Improved knowledge extraction and phase-based quality prediction for batch processes

Industrial and Engineering Chemistry Research

Volumn 47, Issue 3, 2008, Pages 825-834

  Zhao, Chunhui ^a   Wang, Fuli ^a   Mao, Zhizhong ^a   Lu, Ningyun ^a,b   Jia, Mingxing ^a  

^a NORTHEASTERN UNIVERSITY   (China)

^b NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOCORRELATION; CORRELATION THEORY; PARAMETER ESTIMATION; REDUNDANCY;

BATCH PROCESSES; KNOWLEDGE EXTRACTION; QUALITY PREDICTION;

PROCESS CONTROL;

ALGORITHMS; AUTOCORRELATION; CORRELATION THEORY; PARAMETER ESTIMATION; PROCESS CONTROL; REDUNDANCY;

EID: 39449135610     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie0707063     Document Type: Article

References (29)

1. Jackson, J. E. A User's Guide to Principal Components; Wiley: New York, 1991.
2. Geladi, P.; Kowalshi, B. Partial least squares regression: A tutorial. Anal. Chim. Acta 1986, 185, 1.
3. Nomikos, P.; MacGregor, J. F. Monitoring batch processes using multiway principal component analysis. AIChE J. 1994, 40, 1361.
4. Nomikos, P.; MacGregor, J. F. Multi-way partial least squares in monitoring batch processes. Chemom. Intell. Lab. Syst. 1995, 30, 97.
5. Duchesne, C.; MacGregor, C. D. Multivariate analysis and optimization of process variable trajectories for batch processes. Chemom. Intell Lab. Syst. 2000, 51, 125.
6. Chu, Y.-H.; Lee, Y.-H.; Han, C. Improved Quality Estimation and Knowledge Extraction in a Batch Process by Bootstrapping-Based Generalized Variable Selection. Ind. Eng. Chem. Res. 2004, 43, 2680.
7. Lu, N. Y.; Gao, F. R. Stage-Based Process Analysis and Quality Prediction for Batch Processes. Ind. Eng. Chem. Res. 2005, 44, 3547.
8. Lu, N. Y.; Gao, F. R. Stage-Based Online Quality Control for Batch Processes. Ind. Eng, Chem. Res. 2006, 45, 2272.
9. Zhao, S. J.; Zhang, J.; Xu, Y. M. Monitoring of Processes with Multiple Operating Modes through Multiple Principle Component Analysis Models. Ind. Eng. Chem. Res. 2004, 43, 7025.
10. Zhao, S. J.; Zhang, J.; Xu, Y. M. Performance monitoring of processes with multiple operating modes through multiple PLS models. J. Process Control 2006, 16, 763.
11. Doan, X.-T.; Srinivasan, R.; Bapat, P. M.; Wangikar, P. P. Detection of phase shifts in batch fermentation via statistical analysis of the online measurements: A case study with rifamycin B fermentation. J. Biotechnol. 2007, 132, 156-166.
12. 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.
13. Wang, H. Partial Least-Squares Regression-Method and Applications; National Defence Industry Press: Beijing, China, 1999.
14. Kleinbaum, D. G.; Kupper, L. L.; Muller, K. E.; Nizam, A. Applied Regression Analysis and Other Multivariable Methods, third ed.; China Machine Press: Beijing, China, 2003.
15. Kutner, M. H.; Nachtsheim, C. J.; Neter, J. Applied Linear Regression Models, fourth ed.; Higher Education Press: Beijing, China, 2005.
16. Walmsley, A. D. Improved variable selection procedure for multivariate linear regression. Anal. Chim. Acta 1997, 354, 225.
17. Pudil, P.; Novovicvá, J.; Kittler, J. Floating search methods in feature selection. Pattern Recognit. Lett. 1994, 15, 1119.
18. Höskuldsson, A. Variable and subset selection in PLS regression. Chemom. Intell. Lab. Syst. 2001, 55, 23.
19. Eklöv, T.; Lundström, I. Selection of variables for interpreting multivariate gas sensor data. Anal. Chim. Acta 1999, 381, 221.
20. 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.
21. Wise, B. M. A comparison of multiway principal components analysis, tri-linear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. Presented at International Chemometrics Research Meeting, ICRM98, Veldhoven, The Netherlands, 1998.
22. Westerhuis, J. A.; Kourti, T.; Macgregor, J. F. Comparing alternative approaches for multivariate statistical analysis of batch process data. Chemom. J. 1999, 13, 397.
23. van Sprang, E. N. M.; Ramaker, H.-J. Critical evaluation of approaches for on-line batch process monitoring. Chem. Eng. Sci. 2002, 57, 3979.
24. Lee, J.-M.; Yoo, C. K.; Lee, I.-B. Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis. Biotechnology 2004, 110, 119.
25. Nomikos, P.; MacGregor, J. F. Multivariate SPC Charts for Monitoring Batch Processes. Technometrics 1995, 37, 41.
26. Cho, H.-W.; Kim, K.-J. A method for predicting future observations in the monitoring of a batch process. J. Qual. Technol. 2003, 35, 59.
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. Yang, Y.; Gao, F. Adaptive control of injection velocity of thermoplastic injection molding. Control Eng. Pract. 2000, 5, 1285.
29. Zhao, C. H.; Wang, F. L.; Gao, F. R.; Lu, N. Y.; Jia, M. X. Adaptive Monitoring Method for Batch Processes Based on Phase Dissimilarity Updating with Limited Modeling Data. Ind. Eng. Chem. Res. 2007, 46, 4943.