Latent Variable Models and Big Data in the Process Industries

被引：32

作者：

MacGregor, J. F. ^{[1
]}

Bruwer, M. J. ^{[1
]}

Miletic, I. ^{[1
]}

Cardin, M. ^{[1
]}

Liu, Z. ^{[1
]}

机构：

[1] ProSensus Inc, Ancaster, ON, Canada

来源：

IFAC PAPERSONLINE | 2015年 / 48卷 / 08期

关键词：

Latent variables; Big Data; Process analysis; Monitoring; Optimization; Control; Batch processes; Image analysis; PLS; PRODUCT QUALITY;

D O I：

10.1016/j.ifacol.2015.09.020

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 the process industries Big Data has been around since the introduction of computer control systems, advanced sensors, and databases. Although process data may not really be BIG in comparison to other areas such as communications, they are often complex in structure, and the information that we wish to extract from them is often subtle. Multivariate latent variable regression models offer many unique properties that make them well suited for the analysis of historical industrial data. These properties and use of these models are illustrated with applications to the analysis, monitoring, optimization and control of batch processes, and to the extraction of information from on-line multi-spectral images. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：520 / 524

页数：5

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