Multivariate online monitoring of a full-scale biological anaerobic filter process using kernel-based algorithms

Industrial and Engineering Chemistry Research

Volumn 45, Issue 12, 2006, Pages 4335-4344

  Lee, Dae Sung ^a,c   Lee, Min Woo ^a   Woo, Seung Han ^b   Kim, Young Ju ^c   Park, Jong Moon ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

^b Hanbat National University   (South Korea)

^c Kyungpook National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; FILTERS (FOR FLUIDS); LEAST SQUARES APPROXIMATIONS; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; QUALITY CONTROL;

NONLINEAR DATA STRUCTURES; NONLINEAR PROCESSES; PARTIAL LEAST SQUARES (PLS); PROCESS DISTURBANCES;

WASTEWATER TREATMENT;

ALGORITHMS; DATABASE SYSTEMS; FILTERS (FOR FLUIDS); LEAST SQUARES APPROXIMATIONS; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; QUALITY CONTROL; WASTEWATER TREATMENT;

EID: 33745673605     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie050916k     Document Type: Article

References (25)

1. Wise, B. M.; Gallagher, N. B. The Process Chemometrics Approach to Process Monitoring and Fault Detection. J. Process Control 1996, 6, 329.
2. Kourti, T. Process Analysis and Abnormal Situation Detection: from Theory to Practice. IEEE Control Syst. Mag. 2002, Oct, 10.
3. Lee, D. S.; Jeon, C. O.; Park, J. M.; Chang, K. S. Hybrid Neural Network Modeling of a Full-scale Industrial Wastewater Treatment Process. Biotechnol. Bioeng. 2002, 78, 670.
4. Lee, D. S.; Vanrolleghem, P. A. Monitoring of a Sequencing Batch Reactor Using Adaptive Multiblock Principal Component Analysis. Biotechnol. Bioeng. 2003, 82, 489.
5. Lee, D. S.; Park, J. M.; Vanrolleghem, P. A. Adaptive Multiscale Principal Component Analysis for On-line Monitoring of a Sequencing Batch Reactor. J. Biotechnol. 2005, 116, 195.
6. Baffi, G.; Martin, E. B.; Morris, A. J. Non-linear Dynamic Projection to Latent Structures Modeling. Chemom. Intell. Lab. Syst. 2000, 52, 5.
7. Wold, S.; Kettaneh-Wold, N.; Skagerberg, B. Nonlinear PLS Modeling. Chemom. Intell. Lab. Syst. 1989, 7, 53.
8. Kramer, M. A. Nonlinear Principal Component Analysis Using Autoassociative Neural Networks. AIChE J. 1991, 37, 233.
9. Dong, D.; McAvoy, T. J. Nonlinear Principal Component Analysis Based on Principal Curves and Neural Networks. Comput. Chem. Eng. 1996, 20, 65.
10. Jia, F.; Martin, E. B.; Morris, A. J. Non-linear Principal Component Analysis for Process Fault Detection. Comput. Chem. Eng. 1998, 22, S851.
11. Qin, S. J.; McAvoy, T. J. Non-linear PLS Modelling Using Neural Networks. Comput. Chem. Eng. 1992, 23, 395.
12. Lee, D. S.; Vanrolleghem, P. A.; Park, J. M. Parallel Hybrid Modeling Methods for a Full-scale Cokes Wastewater Treatment Plant. J. Biotechnol. 2005, 115, 317.
13. Schölkopf, B.; Smola, A. J.; Müller, K. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Comput. 1998, 10, 1299.
14. Rosipal, R.; Trejo, L. J. Kernel Partial Least Squares Regression in Reproducing Kernel Hubert Space. J. Mach. Learning Res. 2001, 2, 97.
15. Lee, J. M.; Yoo, C.; Choi, S. W.; Vanrolleghem, P. A.; Lee, I. B. Nonlinear Process Monitoring Using Kernel Principal Component Analysis. Chem. Eng. Sci. 2004, 59, 223.
16. Haykin, S. Neural Network; Prentice Hall: Englewood Cliffs, NJ, 1999.
17. Lennox, B.; Montague, G. A.; Hiden, H. G.; Kornfeld, G.; Goulding, P. R. Process Monitoring of an Industrial Fed-batch Fermentation. Biotechnol. Bioeng. 2001, 74, 125.
18. Geladi, P.; Kowalski, B. R. Partial Least Squares Regression: A Tutorial. Anal. Chim. Acta 1986, 185, 1.
19. Wold, S. Cross-validatory Estimation of Components in Factor and Principal Components Models. Technometrics 1978, 20, 397.
20. Krzanowski, W. J. Cross-validation in Principal Component Analysis. Biometrics 1987, 43, 575.
21. Kruger, U.; Antory, D.; Hahn J.; Irwin, G. W.; McCullough, G. Introduction of a nonlinearity measure for principal component models. Comput. Chem. Eng. 2005, 29, 2355.
22. Li, W.; Vue, H.; Valle-Cervants, S.; Qin, S. J. Recursive PCA for Adaptive Process Monitoring. J. Process Control 2000, 10, 471.
23. Leonard, T.; Hsu, J. S. J. Bayesian Methods; Cambridge University Press: New York, 1999.
24. Main, I.; Leonard, T.; Papasouliotis, O.; Hatton, C. G.; Meredith, P. G. One Slope or Two? Detecting Statistically Significant Breaks of Slope in Geophysical Data with Application to Fracture Scaling. Geophys. Res. Lett. 1999, 26, 2801.
25. Seher, T.; Main, I. G. A Statistical Evaluation of a 'Stress-forecast' Earthquake. Geophys. J. Int. 2004, 157, 187.