The pursuit of accurate, efficient predictive models is vital as it could reduce the experimental and design cost associated with chemical processes. This is of particular interest in separation processes, as they may account for 40-70 percent of the capital and operating cost of a chemical plant . Predictive computational tools have become an essential asset in conducting feasibility and comparative studies, as they are significantly quicker and more cost-efficient than experimental determinations . They are necessary when property data is unavailable, specifically in the case of dealing with new products and compounds. However within computational advancement, there is a risk in losing the intuitive molecular understanding of the processes that lead to observed outcomes. Thus, including thermodynamic parameters such as limiting activity coefficients in the computational process provides molecular insight that could greatly assist in resultant recommendations for industrial design .