Simultaneous solution of process and molecular design problems using an algebraic approach

Abstract

Traditionally process design and molecular design problems have been treated as two separate problems, with little or no feedback between them. Introduction of the property integration framework has allowed for simultaneous representation of processes and products from a property perspective and hence established a link between molecular and process design. The simultaneous approach involves solving two reverse problems. The first reverse problem identifies the input molecules’ property targets corresponding to the desired process performance. The second reverse problem is the reverse of a property prediction problem, which identifies the molecular structures that match the targets identified in the first problem. Group contribution methods (GCM) are used to form molecular property operators and these help in tracking properties. Earlier contributions in this area have tried to include higher order estimation of GCM for solving the molecular design problem. In this work, the accuracy of property prediction is enhanced by improving the techniques to enumerate higher order groups. Incorporation of these higher order enumeration techniques increases the efficiency of property prediction and thus the range of applicability of group contribution methods to molecular design problems. This method of generation enables the identification of structural isomers to some extent as it puts a check on the possibility of nonexistence of each higher order group in each combination. Property operator based techniques are used to track properties in both process and molecular design problems. The developed algorithm solves the set of inequality expressions of process and molecular design problems simultaneously to identify the molecules that meet the process performance and environmental restrictions defined in terms of properties. Since the algorithm should be able to solve for any number of properties, an algebraic approach is used to generate possible molecules within the required property range. This contribution will use a case study to highlight the principles of the developed methodology.