Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

doi:10.1016/S1004-9541(13)60506-6

Chin.J.Chem.Eng. ›› 2013, Vol. 21 ›› Issue (6): 633-643.DOI: 10.1016/S1004-9541(13)60506-6

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Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

JIANG Qingchao, YAN Xuefeng

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2011-11-29 Revised:2012-08-30 Online:2013-07-03 Published:2013-06-28
Supported by:
Supported by the 973 project of China (2013CB733600), the National Natural Science Foundation (21176073), the Doctoral Fund of Ministry of Education (20090074110005), the New Century Excellent Talents in University (NCET-09-0346), “Shu Guang” project (09SG29) and the Fundamental Research Funds for the Central Universities.

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

姜庆超, 颜学峰

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

通讯作者: YAN Xuefeng
基金资助:
Supported by the 973 project of China (2013CB733600), the National Natural Science Foundation (21176073), the Doctoral Fund of Ministry of Education (20090074110005), the New Century Excellent Talents in University (NCET-09-0346), “Shu Guang” project (09SG29) and the Fundamental Research Funds for the Central Universities.

Abstract

Abstract: The kernel principal component analysis (KPCA) method employs the first several kernel principal components (KPCs), which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis (SKPCA) is proposed to improve process monitoring performance, i.e., to deal with the discordance of T² statistic and squared prediction error δ_SPE statistic and reduce missed detection rates. T² statistic can be used to measure the variation directly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T² statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.

Key words: statistical process monitoring, kernel principal component analysis, sensitive kernel principal component, Tennessee Eastman process

摘要： The kernel principal component analysis (KPCA) method employs the first several kernel principal components (KPCs), which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis (SKPCA) is proposed to improve process monitoring performance, i.e., to deal with the discordance of T² statistic and squared prediction error δ_SPE statistic and reduce missed detection rates. T² statistic can be used to measure the variation directly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T² statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.

关键词: statistical process monitoring, kernel principal component analysis, sensitive kernel principal component, Tennessee Eastman process

JIANG Qingchao, YAN Xuefeng . Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components[J]. Chin.J.Chem.Eng., 2013, 21(6): 633-643.

姜庆超, 颜学峰. Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components[J]. Chinese Journal of Chemical Engineering, 2013, 21(6): 633-643.

References

1 Lee, J., Kang, B., Kang, S.H., “Integrating independent component analysis and local outlier factor for plant-wide process monitoring”, J. Process Control, 21 (7), 1011-1021 (2011).

2 Li, Y.F., Wang, Z.F., Yuan, J.Q., “On-line fault detection using SVM-based dynamic MPLS for batch processes”, Chin. J. Chem. Eng., 14 (6), 754-758 (2006).

3 Liu, X.Q., Xie, L., Kruger, U., Littler, T., Wang, S.Q., “Statistical-based monitoring of multivariate non-Gaussian systems”, AIChE J., 54 (9), 2379-2391 (2008).

4 MacGregor, J., Kourti, T., “Statistical process control of multivariate processes”, Control Eng. Pract., 3 (3), 403-414 (1995).

5 Zhang, G.X., An Introduction to The New Multivariate Diagnosis Theory with Two Kinds of Quality, Science Press, Bei Jing, China (2001).

6 Dong, D., McAvoy, T.J., “Nonlinear principal component analysis-based on principal curves and neural networks”, Comput. Chem. Eng., 20 (1), 65-78 (1996).

7 Kresta, J.V., Macgregor, J.F., Marlin, T.E., “Multivariate statistical monitoring of process operating performance”, Can. J. Chem. Eng., 69 (1), 35-47 (2009).

8 Wachs, A., Lewin, D.R., “Improved PCA methods for process disturbance and failure identification”, AIChE J., 45 (8), 1688-1700 (2004).

9 Kramer, M.A., “Nonlinear principal component analysis using auto- associative neural networks”, AIChE J., 37 (2), 233-243 (1991).

