Commonly used mixed-refrigerants are azeotropic mixtures of hydrofluorocarbons (HFCs) with high global warming potential. There is a need for reclamation and recovery of these HFCs. Solvent-based extractive distillation is a promising separation technique for recycling of these refrigerant components. Ionic liquids are suitable solvents for this application due to their negligible vapor pressures, tunable properties, and near-zero waste in closed-loop operations. However, the numerous potential combinations of cation-anion pairs make the selection of the optimal ionic liquid challenging. Moreover, the choice of ionic liquid critically affects energy efficiency and separation performance. To address this challenge, we present a hierarchical, multiscale computational workflow for computer-aided molecular and process design (CAMPD) that combines aspects of molecular simulation, machine learning, process performance measures, and equation-oriented process optimization for the solvent-based separation of azeotropic refrigerant mixtures. We employ a decomposition-based solution approach for CAMPD, where we first perform computer-aided molecular design (CAMD) to identify promising ionic liquid candidates through high-throughput screening, considering 16,352 known and generated ionic liquids. Next, we perform a focused CAMPD to identify the solvents that give the best process performance. We highlight the application of our method for the separation of refrigerants R-32 from R-125, which belong to the binary azeotropic refrigerant mixture commonly known and used as R-410A. Our method identified 43 ionic liquids (24 known and 19 generated) that matched all solvent and separation process specifications. Among these, five ionic liquids are found to be more sustainable and superior to others.

Computer-aided Molecular and Process Design, CAMPD, is a technique that simultaneously optimizes the choice of materials, such as solvents, and the corresponding process configurations for many chemical separation processes. The technique involves formulating an equation-oriented optimization model representing the overall design problem, which then can be solved in many ways depending on the chemicals involved, the property and process models, and the complexity and size of the problem, among others. Due to the complexity and large-size of the problem, and a lack of predictive property models, we have applied a decomposition-based CAMPD strategy that involves solving a series of subproblems sequentially to reduce the overall search space, thereby reducing the computational burden. We illustrate our strategy through a case study involving the design of ionic liquids (ILs) as solvents for the extractive-distillation based separation of an azeotropic refrigerant mixture, R-410A. Separation of such mixtures is gaining increased interest due to the need to remove, substitute or reuse constituent refrigerant chemicals that have undesirable properties (such as high global warming potential, flammability, etc.). ILs are considered because of their designable properties as functions of their molecular structures. Based on available measured data, group-contribution based predictive property models have been developed and interfaced with the workflow of the proposed strategy. A set of promising ILs have been identified and their performance verified through process simulation.