Review on data-driven modeling and monitoring for plant-wide industrial processes

被引：496

作者：

Ge, Zhiqiang ^{[1
]}

机构：

[1] Zhejiang Univ, State Key Lab Ind Control Technol, Inst Ind Proc Control, Coll Control Sci & Engn, Hangzhou 310027, Zhejiang, Peoples R China

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2017年 / 171卷

基金：

中国国家自然科学基金;

关键词：

Plant-wide process; Data-driven modeling; Process monitoring; ROOT-CAUSE DIAGNOSIS; CONTROL-SYSTEM DESIGN; FAULT-DETECTION; QUALITY-RELEVANT; COMPONENT ANALYSIS; PERFORMANCE; OSCILLATIONS; PCA; IDENTIFICATION; OPTIMIZATION;

D O I：

10.1016/j.chemolab.2017.09.021

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Data-driven modeling and applications in plant-wide processes have recently caught much attention in both academy and industry. This paper provides a systematic review on data-driven modeling and monitoring for plant-wide processes. First, methodologies of commonly used data processing and modeling procedures for the plant-wide process are presented. Detailed research statuses on various aspects for plant-wide process monitoring are reviewed since 2000. After that, extensions, opportunities, and challenges on data-driven modeling for plant wide process monitoring are discussed and highlighted for future research.

引用

页码：16 / 25

页数：10

共 129 条

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