Multiway gaussian mixture model based multiphase batch process monitoring

Industrial and Engineering Chemistry Research

Volumn 48, Issue 18, 2009, Pages 8585-8594

  Yu, Jie ^a   Qin, S Joe ^b  

^a The University of Texas at Austin   (United States)

^b University of Southern California ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BASIC IDEA; BATCH PROCESS; BATCH PROCESS MONITORING; DATA MATRICES; DETECTION RATES; FAULT SENSITIVITY; GAUSSIAN MIXTURE MODEL; MONITORING APPROACH; MULTIPLE CLUSTERS; MULTIWAY PRINCIPAL COMPONENT ANALYSIS; OPERATION PHASE; OPERATION PHASIS; PENICILLIN FERMENTATION PROCESS; PROBABILITY INDEX; PROCESS FAULTS; SAMPLING POINTS; SAMPLING TIME;

COMMUNICATION CHANNELS (INFORMATION THEORY); FAULT DETECTION; FERMENTATION; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; PROCESS MONITORING;

BATCH DATA PROCESSING;

EID: 70349329819     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie900479g     Document Type: Article

References (30)

1. Wise, B. M., Gallagher, N. B., Butler, S. W., White, D. D., Barna, G. G. A Comparison of Principal Component Analysis, Multiway PrincipalComponent Analysis, Trilinear Decomposition and Parallel Factor Analysisfor Fault Detection in a Semiconductor Etch Process. J. Chemom. 1999, 13, 379-396.
2. Yue, H., Qin, S., Markle, R., Nauert, C., Gatto, M. Fault detectionof plasma etchers using optical emission spectra. IEEE Trans. Semicond.Manuf. 2000, 13, 374-385.
3. Gunther, J. C., Conner, J. S., Seborg, D. E. Fault detection anddiagnosis in an industrial fed-batch cell culture process. Biotechnol. Prog.2007, 23, 851-857.
4. Doan, X., Srinivasan, R. Online monitoring of multi-phase batch processes using phase-based multivariate statistical process control. Comput. Chem. Eng. 2008, 32, 230-243.
5. Nomikos, P., MacGregor, J. F. Monitoring of batch processes using multi-way principal component analysis. AIChE J. 1994, 40, 1361-1375.
6. Kourti, T., Nomikos, P., MacGregor, J. F. Analysis, monitoring, and fault diagnosis of batch processes using multi-block and multi-way PLS. J. Process Control 1995, 5, 277-284.
7. Nomikos, P., MacGregor, J. Multivariate SPC charts for monitoring batch processes. Technometrics 1995, 37, 41-59.
8. Lennox, B., Hiden, H. G., Montague, G. A., Kornfeld, G., Goulding, P. R. Process monitoring of an industrial fed-batch fermentation. Biotechnol. Bioeng. 2001, 74, 125-135.
9. 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, 151-163.
10. Çinar, A., Parulekar, S., Ündey, C., Birol, G. Batch Fermentation: Modeling, Monitoring, Control; CRC Press: New York, 2003.
11. 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, 119-136.
12. Flores-Cerrillo, J., MacGregor, J. F. Multivariate monitoring of batch processes using batch-to-batch information. AIChE J. 2004, 50, 1219- 1228.
13. Lu, N., Gao, F., Wang, F. Sub-PCA Modeling and On-line Monitoring Strategy for Batch Processes. AIChE J. 2004, 50, 255-259. (Pubitemid 38197428)
14. Yoo, C. K., Lee, D. S., Vanrolleghem, P. A. Application of multiway ICA for on-line process monitoring of a sequencing batch reactor. Water Res. 2004, 38, 1715-1732.
15. Ündey, C., Çinar, A. Statistical monitoring of multistage, multiphase batch processes. IEEE Control Syst. Mag. 2002, 10, 40-52.
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, 93-109.
17. Ündey, C., Ertunç, S., Çinar, A. Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis. Ind. Eng. Chem. Res. 2003, 42, 4645-4658.
18. Yoo, C. K., Villez, K., Lee, I. B., Rosén, C., Vanrolleghem, P. A. Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor. Biotechnol. Bioeng. 2007, 96, 687-701.
19. Hyvärinen, A., Karhunen, J., Oja, E. Independent Component Analysis; John Wiley & Sons, Inc.: New York, 2001.
20. Lee, J. M., Qin, S. J., Lee, I. B. Fault detection and diagnosis based on modified independent component analysis. AIChE J. 2006, 52, 3501- 3514.
21. Zhao, C., Wang, F., Mao, Z., Lu, N., Jia, M. Improved Knowledge Extraction and Phase-Based Quality Prediction for Batch Processes. Ind. Eng. Chem. Res. 2008, 47, 825-834. (Pubitemid 351269526)
22. Choi, S. W., Park, J. H., Lee, I. B. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis. Comput. Chem. Eng. 2004, 28, 1377-1387.
23. Yu, J., Qin, S. J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE J. 2008, 54, 1811- 1829.
24. Figueiredo, M. A. F., Jain, A. K. Unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Machine Intell. 2002, 24, 381- 396.
25. De Maesschalck, R., Jouan-Rimbaud, D., Massart, D. L. The Mahalanobis distance. Chemom. Intell. Lab. Syst. 2000, 50, 1-18.
26. Mao, J., Jain, A. K. A self-organizing network for hyperellipsoidal clustering (HEC). IEEE Trans. Neural Networks 1996, 7, 16-29.
27. Box, G. Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-way Classification. Ann. Math. Stat. 1954, 25, 290-302.
28. Qin, S. J. Statistical process monitoring: basics and beyond. J. Chemom. 2003, 17, 480-502.
29. Kassidas, A., MacGregor, J. F., Taylor, P. A. Synchronization of batch trajectories using dynamic time warping. AIChE J. 1998, 44, 864- 875.
30. Birol, G., Ündey, C., Çinar, A. A modular simulation package for fed-batch fermentation: penicillin production. Comput. Chem. Eng. 2002, 26, 1553-1565.