基于改进 NSGA-Ⅱ算法的氯乙烯精馏过程多目标优化

摘要

针对氯乙烯精馏过程中氯乙烯产品纯度低、能耗高的现状,研究了一种新的改进型非支配排序遗传算法(Improved Non-dominated Sorting Genetic Algorithm,NSGA-Ⅱ),用于解决氯乙烯精馏过程多目标优化问题。首先建立了氯乙烯精馏的模拟流程,然后通过对高低沸塔中进料位置、回流比等主要影响因素进行灵敏度分析,在考虑其机理模型及实际生产状况等多种约束条件的基础上,建立了以氯乙烯纯度和能耗为目标的多目标优化函数,最后利用改进 NSGA-Ⅱ对目标函数进行求解。实验结果表明,相比于 NSGA-Ⅱ,该改进算法能得到分布更为均匀的 Pareto 最优解集,为氯乙烯精馏过程中参数的选择提供了有力支撑。%A new improved non-dominated sorting genetic algorithm (NSGA-II)is studied aiming at solving the low purity, high energy consumption problems existed in vinyl chloride rectification process.The method can be used to solve the multi-objective optimization problem of vinyl chloride rectification process.The multi-objective optimization function with the energy consumption and purity of vinyl chlorides based on considering the various constraints of the mechanism model and the actual production conditions were established through the sensitivity analysis for the main operating parameters such as the feeding position and reflux ratio of high and low boiling tower and so on.Finally,the objective function is solved by using the improved NSGA-II.Compared to the NSGA-II,the experimental results show that the improved algorithm can get more uniform distribution of Pareto optimal solution set,which provides a strong support for the selection of parameters in the process of vinyl chloride distillation.