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为了降低能耗,提高经济效益,在AspenPlus平台上建立了分隔壁精馏塔(DWC)分离松节油中蒎烯的四塔等效模拟流程,采用灵敏度分析确定了对能耗和分离效果影响较大的设计变量及其取值范围,以预分离塔塔板数、主塔塔板数以及能耗最小为目标,建立了DWC分离松节油中蒎烯的多目标优化模型,并利用约束多目标微粒群优化(CMOPSO)算法对模型进行了求解。结果表明:CMOPSO算法能很好地解得DWC的Pareto最优解集,为决策者提供了多种可供选择的DWC优化设计方案;经多目标优化后,在总塔板数(或设备投资费)相近时,与DWC分离松节油的单目标优化结果相比,多目标优化结果可进一步节能21.7 kW;气、液相分配比是DWC特有的,且非常重要的设计变量,采用的双变量灵敏度分析方法能够比较准确地得到两者的适宜取值范围,优化时在该范围内搜索气、液相分配比可望进一步缩短寻优时间。
In order to reduce energy consumption and improve economic efficiency, a four-column equivalent simulation process of separation of pinene in pine gas by a partition wall distillation column (DWC) was established on the AspenPlus platform. Sensitivity analysis showed that the energy consumption and the separation effect were greatly affected And the range of design variables, a multi-objective optimization model of pinene in DWC separation turpentine was established based on the number of pre-separation plates, the number of pylons and the minimum energy consumption. The multi- Optimization (CMOPSO) algorithm to solve the model. The results show that the CMOPSO algorithm can well solve the Pareto optimal solution set of DWC and provide decision makers with a variety of alternative DWC optimization designs. After the multi-objective optimization, the total number of plates (or equipment investment ), The multi-objective optimization result can save a further 21.7 kW compared with the single-objective optimization results of DWC separation turpentine. The gas-liquid distribution ratio is a DWC-specific and very important design variable. The bivariate sensitivity The analytical method can get the suitable range of the two parameters more accurately, and searching for the distribution ratio of gas and liquid during the optimization is expected to further shorten the optimization time.