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

被引:480
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
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
收藏
页码:16 / 25
页数:10
相关论文
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
    Aydin, Erdal
    Arkun, Yaman
    Is, Gamze
    Mutlu, Mustafa
    Dikbas, Mine
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2016, 87 : 234 - 245
  • [5] Root Cause Analysis of Linear Closed-Loop Oscillatory Chemical Process Systems
    Babji, S.
    Nallasivam, U.
    Rengaswamy, R.
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2012, 51 (42) : 13712 - 13731
  • [6] A practical method for identifying the propagation path of plant-wide disturbances
    Bauer, Margret
    Thornhill, Nina F.
    [J]. JOURNAL OF PROCESS CONTROL, 2008, 18 (7-8) : 707 - 719
  • [7] Variable MWPCA for adaptive process monitoring
    Bin He, Xiao
    Yang, Yu Pu
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2008, 47 (02) : 419 - 427
  • [8] Nearest neighbors method for detecting transient disturbances in process and electromechanical systems
    Cecilio, Ines M.
    Ottewill, James R.
    Pretlove, John
    Thornhill, Nina F.
    [J]. 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
    Cherry, GA
    Qin, SJ
    [J]. 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
    Chioua, Moncef
    Bauer, Margret
    Chen, Su-Liang
    Schlake, Jan C.
    Sand, Guido
    Schmidt, Werner
    Thornhill, Nina F.
    [J]. CONTROL ENGINEERING PRACTICE, 2016, 49 : 149 - 158