Double Moving Window MPCA for Online Adaptive Batch Monitoring
在线自适应批次过程监视的双滑动窗口MPCA方法
查看参考文献13篇
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文摘
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Online monitoring of chemical process performance is extremely important to ensure the safety of a chemical plant and consistently high quality of products. Multivariate statistical process control has found wide applications in process performance analysis, monitoring and fault diagnosis using existing rich historical database. In this paper, we propose a simple and straight forward multivariate statistical modeling based on a moving window MPCA （multiway principal component analysis） model along the time and batch axis for adaptive monitoring the progress of batch processes in real-time. It is an extension to minimum window MPCA and traditional MPCA. The moving window MPCA along the batch axis can copy seamlessly with variable run length and does not need to estimate any deviations of the ongoing batch from the average trajectories. It replaces an invariant fixed-model monitoring approach with adaptive updating model data structure within batch-to-batch, which overcomes the changing operation condition and slows time-varying behaviors of industrial processes. The software based on moving window MPCA has been successfully applied to the industrial polymerization reactor of polyvinyl chloride （PVC） process in the Jinxi Chemical Company of China since 1999. |
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来源
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Chinese Journal of Chemical Engineering
,2005,13(5):649-655 【核心库】
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关键词
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moving window
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multiway principal component analysis
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batch monitoring
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地址
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1.
Automation Research Center, Northeastern University, 辽宁, Shenyang, 110004
2.
Information Engineering School, Shenyang Institute of Chemical Technology, 辽宁, Shenyang, 110142
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语种
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英文 |
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文献类型
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研究性论文 |
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ISSN
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1004-9541 |
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学科
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化学工业 |
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基金
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国家973计划
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国家自然科学基金
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文献收藏号
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CSCD:2160830
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参考文献 共
13
共1页
|
|
1.
Zhou D H. Modern Fault Diagnosis and Tolerance Control.
Mdern Fault Diagnosis and Tolerance Control,2000
|
CSCD被引
1
次
|
|
|
|
|
2.
Liang J. Multivariate Statistical Process Monitoring and Control:Recent Developments and Applications to Chemical Industry.
Chinese J Chem Eng,2003,11(2):191-203
|
CSCD被引
24
次
|
|
|
|
|
3.
Nomikos P. "Monitoring batch processes using multiway principal component analysis".
AIChE J,1994,40(8):1361-1375
|
CSCD被引
135
次
|
|
|
|
|
4.
Lakshminarayanan S. Extensions and practical issues".
Proceeding of IFAC World Congress,1996:241-246
|
CSCD被引
1
次
|
|
|
|
|
5.
Kaistha N. "Extraction of event times in batch profiles for time synchronization and quality predictions".
Industry Engineering Chemical Rresearch,2001,40:252-260
|
CSCD被引
2
次
|
|
|
|
|
6.
Kassidas A. "Synchronization of batch trajectories using dynamic time warping".
Process System Engineering,1998,44(4):864-875
|
CSCD被引
1
次
|
|
|
|
|
7.
Wold S. "Exponentially weighted moving principal component analysis and projection to latent structures".
Chemometrics and Intelligent Laboratory Systems,1994,23:149-161
|
CSCD被引
28
次
|
|
|
|
|
8.
Lennox B. "Case study investigating multivariate statistical techniques for fermentation supervision".
Computers and Chemical Engineering,1999,23(suppl):827-830
|
CSCD被引
2
次
|
|
|
|
|
9.
Qin S J. "Recursive PLS algorithms for adaptive data modeling".
Computers and Chemical Engineering,1998,22(4/5):503-514
|
CSCD被引
70
次
|
|
|
|
|
10.
Li W H. J.
J Proc Cont,2000,10:471-486
|
CSCD被引
72
次
|
|
|
|
|
11.
Zhao L J. "Study of an onlinear PCA fault detection and diagnosis method".
Information and Control,2001,30(4):359-364
|
CSCD被引
1
次
|
|
|
|
|
12.
Li Y. Faulty Sensor Detection and Reconstruction for a PVC Making Process.
Chinese J Chem Eng,2004,12(2):227-233
|
CSCD被引
3
次
|
|
|
|
|
13.
Li W. J.
J Proc Cont,2000,10:471-486
|
CSCD被引
72
次
|
|
|
|
|
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