Nonlinear batch process monitoring using phase-based kernel-independent component analysis-principal component analysis (KICA-PCA)

Industrial and Engineering Chemistry Research

Volumn 48, Issue 20, 2009, Pages 9163-9174

  Zhao, Chunhui ^a,b   Gao, Furong ^a,b   Wang, Fuli ^a,b  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b NORTHEASTERN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BATCH MONITORING; BATCH PROCESS; BATCH PROCESS MONITORING; COVARIANCE STRUCTURES; FED BATCHES; GAUSSIANS; LINEAR METHODS; MODELING STRATEGY; NON-GAUSSIAN FEATURES; NONLINEAR CHARACTERISTICS; ONLINE MONITORING; PROCESS DATA; STATISTICAL MODELING; SUBPHASES; TIME VARYING;

BATCH DATA PROCESSING; FAULT DETECTION; INDEPENDENT COMPONENT ANALYSIS; NONLINEAR ANALYSIS; PROCESS CONTROL; PROCESS MONITORING;

PRINCIPAL COMPONENT ANALYSIS;

EID: 70350318936     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie8012874     Document Type: Article

References (59)

1. Kourti, T.; MacGregor, J. F. Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemom. Intell. Lab. Syst. 1995, 28, 3.
2. Kosanovich, K. A.; Dahl, K. S.; Piovoso, M. J. Improved process understanding using multiway principal component analysis. Ind. Eng. Chem. Res. 1996, 35, 138.
3. Martin, E. B.; Morris, A. J.; Papazoglou, M. C.; Kiparissides, C. Batch process monitoring for consistent production. Comput. Chem. Eng. 1996, 20, 599.
4. Louwerse, D. J.; Smilde, A. K. Multivariate statistical process control of batch processes based on three-way models. Chem. Eng. Sci. 2000, 55, 1225.
5. Wold, S.; Sjöström, M. Chemometrics, present and future success. Chemom. Intell. Lab. Syst. 1998, 44, 3.
6. 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. Chem. Eng. Sci. 2002, 57, 3979.
7. Nomikos, P.; MacGregor, J. F. Multivariate SPC charts for monitoring batch processes. Technometrics 1995, 37, 41.
8. Nomikos, P.; MacGregor, J. F. Multiway partial least squares in monitoring batch processes. Chemom. Intell. Lab. Syst. 1995, 30, 97.
9. Wise, B. M.; Gallagher, N. B.; Butler, S. W.; White, D. D., Jr.; Barna, G. G. A comparison of principal component analysis, multiway principal components analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. J. Chemom. 1999, 13, 379.
10. Ü ndey, C.; Tatara, E.; Ç inar, A. Intelligent real-time performance monitoring and quality prediction for batch/fed-batch cultivations. J. Biotechnol. 2004, 108, 61.
11. Martin, E. B.; Morris, A. J. Enhanced bio-manufacturing through advanced multivariate statistical technologies. J. Biotechnol. 2002, 99, 223.
12. Wong, C. W. L.; Escott, R.; Martin, E. B.; Morris, A. J. The integration of spectroscopic and process data for enhanced process performance monitoring. Can. J. Chem. Eng. 2008, 86, 905.
13. Comon, P. Independent component analysis, a new concept. Signal Process. 1994, 36, 287.
14. Hyvärinen, A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks 1999, 10, 626.
15. Hyvärinen, A.; Oja, E. Independent component analysis: algorithms and applications. Neural Networks 2000, 13, 411.
16. Ahn, H. C.; Choi, E. S.; Han, I. G. Extracting underlying meaningful features and canceling noise using independent component analysis for direct marketing. Expert Syst. Appl. 2007, 33, 181.
17. Lee, J.-M.; Yoo, C. K.; Lee, I.-B. Statistical process monitoring with independent component analysis. J. Process Control 2004, 14, 467.
18. Lee, J.-M.; Yoo, C. K.; Lee, I.-B. Statistical monitoring of dynamic processes based on dynamic independent component analysis. Chem. Eng. Sci. 2004, 59, 2995.
19. Yoo, C. K.; Lee, J.-M.; Vanrolleghem, P. A.; Lee, I.-B. On-line monitoring of batch processes using multiway independent component analysis. Chemom. Intell. Lab. Syst. 2004, 71, 151.
20. Kano, M.; Tanaka, S.; Hasebe, S.; Hashimoto, I.; Ohno, H. Monitoring independent components for fault detection. AIChE J. 2003, 49, 969.
21. Kano, M.; Hasebe, S.; Hashimoto, I.; Ohno, H. Evolution of multivariate statistical process control: Application of independent component analysis and external analysis. Comput. Chem. Eng. 2004, 28, 1157.
22. Albazzaz, H.; Wang, X. Z. Statistical process control charts for batch operations based on independent component analysis. Ind. Eng. Chem. Res. 2004, 43, 6731.
23. Lee, J.-M.; Qin, S. J.; Lee, I.-B. Fault detection and diagnosis based on modified independent component analysis. AIChE J. 2006, 52, 3501.
24. Kano, M.; Tanaka, S.; Hasebe, S.; Hashimoto, I.; Ohno, H. Combination of independent component analysis and principal component analysis for multivariate statistical process control. In Proceedings of the International Symposium on Design, Operation and Control of Chemical Plants (PSE Asia 2002); Taipei, Taiwan, Dec 4-6, 2002; p 319.
25. Ge, Z. Q.; Song, Z. H. Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors. Ind. Eng. Chem. Res. 2007, 46, 2054.
