Application of principal component analysis for monitoring and disturbance detection of a hydrotreating process

Industrial and Engineering Chemistry Research

Volumn 47, Issue 18, 2008, Pages 6972-6982

  Bezergianni, Stella ^a   Kalogianni, Aggeliki ^a  

^a Centre for Research Technology Hellas   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

PILOT PLANTS; PROCESS ENGINEERING;

DIMENSIONALITY REDUCTION; DISTURBANCE DETECTION; HYDRO-PROCESSING; HYDROTREATING; MULTIVARIATE PROCESSES; PRINCIPAL COMPONENTS; PROCESS VARIABILITY; PROCESS VARIABLES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 53349142118     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie0714605     Document Type: Article

References (21)

1. 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.
2. Kourti, T.; MacGregor, J. F. Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemom. Intell. Lab. Syst. 1995, 28, 3.
3. Martin, E. B.; Morris, A. J.; Papazoglou, M. C.; Kiparissides, C. Batch process monitoring for consistent production. Comput. Chem. Eng. 1996, 20 (Suppl.1), S599.
4. Kresta, J. V.; MacGregor, J. F.; Marlin, T. E. Multivariate Statistical Monitoring of Process Operating Performance. Can. J. Chem. Eng. 1991, 69, 35.
5. Chen, J.; Bondoni, J. A.; Romagnoli, J. A. Robust PCA and Normal Region in Multivariate Statistical Process Monitoring. AIChE J. 1996, 42, 3563.
6. MacGregor, J. F.; Jaeckle, C.; Kiparissides, C.; Koutoudi, M. Process Monitoring and Diagnosis of Multiblock PLS Methods. AIChE J. 1994, 40, 826.
7. Nomikos, P. Detection and Diagnosis of Abnormal Batch Operations Based on Multi-way Principal Component Analysis. ISA Trans. 1996, 35, 259.
8. Rannar, S.; MacGregor, J. F.; Wold, S. Adaptive batch monitoring using hierarchical PCA. Chemom. Intell. Lab. Syst. 1998, 41, 73.
9. Ku, W.; Storer, R. H.; Georgakis, C. Disturbance Detection and Isolation by Dynamic Principal Components Analysis. Chemom. Intell. Lab. Syst. 1995, 30, 179.
10. Tsung, F. Statistical monitoring and diagnosis of automatic controlled processes using dynamic PCA. Int. J. Prod. Res. 2000, 38, 625.
11. Rotem, Y.; Wachs, A.; Lewin, D. R. Ethylene Compressor Monitoring Using Model-based PCA. AIChE J. 2000, 46, 1-825.
12. Bakshi, B. R. Multiscale PCA with application to Multivariate Statistical Process Monitoring. AIChE J. 1998, 44, 1596.
13. Duchesne, C.; Kourti, T.; MacGregor, J. F. Multivariate SPC for startups and grade transitions. AIChE J. 2002, 48, 2890.
14. Albazzaz, H.; Wang, X. Z. Statistical process control charts for batch operations based on independent component analysis. Ind. Eng. Chem. Res. 2004, 43, 6731.
15. Georgakis, C., Steadman, B., Liotta, V. Decentralized PCA charts for performance assessment of Plant-Wide Control Structures; IEEE Press: Piscataway, NJ, 1996; p 97.
16. 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. Chemom. Intell. Lab. Syst. 2000, 50, 243.
17. Jackson, J., E., A user's guide to Principal Component Analysis; John Wiley & Sons: New York, 1990.
18. Beebe, K. R.; Pell, R. J.; Seasholtz, M. B. Chemometrics: A Practical Guide; John Wiley & Sons: New York, 1998.
19. Chiang, L. H.; Russell, E. L.; Braatz, R. D. Fault Detection and Diagnosis in Industrial Systems; Springer-Verlag: London, 2001.
20. Himes, D.; Georgakis, C.; Storer, R. H. Determination of the Number of Principal Components for Disturbance Detection and Isolation. In Proceedings of American Control Conference, Baltimore, MD, June 29-July 1, 1994; p 1279.
21. http://www.osisoft.com/Products/PI%20System/.