PCA-based modeling and on-line monitoring strategy for uneven-length batch processes

Industrial and Engineering Chemistry Research

Volumn 43, Issue 13, 2004, Pages 3343-3352

  Lu, N ^a,b   Gao, F ^a   Yang, Y ^a   Wang, F ^b  

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

^b NORTHEASTERN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

MONITORING; PROCESS CONTROL;

PCA-BASED MODELING; PROCESS MONITORING;

REACTION KINETICS;

ON-LINE TECHNIQUE;

ALGORITHM; ARTICLE; BATCH PROCESS; ONLINE MONITORING; PROCESS CONTROL; PROCESS MODEL;

EID: 2942672193     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie030736f     Document Type: Article

References (28)

1. Nomikos, P.; MacGregor, J. F. Monitoring of batch processes using multi-way principal component analysis. AIChE J. 1994, 40, 1361.
2. Nomikos, P.; MacGregor, J. F. Multivariate SPC charts for monitoring batch processes. Technometrics 1995, 37, 41.
3. Nomikos, P.; MacGregor, J. F. Multiway partial least squares in monitoring batch processes. Chemom. Intell. Lab. Syst. 1995, 30, 97.
4. Nomikos, P. Detection and diagnosis of abnormal batch operation based on multiway principal component analysis. ISA Trans. 1996, 35, 259.
5. Kourti, T.; Nomikos, P.; MacGregor, J. F. Analysis, monitoring and fault diagnosis of batch process using multiblock and multiway PLS. J. Process Control 1995, 5, 277.
6. Kosanovic, K. A.; Dahl, K. S.; Piovoso, M. J. Improved process understanding using multiway principal component analysis. Ind. Eng. Chem. Res. 1996, 35, 138.
7. Kourti, T.; Lee, J.; MacGregor, J. F. Experiences with industrial applications of projection methods for multivariate statistical process control. Comput. Chem. Eng. 1996, 20, S745.
8. Wold, S.; Kettaneh, N.; Friden, H.; Holmberg, A. Modelling and diagnosis of batch processes and analogous kinetic experiments. Chemom. Intell. Lab. Syst. 1998, 44, 331.
9. Neogi, D.; Schlags, C. E. Multivariate statistical analysis of an emulsion batch process. Ind. Eng. Chem. Res. 1998, 37, 3971.
10. Tates, A. A.; Louwerse, D. J.; Smilde, A. K. Monitoring a PVC batch process with multivariate statistical process control charts. Ind. Eng. Chem. Res. 1999, 38, 4769.
11. Louwerse, D. J.; Smilde, A. K. Multivariate statistical process control of batch processes based on three-way models. Chem. Eng. Sci. 2000, 55, 1225.
12. Zheng, L. L.; McAvoy, T. J.; Huang, Y.; Chen, G. Application of multivariate statistical analysis in batch processes. Ind. Eng. Chem. Res. 2001, 40, 1661.
13. Chen, J. H.; Liu, K. C. On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chem. Eng. Sci. 2002, 57, 63.
14. Ramaker, H. J.; Sprang, van E. N. M.; Gurden, S. P.; Westerhuis, J. A.; Smilde, A. K. Improved monitoring of batch processes by incorporating external information. J. Process Control 2002, 12, 2002.
15. Kassidas, A.; MacGregor, J. F.; Taylor, P. A. Synchronization of batch trajectories using dynamic time warping. AIChE J. 1998, 44, 864.
16. Kourti, T. Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. J. Chemom. 2003, 17, 93.
17. Rothwell, S. G.; Martin, E. B.; Morris, A. J. Comparison of methods for dealing with uneven length batches. In Proceedings of the 7th International Conference on Computer Application in Biotechnology (CAB7); Yoshida, T., Shioya, S., Eds.; Elsevier Science: New York, 1998; pp 387-392.
18. Itakura, F. Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust., Speech Signal Process. 1975, ASSP-23, 67.
19. Sakoe, H.; Chiba, S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust., Speech Signal Process. 1978, ASSP-26, 43.
20. Kiers, H. A. L.; ten Berge, J. M. F.; Bro, R. PARAFAC2-Part I. A direct fitting algorithm for the PARAFAC2 model. J. Chemom. 1999, 13, 275.
21. Wise, B. M.; Gallagher, N. B.; Martin, E. B. Application of PARAFAC2 to fault detection and diagnosis in semiconductor etch. J. Chemom. 2001, 15, 285.
22. Bro, R. PARAFAC. Tutorial and applications. Chemom. Intell. Lab. Syst. 1997, 38, 149.
23. Lu, N.; Gao, F.; Wang, F. A sub-PCA modeling and online monitoring strategy for batch processes. AIChE J. 2004, 50, 255.
24. Westerhuis, J. A.; Kourti, T.; MacGregor, J. F. Comparing alternative approaches for multivariate statistical analysis of batch process data. J. Chemom. 1999, 13, 397.
25. Miller, P.; Swanson, R. E.; Heckler, C. E. Contribution plots: A missing link in multivariate quality control. Appl. Math. Comput. Sci. 1998, 8, 775.
26. Rubin, I. I. Injection Molding Theory and Practice; John Wiley & Sons: New York, 1972.
27. Yang, Y.; Gao, F. Cycle-to-cycle and within-cycle adaptive control of nozzle pressures during packing-holding for thermoplastic injection molding. Polym. Eng. Sci. 1999, 39, 2042.
28. Lu, N.; Yang, Y.; Gao F.; Wang, F. Stage-based multivariate statistical analysis for injection molding. In Proceedings of the 7th International Symposium on Advanced Control of Chemical Processes (ADCHEM); Allgower, F., Gao, F., Eds.; Elsevier Science: U.K., 2003.