Post analysis on different operating time processes using orthonormal function approximation and multiway principal component analysis

被引:9
|
作者
Chen, JH [1 ]
Liu, JL
机构
[1] Chung Yuan Christian Univ, Dept Chem Engn, Chungli 320, Taiwan
[2] Ind Technol Res Inst, Ctr Ind Safety & Hlth Technol, Hsinchu 310, Taiwan
关键词
orthonormal function approximation; PCA; MPCA; batch process; monitoring and detection;
D O I
10.1016/S0959-1524(00)00016-0
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 most batch process operations, operators often need to adjust the different operating time in each batch run to get the desired product quality since the input specifications provided are different. The proposed method is the combination of the orthonormal function approximation and the multiway principal component analysis (MPCA). It is used to analyze and monitor batch processes at the different operating time. Like the philosophy of statistical process control in the traditional MPCA, this method leads to simple monitoring charts, easy tracking of the progress on each batch run and monitoring the occurrence of observable upsets. The only information needed to exploit the procedure is the historical data collected from the past successful batches. The methodology has been applied to two examples, a batch chemical reactor and a wafer plasma etching process, to illustrate the general use of this proposed method. (C) 2000 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:411 / 418
页数:8
相关论文
共 50 条
  • [1] MONITORING BATCH PROCESSES USING MULTIWAY PRINCIPAL COMPONENT ANALYSIS
    NOMIKOS, P
    MACGREGOR, JF
    AICHE JOURNAL, 1994, 40 (08) : 1361 - 1375
  • [2] Fault detection of batch processes using multiway kernel principal component analysis
    Lee, JM
    Yoo, C
    Lee, IB
    COMPUTERS & CHEMICAL ENGINEERING, 2004, 28 (09) : 1837 - 1847
  • [3] Improved process understanding using multiway principal component analysis
    Kosanovich, KA
    Dahl, KS
    Piovoso, MJ
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1996, 35 (01) : 138 - 146
  • [4] Fault detection in batch processes through variable selection integrated to multiway principal component analysis
    Pimentel Peres, Fernanda Araujo
    Peres, Thiago Neves
    Fogliatto, Flavio Sanson
    Anzanello, Michel Jose
    JOURNAL OF PROCESS CONTROL, 2019, 80 : 223 - 234
  • [5] Multiway principal component analysis contributions for structural damage localization
    Ruiz, Magda
    Eduardo Mujica, Luis
    Sierra, Julian
    Pozo, Francesc
    Rodellar, Jose
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2018, 17 (05): : 1151 - 1165
  • [6] Aluminium process fault detection by Multiway Principal Component Analysis
    Majid, Nazatul Aini Abd
    Taylor, Mark P.
    Chen, John J. J.
    Stam, Marco A.
    Mulder, Albert
    Young, Brent R.
    CONTROL ENGINEERING PRACTICE, 2011, 19 (04) : 367 - 379
  • [7] Wavelet and principal component subspace analysis for function approximation and data compression
    Ahmadi, HC
    Dumont, GA
    Ghofraniha, J
    PROCEEDINGS OF THE IEEE-SP INTERNATIONAL SYMPOSIUM ON TIME-FREQUENCY AND TIME-SCALE ANALYSIS, 1998, : 409 - 412
  • [8] Asynchronous Distributed Principal Component Analysis Using Stochastic Approximation
    Morral, Gemma
    Bianchi, Pascal
    Jakubowicz, Jeremie
    2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2012, : 1398 - 1403
  • [9] On-line monitoring of batch processes using multiway independent component analysis
    Yoo, CK
    Lee, JM
    Vanrolleghem, PA
    Lee, IB
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2004, 71 (02) : 151 - 163
  • [10] Fault detection of nonlinear processes using multiway kernel independent component analysis
    Zhang, Yingwei
    Qin, S. Joe
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2007, 46 (23) : 7780 - 7787