支持向量回归在乙烯裂解产物收率软测量中的应用  

Soft-sensor of product yields in ethylene pyrolysis based on support vector regression

作　　者：吴文元[1] 熊智华[1] 吕宁[1] 王京春[1] 邵杰峰 钟向宏 

机构地区：[1]清华大学自动化系,北京100084 [2]中石化茂名分公司,广东茂名525011

出　　处：《化工学报》2010年第8期2046-2050,共5页CIESC Journal

基　　金：国家高技术研究发展计划项目(2007AA041402);国家自然科学基金项目(60874049);北京市科技新星计划项目(2006A62)~~

摘　　要：乙烯裂解产物收率的实时预报对于裂解炉的生产具有重要意义。针对有效的样本数据较少的问题,采用支持向量回归方法建立裂解产物收率的软测量模型。对于支持向量机中模型参数的选取,采用了微粒群优化算法进行参数寻优,提高了建模效率和模型精度。基于现场数据的建模实验结果表明,基于支持向量回归方法的乙烯裂解产物收率软测量模型预报精度较高,趋势跟踪性能良好。It is very important for ethylene pyrolysis process to obtain product yields on line.To address the problem with few valid sampling data,soft-sensor models of several kinds of product yields were developed based on support vector regression （SVR）.Particle swam optimization （PSO） algorithm was used to determine the proper parameters of SVR model,and model efficiency and performance were then improved.SVR based product yield models got high accuracy and good trend tracking performance on the real industrial data.

关 键 词：乙烯裂解 支持向量回归 微粒群优化算法 软测量 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]