Multi-objective optimization of distillation sequences using a genetic-based algorithm

Abstract

The distillation sequences of selected two-case hydrocarbon mixtures are determined in this study by an exergoeconomic multi-objective optimization using a genetic-based solver. A sole computer program (DISMO) is developed for achieving this aim including the database of thermophysical properties and genetic algorithm-based solver. The number of possible sharp distillation sequences increase markedly with the number of feed components and proper sequencing from maximum exergetic profit and minimum exergy destruction. Also, a parametric investigation is made for various weighing factors of objective functions for the sake of revealing the true characteristics of the system. The results of the illustrated cases show that the algorithm is applicable for the determination of the optimum alternative of the distillation sequences as the Pareto Solution Set and the optimum configuration is considered, and it is found that the maximum profit and minimum exergy destruction is 107,647 $/kW and 9302 kW, respectively, with a sequencing of 5-4-3-2-1 and 2-1-4-5-3 for a 6-component hydrocarbon mixture.