基于改进文化粒子群算法的多组分石脑油调和优化问题研究  

Research on Multi-component Naphtha Recipe Optimization with Improved Particle Swarm Optimization Using Cultural Algorithm

作　　者：汪丽娜[1] 曹萃文[1] 

机构地区：[1]华东理工大学信息科学与工程学院,上海200237

出　　处：《石油化工自动化》2012年第1期43-47,共5页Automation in Petro-chemical Industry

基　　金：国家"863"高科技研究发展项目(2009AA04Z141);中央高校基础研究项目;教育部博士点基金(200802510010);上海市自然科学基金(10ZR1408300;11ZR1409800);上海市重点学科项目(B504)

摘　　要：针对某炼油厂关于多组分石脑油调和优化存在的产量和质量不达标问题,分别对调和优化模型及求解算法进行了研究。根据各组分油的库存量、质量属性及石脑油产品油的质量指标等制约因素,建立基于组合数学的调和优化模型,并提出一种改进的文化粒子群算法,求解所有可行调和配方从而确定最优调和配方。典型的测试函数验证了该算法解决约束问题的有效性,应用实例表明该模型具有良好的可行性,也进一步说明了算法的有效性。According to existing problem of yield and quality missing the standard requirement for multi-component naphtha of a refinery, the blending optimization model and the calculation are studied respectively. The optimized model based on combinatorics,is established according to the restricted factors of the stock of all naphtha components, product property and finished goods quality specifications, and an improved particle swarm optimization based on cultural algorithm is proposed to conduct the calculation for defining all feasible recipes, and provide the optimized recipe. The effectiveness of the algorithm solving constrained problem is verified by classical test functions. The actual application case indicates the good feasibility of the model, which further shows the validity of the algorithm.

关 键 词：石脑油 调和优化 文化算法 粒子群算法 模型 

分 类 号：TE624.5[石油与天然气工程—油气加工工程] TP301.6[自动化与计算机技术—计算机系统结构]