摘要
为了降低环己酮环己醇分离过程中的能量消耗以及设备投资,以某厂脱去轻组分的环己酮、环己醇、重组分混合物为原料,提出将传统流程中的酮塔和醇塔耦合成隔壁塔的新工艺。使用MATLAB将改进的非支配排序遗传算法(NSGA-Ⅱ)与Aspen Plus模拟软件联用,以年总费用(TAC)和二氧化碳排放量为目标变量,要求环己酮的质量分数达到99.5%、环己醇的质量分数达到99.0%,同时产品的回收率达到95.0%,对隔壁塔的操作参数进行优化。设计种群大小为400,最大遗传代数为500,交叉分数0.85、变异分数0.1。优化过程中不断向目标函数减少的方向进行,当种群大小进化到400代左右时,目标函数不再有明显变化。选择TAC最小的一组Pareto最优前沿解作为最优解,与传统的双塔流程相比,在分离过程中,二氧化碳排放量降低了31.49%,TAC降低了28.98%。
In order to reduce energy consumption and equipment investment in the separation process of cyclohexanone and cyclohexanol,a new process coupling the ketone column and alcohol tower into a dividing wall column(DWC)was proposed.The mixture from a certain factory was used as the feedstock.The integration of NSGA-Ⅱwith Aspen Plus via MATLAB was employed to reduce the total annual cost(TAC)and CO_(2) emission and optimize the operating parameters.The desired mass fraction were set at 99.5%for cyclohexanone and 99.0%for cyclohexanol,to maintain a 95.0%global recovery rate for the products.Optimization of the DWC parameters was conducted with a population size of 400 and a maximum genetic generation of 500,including a crossover rate of 0.85 and a mutation rate of 0.1.The TAC and CO_(2) emission kept decreasing during parameter optimization.When the population size evolved to around 400 generations,the objective function no longer changed significantly.The Pareto optimal frontier solution of the minimum TAC was selected as the optimal solution.Compared with the traditional two-tower process,this strategy resulted in a 31.49%reduction in CO_(2) emission and a 28.98%decrease in TAC in the separation process.
作者
薄守石
张家凯
徐子涵
孙兰义
张其克
BO Shoushi;ZHANG Jiakai;XU Zihan;SUN Lanyi;ZHANG Qike(College of Chemistry and Chemical Engineering,China University of Petroleum(East China),Qingdao 266580,Shandong,China;Shandong Zhengnuo Chemical Equipment Co.,Ltd.,Zibo 255080,Shandong,China)
出处
《化工进展》
EI
CAS
CSCD
北大核心
2024年第11期6077-6082,共6页
Chemical Industry and Engineering Progress
关键词
遗传算法
多目标
隔壁塔
分离
优化
genetic algorithm
multiple objectives
dividing wall column
separation
optimization
