基于混合遗传算法的催化重整过程多目标优化  

Multi-objective optimization based on hybrid genetic algorithm for naphtha catalytic reforming process

作　　者：李鸿亮[1,2] 陆金桂[1] 侯卫锋[2] 赵英凯[1] 

机构地区：[1]南京工业大学自动化学院,江苏南京210009 [2]浙江大学智能系统与控制研究所,工业控制技术国家重点实验室,浙江杭州310027

出　　处：《化工学报》2010年第2期432-438,共7页CIESC Journal

基　　金：国家高技术研究发展计划项目(2007AA04Z172);国家创新研究群体科学基金项目(60721062)~~

摘　　要：为实现催化重整过程生产指标的综合优化,基于已实现工业应用的催化重整17集总反应动力学模型和催化重整过程机理模型,考虑相应的多种约束条件,建立了以最大化总芳烃收率和最小化重芳烃收率为目标的多目标操作优化模型。提出了一种将遗传算法与局部优化方法相结合的多目标混合遗传算法HNAGA,并用于多目标操作优化模型的求解。现场工业数据的仿真研究表明,HNAGA在寻找Pareto最优解前沿方面比原遗传算法具有一定的优越性。将该多目标优化模型和求解方法应用于工业催化重整装置的操作优化,可以有效提高决策的准确性。To optimize the global production indices of catalytic reforming process, based on a 17-lumped kinetics model and a catalytic reforming process model and considering various constraints, a multiobjective optimization model was proposed to maximize the aromatics yield and minimize the yield of heavy aromatics. Then an improved multi-objective hybrid genetic algorithm （HNAGA）, was proposed by integrating a genetic algorithm with traditional local optimization algorithms and was then used to solve the model. Finally, the industrial simulation result proved that the hybrid algorithm HNAGA was better than the genetic algorithm in obtaining Pareto optimal solutions. The model and algorithm could effectively improve the accuracy of decision-making in operation optimization of the catalytic reforming unit.

关 键 词：催化重整 多目标优化 混合遗传算法 机理模型 

分 类 号：TE624.4[石油与天然气工程—油气加工工程] TQ018[化学工程]