A decomposition method for synthesizing complex column configurations using tray-by-tray GDP models

Abstract

This paper describes an optimization procedure for the synthesis of complex distillation configurations. A superstructure based on the Reversible Distillation Sequence Model (RDSM) is proposed embedding all possible alternative designs using tray-by-tray models. Generalize disjunctive programming (GDP) is used to model the superstructure. Each column section of the superstructure is modeled using rigorous MESH equations. Due to the large size and complexity of the formulation, as well as the great difficulty in coverging the corresponding equations, a decomposition solution strategy is proposed where discrete decisions are decomposed into two hierarchical levels within an iterative procedure. In the first level, the column sections are selected yielding a candidate configuration. In the second level, the feed location and the number of trays of the selected sections are optimized. A preprocessing phase including thermodynamic information is considered to provide a good starting point to the algorithm in order to improve the convergence and robustness of the method. Examples are presented for zeotropic and azeotropic multicomponent mixtures to illustrate the performance of the proposed method. Non-trivial configurations are obtained involving modest solution times.