2020 Virtual AIChE Annual Meeting

(479g) Process Design Under Uncertainty for Novel Separations of Azeotropic High Global Warming Potential Hydrofluorocarbon Refrigerants Using Ionic Liquids

Authors

Garciadiego, A. - Presenter, University of Notre Dame
Maginn, E. - Presenter, University of Notre Dame
Befort, B., 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 methods for separating the high and low 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]. Millions of potential IL separating agents exist [4], though, making trial-and-error molecular discovery intractable. Instead, we are creating a molecular design framework which integrates molecular simulations, experimental measurements, and molecular and process optimization to concurrently design novel separation agents and processes for HFC azeotropic mixtures.

While a design for an IL-enabled azeotropic distillation process for HFC mixture separations has been proposed [5], there is opportunity for optimization at both the molecular and process scales. To begin identifying these opportunities requires thermodynamic model parameterization, process modeling, and uncertainty quantification. To this end, parameters for the van der Waals equation of state have been fit to experimental data for HFC-IL systems. Results show that variation of pseudo-critical properties of ILs and certain critical parameters have little impact on the fit of the model, indicating that while the model parameters give an excellent fit to experimental data, the model may be over-parameterized. This can lead to a reduction in the predictive capabilities of the model and cause diverging results when the model is used within process optimization. Due to the concern of over-parameterization in the general van der Waals model, we perform a systematic search over van der Waals model simplifications, alternative equations of state and thermodynamic models, and mixing rules to determine the HFC-IL thermodynamic model best supported by experimental data. Once a thermodynamic model is found, we utilize the open-source IDAES-PSE Python modeling framework to perform process optimization of the HFC-IL separation. Additionally, we are able to simulate and optimize the process utilizing different ILs, bypassing lengthy and costly experimental efforts. Finally, we propagate experimental uncertainty through the thermophysical property model parameters and subsequent process models comprising this process design. This uncertainty analysis provides insight into optimizing the process design before the process is tested on a lab scale.

References:

[1] Ng, L. Y., Chong, F. K., & Chemmangattuvalappil, N. G. (2015). Computers & Chemical Engineering, 81, 115-129.

[2]Valencia-Marquez, D., Flores-Tlacuahuac, A., & Vasquez-Medrano, R. (2011). Industrial & Engineering Chemistry Research, 51(17), 5866-5880.

[3] Chávez-Islas, L. M., Vásquez-Medrano, R., & Flores-Tlacuahuac, A. (2010). Industrial & Engineering Chemistry Research, 50(9), 5175-5190.

[4]Holbrey, J. D., & Seddon, K. R. (1999). Ionic liquids. Clean products and processes, 1(4), 223-236.

[5] Shiflett, M. B. and Yokozeki, A. (2006). Chimica Oggi - Chemistry Today, 24(2).