MULTIVARIATE DATA-ANALYSIS APPLIED TO LOW-DENSITY POLYETHYLENE REACTORS

被引：94

作者：

SKAGERBERG, B

MACGREGOR, JF

KIPARISSIDES, C

机构：

[1] MCMASTER UNIV, DEPT CHEM ENGN, HAMILTON L8S 4L7, ONTARIO, CANADA

[2] ARISTOTELIAN UNIV SALONIKA, DEPT CHEM ENGN, Thessaloniki, GREECE

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 1992年 / 14卷 / 1-3期

关键词：

D O I：

10.1016/0169-7439(92)80117-M

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we discuss how partial least squares regression (PLS) can be applied to the analysis of complex process data. PLS models are here used to: (i) accomplish a better understanding of the underlying relations of the process; (ii) monitor the performance of the process by means of multivariate control charts; and (iii) build predictive models for inferential control. The strategies for applying PLS to process data are described in detail and illustrated by an example in which low-density polyethylene production is simulated.

引用

页码：341 / 356

页数：16

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