MTO烯烃分离过程的多目标操作优化

现代煤化工中，甲醇制烯烃 （MTO） 是一个非常重要的装置。其烯烃分离过程面临着原料变动大、烯烃产品损失以及较高的公用工程消耗等问题。这就需要在满足产品规格和需求的情况下，优化操作条件以实现最大效益。以Lummus前脱丙烷的烯烃分离工艺为研究对象，以增加乙烯与丙烯的总收率和降低总能耗为优化目标，对该工艺流程进行建模模拟与多目标优化。采用非支配排序遗传算法（NSGA-II）进行多目标优化的求解，实现了15个操作变量的同时优化。在维持产品收率不变的前提下，可通过降低脱丙烷塔、脱乙烷塔和1^#丙烯精馏塔的回流比等优化措施找到了当前最优操作点。结果表明，该最优操作点与现有操作点相比可降低20 MW能耗。通过对决策变量的综合分析，确定了不同目标权衡下对应的各个操作变量的优化区间，发现精馏塔可以在多个最佳操作区间内运行。

Multi-objective operation optimization of olefin separation process for MTO plant

In modern coal processing industries, methanol-to-olefins (MTO) is an important equipment. Its olefin separation process is facing with problems such as the change of raw materials, the loss of olefin products and the high consumption of utilities. Operation optimization is required to achieve maximum benefits under the circumstance of quality assurance and requirements. This article takes the pre-depropanized olefin separation process of Lummus as the research object. And the optimization objectives are the total yield of ethylene and propylene as well as the total energy consumption. Modeling, simulation and multi-objective optimization of the process are conducted. Non-dominated sorting genetic algorithm (NSGA-II) is used to solve multi-objective optimization problem. The simultaneous optimization of 15 operational variables is achieved. Under the current yield, the optimal operation point is found by reducing the reflux ratio of low pressure depropanizer, deethanizer and 1^# propylene tower and so on. The results show that the optimal operating point can reduce energy consumption by 20 MW compared with the existing operating point. The optimization interval of each operation variable corresponding to different trade-off points is determined by the comprehensive analysis of decision variables. It is also found that distillation equipment can operate in different optimal operation intervals.