Superstructure optimization of chemical process

In this paper we consider the superstructure optimization of chemical process networks. The objective of the superstructure optimization is to minimize the total cost of the process. First we present the mathematical modeling framework for the process networks, where the selection of different process is made by discrete choices. Generalized disjunctive programming (GDP) model and mixed-integer nonlinear programming (MINLP) model are used for the formulation of the process networks. The optimization algorithm for these discrete/continuous optimization models is applied and the optimal solution has lower cost than the base case solution. The industrial applications are shown with monomer reaction process and olefin separation process.