An online application of dynamic PLS to a dearomatization process

Computers and Chemical Engineering

Volumn 28, Issue 12, 2004, Pages 2611-2619

  Komulainen, Tiina ^a   Sourander, Mauri ^b   Jämsä Jounela, Sirkka Liisa ^a  

^a AALTO UNIVERSITY   (Finland)

^b Neste Oil Oyj   (Finland)

Author keywords

Computed variables; Distillation curve analyser; Dynamic PLS; Flash point analyser; Industrial real time application; Online analysers; Online process monitoring; Time lagged variables; Trickle bed reactor

Indexed keywords

ACCIDENT PREVENTION; MAINTENANCE; ONLINE SYSTEMS; REAL TIME SYSTEMS;

DEAROMATIZATION; PROCESS EQUIPMENT;

PROCESS ENGINEERING;

COMPUTER;

CHEMICAL MODIFICATION; DATA ANALYSIS; DEAROMATIZATION; DISTILLATION; DYNAMICS; ONLINE SYSTEM; PARTIAL LEAST SQUARE; PROCESS MODEL; PROCESS MONITORING; REVIEW;

EID: 9544254890     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2004.07.014     Document Type: Article

References (24)

1. E. Alhoniemi, J. Hollmén, O. Simula, and J. Vesanto Process monitoring and modeling using the self-organizing map Integrated Computer-Aided Engineering 6 1999 3 14
2. A. Berglund, and S. Wold INLR: Implicit non-linear latent variable regression Journal of Chemometrics 11 1997 141 156
3. G. Chen, T.J. McAvoy, and M.J. Piovoso A multivariate statistical controller for online quality improvement Journal of Process Control 8 1998 139 149
4. B.S. Dayal, and J.F. MacGregor Recursive exponentially weighted PLS and its applications to adaptive control and prediction Journal of Process Control 7 1997 169 179
5. D. Dong, and T.J. McAvoy Nonlinear principal component analysis based on principal curves and neural networks Computers and Chemical Engineering 20 1996 65 78
6. R.W. Gerlach, B.R. Kowalski, and H. Wold Partial least squares modelling with latent variables Analytica Chimica Acta 112 1979 417 421
7. K. Helland, H.E. Berntsen, O.S. Borgen, and H. Martens Recursive algorithm for partial least squares regression Chemometrics and Intelligent Laboratory Systems 14 1992 129 137
8. J.E. Jackson Principal component and factor analysis: Part I - principal components Journal of Quality Technology 12 1980 201 213
9. S.-L. Jämsä-Jounela, M. Vermasvuori, P. Endén, and S. Haavisto A process monitoring system based on the Kohonen self-organizing maps Control Engineering Practice 11 2003 83 92
10. Kämpjärvi, P., Sourander, M., & Jämsä-Jounela, S.-L. (2004). Online analyser validation and process fault diagnosis for ethylene cracking process under MPC feedback. Industrial Engineering and Chemistry Research, submitted for publication.
11. T. Kohonen Self-organizing maps 3rd ed. 2001 Springer Berlin
12. Komulainen, T. (2003). Online monitoring of a dearomatization process. Master's thesis, Helsinki University of Technology, Espoo, p. 135.
13. M.A. Kramer Nonlinear principal component analysis using autoassociative neural networks AIChE Journal 37 1991 233 243
14. W. Ku, R.H. Storer, and C. Georgakis Disturbance detection and isolation by dynamic principal component analysis Chemometrics and Intelligent Laboratory Systems 30 1995 179 196
15. B. Lennox, and D. Sandoz Recent experiences in the industrial exploitation of principle component based fault detection methods Proceedings of the IEEE International Symposium on Computer Aided Control System Design Glasgow 2002 xxxv xliv
16. W. Li, H.H. Yue, S. Valle-Cervantes, and S.J. Qin Recursive PCA for adaptive process monitoring Journal of Process Control 10 2000 471 486
17. M. Misra, H.H. Yue, S.J. Qin, and C. Ling Multivariate process monitoring and fault diagnosis by multi-scale PCA Computers and Chemical Engineering 26 2002 1281 1293
18. S.J. Qin, and T.J. McAvoy Nonlinear PLS modelling using neural networks Computers and Chemical Engineering 16 1992 379 391
19. R. Rallo, J. Ferre-Giné, A. Arenas, and F. Giralt Neural virtual sensor for the inferential prediction of product quality from process variables Computers and Chemical Engineering 26 2002 1735 1754
20. R. Shao, F. Jia, E.B. Martin, and A.J. Morris Wavelets and non-linear principal components analysis for process monitoring Control Engineering Practice 7 1999 865 879
21. V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S.N. Kavuri A review of process fault detection and diagnosis Part I: Quantitative model-based methods Computers and Chemical Engineering 27 2003 327 346
22. V. Venkatasubramanian, R. Rengaswamy, S.N. Kavuri, and K. Yin A review of process fault detection and diagnosis Part III: Process history based methods Computers and Chemical Engineering 27 2003 327 346
23. D. Wienke, W. van den Broek, L. Buydens, T. Huth-Fehre, R. Feldhoff, and T. Kantimm Adaptive resonance theory based neural network for supervised chemical pattern recognition (FuzzyARTMAP) Part 2: Classification of post-consumer plastics by remote NIR spectroscopy using an InGaAs diode array Chemometrics and Intelligent Laboratory Systems 32 2 1996 165 176
24. S. Wold, M. Sjöström, and L. Eriksson PLS-regression: A basic tool of chemometrics Chemometrics and Intelligent Laboratory Systems 58 2001 109 130