Data‐driven approximation of thermodynamic phase equilibria

Abstract

AbstractWe present a new data‐driven approach for both accurate and computationally efficient approximation of vapor liquid equilibria (VLE) models. Our method is able to provide guaranteed enclosure to limit the approximation errors over the entire domain of interest, all just by sampling only at select points. The approximation relies on a mixed‐integer linear programming (MILP) formulation that exploits vertex polyhedral properties of theoretically guaranteed lower and upper bounds to enclose nonlinear and nonconvex equations of state (EOS) and empirical models. Another advantage is that, unlike traditional full simulation‐based data‐driven approaches, we do not solve nonlinear system of equations (f(x) = 0) for sampling. Instead of looking for only feasible samples, we evaluate f(x) over x‐domain. This functional evaluation eliminates the need for computationally‐demanding full‐scale simulations and the associated convergence issues. We demonstrate excellent performance of the proposed MILP formulation in predicting the solubility of hydrofluorocarbon (HFC) refrigerants in ionic liquids (IL).