On-line batch process monitoring using hierarchical kernel partial least squares

Chemical Engineering Research and Design

Volumn 89, Issue 10, 2011, Pages 2078-2084

  Zhang, Yingwei ^a   Hu, Zhiyong ^a  

^a KEY LABORATORY OF INTEGRATED AUTOMATION OF PROCESS INDUSTRY   (China)

Author keywords

Hierarchical kernel partial least squares (HKPLS); Multi way PLS (MPLS); On line batch process monitoring

Indexed keywords

BATCH PROCESS; BATCH PROCESS MONITORING; CONTINUOUS ANNEALING PROCESS; DETECTABILITY; KERNEL PARTIAL LEAST SQUARES; MONITORING APPROACH; MULTI-WAY PLS (MPLS); NONLINEAR INFORMATION; PARTIAL LEAST SQUARES; PROCESS VARIABLES; TRAJECTORY DEVIATION;

PROCESS CONTROL; PROCESS MONITORING;

BATCH DATA PROCESSING;

EID: 80052801546     PISSN: 02638762     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cherd.2011.01.002     Document Type: Article

References (37)

1. Akaho S. A kernel method for canonical correlation analysis. Proceedings of the International Meeting of the Psychometric Society 2001, Springer-Verlag, Tokyo.
2. AUndey C., Cinar A. Statistical monitoring of multistage, multiphase batch processes. IEEE Control Systems Magazine 2002, 22(5):40-52.
3. AUndey C., Ertunc S., Cinar A. Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis. Industrial and Engineering Chemistry Research 2003, 42(20):4645-4658.
4. Bach F.R., Jordan M.I. Kernel independent component analysis. Journal of Machine Learning Research 2002, 3:1-48.
5. Baudat G., Anouar F. Generalized discriminant analysis using a kernel approach. Neural Computation 2000, 12:2385-2404.
6. Haykin S. Neural Networks 1999, Prentice-Hall, Englewood Cliffs, NJ.
7. Herbrich R. Learning Kernel Classifiers: Theory and Algorithms 2001, MIT Press, Cambridge, MA, USA.
8. Kassidas A., MacGregor J.F., Taylor P.A. Synchronization of batch trajectories using dynamic time warping. AIChE Journal 1998, 44:864-875.
9. Kosanovich K.A., Dahl K.S., Piovoso M.J. Improved process understanding using multiway principal component analysis. Industrial and Engineering Chemistry Research 1996, 35:138-146.
10. Kourti T., Nomikos P., MacGregor J.F. Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. Journal of Process Control 1995, 5(4):277-284.
11. Lai P.L., Fyfe C. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems 2000, 10(10):365-377.
12. Lee D.S., Vanrolleghem P.A. Adaptive consensus principal component analysis for on-line batch process monitoring. Environmental Monitoring and Assessment 2004, 92:119-135.
13. Lee D.S., Lee M.W., Woo S.H., Kim Y.J., Park J.M. Multivariate online monitoring of a full-scale biological anaerobic filter process using kernel-based algorithms. Industrial and Engineering Chemistry Fundamentals 2006, 45:4335-4344.
14. Lee J.M., Yoo C.K., Lee I.B. Enhanced process using multi-way principle component analysis. AIChE Journal 1994, 40(8):1361-1375.
15. Lennox B., Montague G.A., Hiden H.G., Kornfeld G., Goulding P.R. Process monitoring of an industrial fed-batch fermentation. Biotechnology and Bioengineering 2001, 74(2):125-135.
16. Martin E.B., Morris A.J. Enhanced bio-manufacturing through advanced multivariate statistical technologies. Journal of Biotechnology 2002, 99(3):223-235.
17. Melzer T., Reiter M., Bischof H. Nonlinear feature extraction using generalized canonical correlation analysis. Proceedings of the International Conference on Artificial Neural Networks 2001, Springer-Verlag, New York.
18. Nomikos P., MacGregor J.F. Multivariate SPC charts for monitoring batch processes. Technometrics 1995, 37(1):41-59.
19. Nomikos P., MacGregor J.F. Monitoring batch processes using multiway principal component analysis. AIChE Journal 1994, 40:1361-1375.
20. Nomikos P., MacGregor J.F. Multi-way partial least squares in monitoring batch processes. Chemometrics and Intelligent Laboratory Systems 1995, 30:97-108.
21. Qin S.J., Valle S., Piovoso M.J. On unifying multi-block analysis with application to decentralized process monitoring. Journal of Chemometrics 2001, 15(9):715-742.
22. Ränner S., MacGregor J.F., Wold S. Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems 1998, 41:73-81.
23. Rosipal R., Trejo L.J., Matthews B. Kernel PLS-SVC for linear and nonlinear classification. Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003) 2003, 640-647.
24. Rosipal R. Kernel partial least squares for nonlinear regression and discrimination. Neural Network World 2003, 13:291-300.
25. Rosipal R., Trejo L.J. Kernel partial least squares regression in reproducing Kernel Hilbert space. Journal of Machine Learning Research 2001, 2:97-123.
26. Schölkopf B., Smola A.J. Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2001, MIT Press, Cambridge, MA, USA.
27. Schölkopf B., Smola A.J., MJuller K. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 1998, 10(5):1299-1399.
28. Shawe-Taylor J., Cristianini N. Kernel Methods for Pattern Analysis 2004, Cambridge University Press, New York, NY, USA.
29. Tracy N.D., Young J.C., Mason R.L. Multivariate control charts for individual observations. Journal of Quality Technology 1992, 24:88-95.
30. Van Sprang E.N.M., Ramaker H.J., Westerhuis J.A., Gurden S.P., Smilde A.K. Critical evaluation of approaches for on-line batch process monitoring. Chemical Engineering Science 2002, 57:3979-3991.
31. Wise B.M., Gallagher N.B. The process chemometrics approach to process monitoring and fault detection. Journal of Process Control 1996, 6:329-348.
32. Wold S., Geladi P., Esbensen K., Ohman J. Multi-way principle component analysis and PLS analysis. Journal of Chemometrics 1987, 1:41-56.
33. Xu D., Yan S., Luo J. Face recognition using spatially constrained earth mover's distance. IEEE Transactions on Image Processing 2008, 17(11):2256-2260.
34. Zhang Y., Qin S.J. Improved nonlinear fault detection technique and statistical analysis. AIChE Journal 2008, 12:3207-3220.
35. Zhang Y.W., Teng Y.D., Zhang Y. Complex process quality prediction using modified kernel partial least squares. Chemical Engineering Science 2010, 65:2153-2158.
36. Zhang Y., Zhang Y. Process monitoring, fault diagnosis and quality prediction methods based on the multivariate statistical techniques. IETE Technical Review 2010, 27:406-420.
37. Zhang Y., Qin S.J. Fault detection of nonlinear processes using multiway kernel independent analysis. Industrial and Engineering Chemistry Research 2007, 46:7780-7787.