Optimization of membrane gas separation systems using genetic algorithm

Genetic algorithm is applied for the optimization of the membrane gas separation systems. Air separation for enriched oxygen production is the selected system for investigation. Optimizations for single and triple objective functions are studied. The optimization problem involves the selection of the optimal system configurations from three alternatives, including continuous membrane column (CMC), single stripper permeator (SSP), and two stripper in series permeator (TSSP), as well as the optimal operating conditions. Models of the three configurations and the genetic algorithm procedure are computerized. The objective functions discussed are the Rony separation index, power consumption per unit equivalent pure oxygen, and the membrane area. Both high-pressure and low-pressure (vacuum) operation modes are optimized and the effects of different oxygen product purity and feed rate are analyzed. For single objective function optimization, the solutions obtained using genetic algorithm are slightly inferior in one case but superior in other cases compared to those by pure mathematical optimization methods. For triple objective function optimization, the Pareto plots presenting multiple trade-off solutions are generated. In general, compared to high-pressure operation mode, the product recovery and power consumption for low-pressure operation mode are lower. For almost all the cases studied, CMC configuration with its high flexibility appears in the optimal solutions.