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

[1]

[Anonymous], 2012, DATA MINING KNOWLEDG

[2]

[Anonymous], 2012, STAT MONITORING COMP, DOI [DOI 10.1002/9780470517253, 10.1002/9780470517253]

[3]

[Anonymous], 2013, Multivariate statistical process control: process monitoring methods and applications

[4] Plant-wide optimization and control of an industrial diesel hydro-processing plant [J].

Aydin, Erdal ;

Arkun, Yaman ;

Is, Gamze ;

Mutlu, Mustafa ;

Dikbas, Mine .

COMPUTERS & CHEMICAL ENGINEERING, 2016, 87 :234-245

[5] Root Cause Analysis of Linear Closed-Loop Oscillatory Chemical Process Systems [J].

Babji, S. ;

Nallasivam, U. ;

Rengaswamy, R. .

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2012, 51 (42) :13712-13731

[6] A practical method for identifying the propagation path of plant-wide disturbances [J].

Bauer, Margret ;

Thornhill, Nina F. .

JOURNAL OF PROCESS CONTROL, 2008, 18 (7-8) :707-719

[7] Variable MWPCA for adaptive process monitoring [J].

Bin He, Xiao ;

Yang, Yu Pu .

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2008, 47 (02) :419-427

[8] Nearest neighbors method for detecting transient disturbances in process and electromechanical systems [J].

Cecilio, Ines M. ;

Ottewill, James R. ;

Pretlove, John ;

Thornhill, Nina F. .

JOURNAL OF PROCESS CONTROL, 2014, 24 (09) :1382-1393

[9] Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis [J].

Cherry, GA ;

Qin, SJ .

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, 2006, 19 (02) :159-172

[10] Plant-wide root cause identification using plant key performance indicators (KPIs) with application to a paper machine [J].

Chioua, Moncef ;

Bauer, Margret ;

Chen, Su-Liang ;

Schlake, Jan C. ;

Sand, Guido ;

Schmidt, Werner ;

Thornhill, Nina F. .

CONTROL ENGINEERING PRACTICE, 2016, 49 :149-158

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