Modeling and monitoring of multimode process based on subspace separation

Chemical Engineering Research and Design

Volumn 91, Issue 5, 2013, Pages 831-842

  Zhang, Yingwei ^a   Wang, Chuang ^a   Lu, Renquan ^b  

^a STATE KEY LABORATORY OF SYNTHETICAL AUTOMATION FOR PROCESS INDUSTRIES   (China)

^b HANGZHOU DIANZI UNIVERSITY   (China)

Author keywords

Batch process; Kernel PCA; Multimode process monitoring; Subspace separation

Indexed keywords

BATCH PROCESS; CONVENTIONAL METHODS; KERNEL PCA; KERNEL PRINCIPAL COMPONENT; MONITORING APPROACH; MULTI-MODE PROCESS; MULTIMODE PROCESS MONITORING; SUBSPACE SEPARATIONS;

BATCH DATA PROCESSING; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; PROCESS MONITORING;

SEPARATION;

EID: 84877622612     PISSN: 02638762     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cherd.2012.09.006     Document Type: Article

References (34)

1. Bakshi B.R. Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal 1998, 44(7):1596-1610.
2. Chiang L.H., Braatz R.D. Process monitoring using causal map and multivariate statistics: fault detection and identification. Chemometrics and Intelligent Laboratory Systems 2003, 65(2):159-178.
3. Chiang L.H., Russell E.L., Braatz R.D. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometrics and Intelligent Laboratory Systems 2000, 50(2):243-252.
4. Dong D., McAvoy T.J. Nonlinear principal component analysis based on principal curves and neural networks. Computers and Chemical Engineering 1996, 20(1):65-78.
5. Fourie, Vaal S.H., de P. Advanced process monitoring using on-line non-linear multiscale principle component analysis methodology. Computers and Chemical Engineering 2000, 24(2-7):755-760.
6. Gallagher N.B., Wise B.M., Stewart C.W. Application of multi-way principal components analysis to nuclear waste storage tank monitoring. Computers and Chemical Engineering 1996, 20:S739-S744.
7. Gregersen L., Jorgensen S.B. Supervision of fed-batch fermentations. Chemical Engineering Journal 1999, 75(1):69-76.
8. Ham J.H., Lee D.D., Mika S., Schokopf B. A kernel view of the dimensionality reduction of manifolds. Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 2004, 369-376.
9. He N., Wang S., Xie L. An improved adaptive multi-way principal component analysis for monitoring streptomycin fermentation process. Chinese Journal of Chemical Engineering 2004, 12(1):96-101.
10. Jia F., Martin E.B., Morris A.J. Nonlinear principal components analysis with application to process fault detection. International Journal of Systems Science 2000, 31(11):1473-1487.
11. Jia M.X., Chu F., Wang F.L. On-line batch process monitoring using batch dynamic kernel principal component analysis. Chemometrics and Intelligent Laboratory Systems 2010, 101(2):110-122.
12. Kosanovich K.A., Dahl K.S., Piovoso M.J. Improved process understanding using multiway principal component analysis. Industrial Engineering and Chemical Research 1996, 35:138-146.
13. Kramer M.A. Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal 1991, 37(2):233-243.
14. Lane S., Martin E.B., Kooijmans R.K., Morris A.J. Performance monitoring of a multi-product semi-batch process. Journal of Process Control 2001, 11(1):1-11.
15. Lee J.M., Yoo C.K., Lee I.B. Online batch process monitoring using a consecutively updated multi-way principal analysis model. Journal of Biotechnology 2004, 108(1):61-77.
16. Lee J.M., Yoo C.K., Lee I.B. Fault detection of batch processes using multiway kernel principal component analysis. Computers and Chemical Engineering 2004, 28:1837-1847.
17. Nomikos P. Detection and diagnosis of abnormal batch operations based on multi-way principal component analysis. ISA Transaction 1996, 35(3):259-266.
18. Nomikos P., MacGregor J.F. Monitoring batch processes using multiway principal component analysis. American Institute of Chemical Engineers Journal 1994, 40(8):1361-1375.
19. Nomikos P., MacGregor J.F. Multivariate SPC charts for monitoring batch processes. Technometrics 1995, 37(1):41-59.
20. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290(5500):2323-2326.
21. Schölkopf B., Smola A., Müller K. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 1998, 10(5):1299-1399.
22. Singhai A., Seborg D.E. Evaluation of a pattern matching method for the Tennessee Eastman challenge process. Journal of Process Control 2007, 16(6):601-613.
23. Tian X.M., Zhang X.L., Deng X.G. Multiway kernel independent component analysis based on feature samples for batch process monitoring. Neurocomputing 2009, 72(7-9):1584-1596.
24. Weinberger K.Q., Sha F., Saul L.K. Learning a kernel matrix for nonlinear dimensionality reduction. Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 2004, 839-846.
25. Westerhuis J.A., Kourti T., MacGregor J.F. Comparing alternative approaches for multivariate statistical analysis of batch process data. Journal of Chemometrics 1999, 13:397-413.
26. Xiang S.M., Nie F.P., Pan C.H., Zhang C.S. Regression reformulations of LLE and LTSA with locally linear transformation. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 2011, 41(5):1250-1262.
27. Yoo C.K., Villez K., Lee I.B., Rosen C., Vanrolleghem P.A. Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor. Biotechnology and Bioengineering 2007, 96(4):687-701.
28. Zhang Y.W., Zhang Y. Fault detection of non-Gaussian processes based on modified independent component analysis. Chemical Engineering Science 2010, 65(16):4630-4639.
29. Zhang Y.W. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM. Chemical Engineering Science 2009, 64(5):801-811.
30. Zhang Y.W., Ma C. Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS. Chemical Engineering Science 2011, 66(1):64-72.
31. Zhang Y.W., Zhang Y., Li C. Fault magnitude estimation for processes. Chemical Engineering Science 2011, 66(18):4261-4267.
32. Zhang Y.W., Zhou H., Qin S.J. Decentralized fault diagnosis of large-scale processes using multiblock kernel principal component analysis. Acta Automatica Sinica 2010, 36(4):593-597.
33. Zhao S.J., Zhang J., Xu Y.M. Monitoring of processes with multiple operating modes through multiple principle component analysis models. Industrial and Engineering Chemistry Research 2004, 43(22):7025-7035.
34. Zhao S.J., Zhang J., Xu Y.M. Performance monitoring of processes with multiple operating modes through multiple PLS models. Journal of Process Control 2006, 16(7):763-772.