Process data modeling using modified kernel partial least squares

Chemical Engineering Science

Volumn 65, Issue 24, 2010, Pages 6353-6361

  Zhang, Yingwei ^a   Teng, Yongdong ^a  

^a KEY LABORATORY OF INTEGRATED AUTOMATION OF PROCESS INDUSTRY   (China)

Author keywords

Chemical analysis; Data modeling; Design; Process control; Regression model; Simulation

Indexed keywords

COMPUTING SPEED; DATA MODELING; DATA PREPROCESSING; EIGENVECTORS; FUTURE OBSERVATIONS; INDUSTRIAL PROCESSS; ITERATIVE CALCULATION; LARGEST EIGENVALUES; LATENT STRUCTURES; MODIFIED KERNELS; MULTIVARIATE DATA; NON-LINEARITY; NONLINEAR DATA; ORTHOGONAL PROJECTION; PRECISION LIMITS; PREDICTION PERFORMANCE; PRINCIPAL COMPONENTS; PROCESS DATA; QUALITY PREDICTION; REGRESSION FITTING; REGRESSION MODEL; SIGNAL FILTERING; SIMULATION; SYSTEMATIC VARIATION;

ASSOCIATION RULES; CHEMICALS; COMPUTER SIMULATION; DATA HANDLING; EIGENVALUES AND EIGENFUNCTIONS; FORECASTING; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; STRUCTURAL DESIGN;

PROCESS CONTROL;

EID: 78149465043     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2010.09.009     Document Type: Article

References (37)

1. Chiang L.H., Russell F.L., Braatz R.D. Fault Detection and Diagnosis in Industrial Systems 2001, Springer, London.
2. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 2000, Cambridge University Press, UK.
3. Eriksson L., Trygg J., Johansson E., Bro R., Wold S. Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data. Anal. Chim. Acta 2000, 420:181-195.
4. Fearn T. On orthogonal signal correction. Chemometrics and Intelligent Laboratory Systems 2000, 50:47-52.
5. Feudale R.N., Tan H.W., Brown S.D. Piecewise orthogonal signal correction. Chemometrics and Intelligent Laboratory Systems 2002, 63:129-138.
6. Frank I.E. A non-linear PLS model. Chemometrics and Intelligent Laboratory Systems 1990, 8:109-119.
7. Geladi P., Kowalski B.R. Partial least squares regression: a tutorial. Analytica Chimica Acta 1986, 185:1-17.
8. Goicoechea H.C., Olivieri A.C. A comparison of orthogonal signal correction and net analyte preprocessing methods. Theoretical and experimental study. Chemometrics and Intelligent Laboratory Systems 2001, 56:73-81.
9. Haykin S. Neural Networks 1999, Prentice-Hall, Englewood Cliffs, NJ.
10. Holcomb T.R., Morari M. PLS/neural networks. Computers and Chemical Engineering 1992, 16:393-411.
11. Hoskuldsson A. PLS regression methods. Journal of Chemometrics 1988, 2:211-228.
12. Höskuldsson A. Variable and subset selection in PLS regression. Chemometrics and Intelligent Laboratory Systems 2001, 55:23-38.
13. Kvalheim O.M. The latent variable. Chemometrics and Intelligent Laboratory Systems 1992, 14:1-3.
14. Kyungpil K., Lee J.-M., Lee I.-B. A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction. Chemometrics and Intelligent Laboratory Systems 2005, 79:22-30.
15. Lee G., Han C.H., Yoon E.S. Multiple-fault diagnosis of the Tennessee Eastman process based on system decomposition and dynamic PLS. Industrial and Engineering Chemistry Research 2004, 43:8037-8048.
16. Lee J.-M., Yoo C., Choi S.W., Vanrolleghem P.A., Lee I.-B. Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science 2004, 59:223-234.
17. Malthouse E.C., Tamhane A.C., Mah R.S.H. Nonlinear partial least squares. Computers and Chemical Engineering 1997, 21:875-890.
18. Mika S., Schölkopf B., Smola A.J., Müller K.-R., Scholz M., Rätsch G. Kernel PCA and de-noising in feature spaces. Advances in Neural Information Processing Systems 1999, 11:536-542.
19. Qin S.J., McAvoy T.J. Nonlinear PLS modeling using neural networks. Computers and Chemical Engineering 1992, 16:379-391.
20. Rännar S., Lindgren F., Geladi P., Wold S. A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: theory and algorithm. Journal of Chemometrics 1994, 8:111-125.
21. Rosipal R. Kernel partial least squares for nonlinear regression and discrimination. Neural Network World 2003, 13:291-300.
22. Rosipal R., Trejo L.J. Kernel partial least squares regression in reproducing Kernel Hilbert space. Journal of Machine Learning Research 2001, 2:97-123.
23. Rosipal, R., Trejo, L.J., Matthews, B., Kernel PLS-SVC for linear and nonlinear classification. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington, DC, 2003, pp. 640-647.
24. Sjöblom J., Svensson O., Josefson M., Kullberg H., Wold S. An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra. Chemometrics and Intelligent Laboratory Systems 1998, 44:229-244.
25. Svensson O., Kourti T., MacGregor J.F. An investigation of orthogonal signal correction algorithms and their characteristics. Journal of Chemometrics 2002, 16:176-188.
26. Trygg, J., Parsimonious multivariate models. Ph.D. Thesis, Umea University, 2001, pp. 19-54
27. Trygg J., Wold S. Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics 2002, 16:119-128.
28. Westerhuis J.A., de J.S., Smilde A.K. Direct orthogonal signal correction. Chemometrics and Intelligent Laboratory Systems 2001, 56:13-25.
29. Wold H. Soft modeling. The basic design and some extensions. Systems under Indirect Observation 1982, vol. 2:1-53. North-Holland, Amsterdam.
30. Wold S. Nonlinear partial least squares modelling: II. Spline inner relation. Chemometrics and Intelligent Laboratory Systems 1992, 14:71-84.
31. Wold S., Kettaneh-Wold N., Skagerberg B. Nonlinear PLS modeling. Chemometrics and Intelligent Laboratory Systems 1989, 7:53-65.
32. Wold S., Antti H., Lindgren F., Öhman J. Orthogonal signal correction of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems 1998, 44:175-185.
33. Wold S., Trygg J., Berglund A., Antii H. Some recent developments in PLS modeling. Chemometrics and Intelligent Laboratory Systems. 2001, 58:131-150.
34. Wold S., Sjöström M., Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 2001, 58:109-130.
35. Zhang Y., Joe Qin S. Improved nonlinear fault detection technique and statistical analysis. AIChE Journal 2008, 54(12):3207-3220.
36. Zhang Y., Joe Qin.S. Fault diagnosis and isolation of multi-input-multi-output networked control systems. Industrial & Engineering Chemistry Research 2008, 47:2636-2642.
37. Zhang Y., Joe Qin S. Adaptive actuator fault compensation for linear systems with matching and unmatching uncertainties. Journal of Process Control 2009, 16(9):985-990.