26. Widodo, A.; Yang, B.-S. Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Syst. Appl. 2007, 33, 241.
27. Cho, J.-H.; Lee, J.-M.; Choi, S. W.; Lee, D. K.; Lee, I.-B. Fault identification for process monitoring using kernel principal component analysis. Chem. Eng. Sci. 2005, 60, 279.
28. 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. 2004, 59, 223.
29. Lee, J.-M.; Yoo, C. K.; Lee, I.-B. Fault detection of batch processes using multiway kernel principal component analysis. Comput. Chem. Eng. 2004, 28, 1837.
30. 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. 2005, 75, 55.
31. Cui, P. L.; Li, J. H.; Wang, G. Z. Improved kernel principal component analysis for fault detection. Expert Syst. Appl. 2008, 34, 1210-1219.
32. NicolaÏ, B. M.; Theron, K. I.; Lammertyn, J. Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemom. Intell. Lab. Syst. 2007, 85, 243.
33. Yang, J.; Frangi, A. F.; Yang, J.-Y.; Zhang, D.; Jin, Z. KPCA plus LDA: A complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Trans. Pattern Anal. 2005, 27, 230.
34. Cho, H.-W. Nonlinear feature extraction and classification of multivariate process data in kernel feature space. Expert Syst. Appl. 2007, 32, 534.
35. Cho, H.-W. Identification of contributing variables using kernelbased discriminant modeling and reconstruction. Expert Syst. Appl. 2007, 33, 274.
36. Zhang, Y. W.; Qin, S. J. Fault detection of nonlinear process using multiway kernel independent component analysis. Ind. Eng. Chem. Res. 2007, 46, 7780.
37. Zhang, Y. W. Fault detection and diagnosis of nonlinear processes using improved kernel independent component analysis (KICA) and support vector machine (SVM). Ind. Eng. Chem. Res. 2008, 47, 6961.
38. Yang, J.; Gao, X.; Zhang, D.; Yang, J.-Y. Kernel ICA: An alternative formulation and its application to face recognition. Pattern Recogn. 2005, 38, 1784.
39. Wold, S.; Kettaneh, N.; Friden, H.; Holmberg, A. Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemom. Intell. Lab. Syst. 1998, 44, 331.
40. Lee, J.-M.; Yoo, C. K.; Lee, I.-B. Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis. J. Biotechnol. 2004, 110, 119.
41. Lee, J.-M.; Yoo, C. K. On-line batch process monitoring using different unfolding method and independent component analysis. J. Chem. Eng. Jpn. 2003, 36, 1384.
42. Rännar, S.; MacGregor, J. F.; Wold, S. Adaptive batch monitoring using hierarchical PCA. Chemom. Intell. Lab. Syst. 1998, 41, 73.
43. Ü ndey, C.; Ertunç, S.; Ç inar, A. Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis. Ind. Eng. Chem. Res. 2003, 42, 4645.
44. Lu, N. Y.; Gao, F. R.; Wang, F. L. A sub-PCA modeling and online monitoring strategy for batch processes. AIChE J. 2004, 50, 255.
45. Dong, D.; McAvoy, T. J. Multi-stage Batch Process Monitoring. In Proceedings of the American Control Conference; IEEE Press: Piscataway, NJ, 1995; p 1857.
46. Lennox, B.; Hiden, H.; Montague, G.; Kornfeld, G.; Goulding, P. Application of multivariate statistical process control to batch operations. Comput. Chem. Eng. 2000, 24, 291.
47. Lane, S.; Martin, E. B.; Kooijmans, R.; Morris, A. J. Performance monitoring of a multi-product semi-batch process. J. Process Control 2001, 11, 1.
48. Ündey, C.; Cinar, A. Statistical monitoring of multistage, multiphase batch processes. IEEE Control Syst. Mag. 2002, 22, 40.
49. Ramaker, H.-J.; van Sprang, E. N. M.; Westerhuis, J. A.; Smilde, A. K. Fault detection properties of global, local and time evolving models for batch process monitoring. J. Process Control 2005, 15, 799.
50. Camacho, J.; Picó, J. Online monitoring of batch processes using multi-phase principal component analysis. J. Process Control 2006, 16, 1021.
51. Zhao, C. H.; Wang, F. L.; Mao, Z. Z.; Lu, N. Y.; Jia, M. X. Improved knowledge extraction and phase-based quality prediction for batch processes. Ind. Eng. Chem. Res. 2008, 47, 825.
52. Haykin, S. Neural Networks; Prentice-Hall: Englewood Cliffs, NJ, 1999.
53. Christianini, N., Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods; Cambridge University Press: Cambridge, U.K., 2000.
54. Silverman, B. W. Density Estimation for Statistics and Data Analysis; Chapman & Hall: London, 1986.
55. Ryan, T. P. Statistical Methods for Quality Improvement; Wiley: New York, 1989.
56. Chen, Q.; Wynne, R. J.; Goulding, P.; Sandoz, D. The application of principal component analysis and kernel density estimation to enhance process monitoring. Control Eng. Pract. 2000, 8, 531.
57. Martin, E. B.; Morris, A. J. Non-parametric confidence bounds for process performance monitoring charts. J. Process Control 1996, 6, 349.
58. Birol, G.;Ü ndey, C.; Parulekar, S. J.; Cinar, A. A morphologically structured model for penicillin production. Biotechnol. Bioeng. 2002, 77, 538.
59. Birol, G.; Ü ndey, C.; Cinar, A. A modular simulation package for fed-batch fermentation: Penicillin production. Comput. Chem. Eng. 2002, 26, 1553.