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

被引：0

作者：

Wu, Wenyuan ^{[1
]}

Xiong, Zhihua ^{[1
]}

Lü, Ning ^{[1
]}

Wang, Jingchun ^{[1
]}

Shao, Jiefeng ^{[2
]}

Zhong, Xianghong ^{[2
]}

机构：

[1] Department of Automation, Tsinghua University, Beijing 100084, China

[2] China Petroleum and Chemical Corporation Maoming Branch, Maoming 525011, Guangdong, China

来源：

Huagong Xuebao/CIESC Journal | 2010年 / 61卷 / 08期

关键词：

Pyrolysis - Regression analysis - Particle swarm optimization (PSO);

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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. © All Rights Reserved.

引用

页码：2046 / 2050

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