesian thermodynamic model parameter estimation methods and implement Bayesian thermodynamic model selection for the models of interest. We perform identifiability analysis to determine the sensitivity of these models to the fitted parameters within the mixing rules studied. Additionally, we use uncertainty quantification and sensitivity analyses to explore the type of data (e.g. full or partial solubility curves, Henry’s constants) and level of experimental accuracy which is necessary to generate reliable process models. This analysis offers quantitative insights into the appropriate data and models for designing and screening ILs for further study as HFC separating agents.


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