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Received November 12, 2004
Accepted January 11, 2005
- This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Real-Time Risk Monitoring System for Chemical Plants
Kwangwoon University, 447-1, Wolgye-Dong, Nowon-Ku, Seoul 139-701, Korea
Korean Journal of Chemical Engineering, January 2005, 22(1), 26-31(6), 10.1007/BF02701457
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Abstract
This study was performed to develop a Real-Time Risk Monitoring System which helps to do fault detection using the information from plant information systems in a chemical process. In this study, to do fault detection, principal component analysis (PCA) methods of multivariate statistical analysis were used. The fundamental notions are a set of variable combinations, that is, detection of principal components which indicate the tendency of variables and operating data. Besides classical statistic process control, PCA can reduce the dimension of variables with monitoring process. Therefore, they are known as suitable methods to treat enormous data composed of many dimensions. The developed Real-Time Risk Monitoring System can analyze and manage the plant information on-line, diagnose causes of abnormality and so prevent major accidents. It’s useful for operators to treat numerous process faults efficiently.
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Kim KS, "Development of the Real-time Risk Monitoring System for Chemical Plants," PhD Dissertation, Kwangwoon University, Seoul (2000)
MacGregor JF, Kourti T, Control Eng. Practice, 3(3), 403 (1995)
Park MJ, Doyle FJ, Korean J. Chem. Eng., 21(1), 168 (2004)
Geladi P, Kowalski BR, Anal. Chim. Acta, 185, 1 (1986)
Sohn JH, Jung SH, Non-linear PLS Based on Non-linear Principal Component Analysis and Neural Network, KACC (2000)