10 Cui, P., Li, J., Wang, G., “Improved kernel principal component analysis for fault detection”, Expert Syst. Appl., 34 (2), 1210-1219 (2008).

11 Jia, F., Martin, E., Morris, A., “Non-linear principal components analysis with application to process fault detection”, Int. J. Sys. Sci., 31 (11), 1473-1487 (2000).

12 Jia, M., Chu, F., Wang, F., Wang, W., “On-line batch process monitoring using batch dynamic kernel principal component analysis”, Chemom. Intell. Lab. Syst., 101 (2), 110-122 (2010).

13 Ku, W., Storer, R.H., Georgakis, C., “Disturbance detection and isolation by dynamic principal component analysis”, Chemom. Intell. Lab. Syst., 30 (1), 179-196 (1995).

14 Li, R., Rong, G., “Fault isolation by partial dynamic principal component analysis in dynamic process”, Chin. J. Chem. Eng., 14 (4), 486-493 (2006).

15 Xie, L., Zhang, J., Wang, S., “Investigation of dynamic multivariate chemical process monitoring”, Chin. J. Chem. Eng., 14 (5), 559-568 (2006).

16 Chen, T., Martin, E., Montague, G., “Robust probabilistic PCA with missing data and contribution analysis for outlier detection”, Comput. Stat. Data An., 53 (10), 3706-3716 (2009).

17 Kim, D., Lee, I.B., “Process monitoring based on probabilistic PCA”, Chemom. Intell. Lab. Syst., 67 (2), 109-123 (2003).

18 Lee, J.M., Yoo, C.K., Lee, I.B., “Fault detection of batch processes using multiway kernel principal component analysis”, Comput. Chem. Eng., 28 (9), 1837-1847 (2004).

19 Wang, Z.F., Yuan, J.Q., “Online supervision of penicillin cultivations based on rolling MPCA”, Chin. J. Chem. Eng., 15 (1), 92-96 (2007).

20 Choi, S.W., Lee, C., Lee, J.M., Park, J.H., Lee, I.B., “Fault detection and identification of nonlinear processes based on kernel PCA”, Chemom. Intell. Lab. Syst., 75 (1), 55-67 (2005).

21 Nguyen, V.H., Golinval, J.C., “Fault detection based on Kernel principal component analysis”, Eng. Struct., 32 (11), 3683-3691 (2010).

22 Schölkopf, B., Smola, A., Müller, K.R., “Nonlinear component analysis as a kernel eigenvalue problem”, Neural comput., 10 (5), 1299-1319 (1998).

23 Haykin, S., Neural Networks., Practice-Hall Press, New Jersey, U.S.A. (1999).

24 Lee, J.M., Yoo, C.K., Choi, S.W., Vanrolleghem, P.A., Lee, I.B., “Nonlinear process monitoring using kernel principal component analysis”, Chem. Eng. Sci., 59 (1), 223-234 (2004).

25 Romdhani, S., Gong, S., Psarrou, A., “A multi-view nonlinear active shape model using kernel PCA”, In: British Machine Vision Conference, Nottingham, U.K. (1999).

26 Webb, A.R., Statistical Pattern Recognition, Oxford University Press, New York, U.S.A. (1999).

27 Read, C.B., Kotz, S., Johnson, N.L., Encyclopedia of Statistical Sciences, Wiley, Germany (1988).

28 Downs, J.J., Vogel, E.F., “A plant-wide industrial process control problem”, Comput. Chem. Eng., 17 (3), 245-255 (1993).

29 Lyman, P.R., Georgakis, C., “Plant-wide control of the Tennessee Eastman problem”, Comput. Chem. Eng., 19 (3), 321-331 (1995).

30 McAvoy, T., Ye, N., “Base control for the Tennessee Eastman problem”, Comput. Chem. Eng., 18 (5), 383-413 (1994).

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Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

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