Multi-objective optimization methodology for cryogenic air separation process

Abstract

Cryogenic air separation processes are used to produce gases with different quantities and qualities and the process has a tight integration of heat and mass. Like in many other chemical processes, various conflicting objectives must be achieved in this process. Cryogenic air separation process can be used to maximize the oxygen purity and nitrogen purity, each separately or in combination. In this study cryogenic air separation process was simulated using Aspen Plus and the data generated from the simulation were analyzed for multi-objective optimization in MATLAB using genetic algorithm. Three scenarios based on uncertain product demand were examined: maximize O 2 purity, maximize N 2 purity and maximize both O 2 and N 2 purity together. The reflux ratio of high-pressure column (RR) was found to significantly alter the purity of final products. The effect of waste flow rate on purity of products was found to be significant depending on the selection of the position of waste stream stage. The position of waste stream was only found to be significant below the main feed stream of the low-pressure column. Multi-Objective optimization is useful in identifying the optimum conditions of the process variables for product requirements with conflicting objectives.