Soft sensing modelling based on optimal selection of secondary variables and its application

被引：4

作者：

Li Q. ^{[1
]}

Shao C. ^{[1
]}

机构：

[1] Institute of Advanced Control Technology, Dalian University of Technology

来源：

International Journal of Automation and Computing | 2009年 / 6卷 / 4期

关键词：

Distillation column; Kernel ridge regression (KRR); Ridge regression; Sensitivity matrix analysis; Soft sensor;

D O I：

10.1007/s11633-009-0379-x

中图分类号：

学科分类号：

摘要：

The composition of the distillation column is a very important quality value in refineries, unfortunately, few hardware sensors are available on-line to measure the distillation compositions. In this paper, a novel method using sensitivity matrix analysis and kernel ridge regression (KRR) to implement on-line soft sensing of distillation compositions is proposed. In this approach, the sensitivity matrix analysis is presented to select the most suitable secondary variables to be used as the soft sensor's input. The KRR is used to build the composition soft sensor. Application to a simulated distillation column demonstrates the effectiveness of the method. © Institute of Automation, Chinese Academy of Sciences and Springer Berlin Heidelberg 2009.

引用

页码：379 / 384

页数：5

共 17 条

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