Simulation and Multi-Objective Optimization of Three-Column Double-Effect Methanol Distillation by NSGA-III Algorithm

Abstract

The multi-objective optimization of methanol distillation is a critical and complex issue in the methanol industry. The three-column methanol distillation scheme is first simulated with Aspen Plus to provide the initial value of the NSGA-III algorithm. The operating parameters are optimized through the Python-Aspen platform. The total annual cost and CO2 emissions are considered the objective function. A small value of indicator generational distance can be achieved by increasing the number of generations, which is helpful in improving algorithm convergence. The NSGA-III algorithm has good convergence and distribution performance. By comparing the optimized results with the original ones, the total annual cost and CO2 emissions are, respectively, reduced by 5.35% and 12.80% when the operating parameters of the methanol distillation sequence are optimized through NSGA-III. As a result, substantial economic and energy savings can be made, offering great potential to improve the performance of the three-column methanol distillation.