Systematic Integration of Product Attributes and Molecular Synthesis

The complete and efficient solution to the enumeration of candidate compounds and mixtures that meet specified consumer attributes is often a difficult mathematical programming problem. Most approaches to this problem involve the solution of a mixed integer nonlinear program (MINLP) which may achieve only local optima solutions. In this paper a proof-of-concept study is presented to show that empirical models can be used in a reverse problem formulation to ensure a complete set of candidate compounds and mixtures are found subject to the predictive power of the models. The method utilizes a transformation of consumer attributes to properties described by the group contribution method and solves the reverse problem formulation using the property clustering technique. The design of an environmentally benign refrigerant for the replacement of 1,1,1,2-tetrafluoroethane (R-134a) in a typical refrigeration cycle is used to highlight the method.