最小二乘支持向量机用于常压塔汽油干点的软测量  

Gasoline endpoint soft-sensing of crude column based on least squares support vector machine

作　　者：李悦卿[1] 金思毅[1] 陶少辉[1] 李宁[1] 

机构地区：[1]青岛科技大学化工学院,山东青岛266042

出　　处：《计算机与应用化学》2008年第8期928-930,共3页Computers and Applied Chemistry

基　　金：重质油加工国家重点实验室开放基金;青岛科技大学科研基金

摘　　要：最小二乘支持向量机(least squares support vector machine,LSSVM)为一种遵循结构风险最小化原则的核函数学习机器,其训练仅需求解一个线性方程组,且超参数较标准支持向量机更少。由于其实现简单且预测效果良好,近年来在化学、化工领域的应用日益广泛。本文研究了基于LSSVM的软测量建模过程中的数据预处理和优选超参数等问题。并将其应用于常压塔塔顶汽油干点的软测量建模。计算结果表明,其预测精度能够满足生产实际要求,是一种简单有效的非线性软测量建模工具。Least squares support vector machine （LSSVM） is a kernel learning machine which obeys structural risk minimization （SRM） during training, which has less hyper parameters compared with standard support vector machine and its training is a linear equations set solving problem. Because of its simplicity and good prediction precision, LSSVM is applied widely in chemistry and chemical engineering field. The data pre-processing and hyper parameters selection problems for LSSVM are researched here, and soft sensing for gasoline end point of atmospheric column is used to test the performance of LSSVM soft senor. The result showed that the prediction performance of LSSVM can satisfy the manufacturing request and it＇ s an effective tool for nonlinear soft sensing.

关 键 词：软测量 最小二乘支持向量机 常压塔 汽油干点 

分 类 号：TQ206[化学工程—有机化工]