Synthesis and optimization of a nonideal distillation system using a parallel genetic algorithm

The automated synthesis of separation sequences for nonideal mixtures has only recently been attempted. The need for rigorous physical property estimation procedures along with the possibly complex recycle structures leads to the specification of a nonlinear programming problem, one in which both the objective function and the feasibility region are nonconvex. This paper describes the use of a stochastic optimization procedure, genetic algorithms, for the optimization of a preselected sequence of distillation units for the separation of a three component azeotropic mixture. By fixing the structure in terms of the units desired, the optimization problem is reduced to one of designing each of the relevant units, choosing the appropriate operating conditions, and finding the optimal heat exchange network. To tackle the high computational effort required, the implementation uses a distributed memory multicomputer in the form of a network of workstations.