Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing

Industrial and Engineering Chemistry Research

Volumn 51, Issue 18, 2012, Pages 6416-6428

  Ni, Wangdong ^a,b   Tan, Soon Keat ^a,c   Ng, Wun Jern ^b,c   Brown, Steven D ^d  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^d UNIVERSITY OF DELAWARE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EMBEDDED NOISE; GAUSSIAN PROCESS REGRESSION; INDUSTRIAL PROCESSS; MODEL PREDICTION; MODELING STRATEGY; MODELING STUDIES; MOVING-WINDOW; NON-LINEAR DYNAMIC SYSTEMS; NON-LINEARITY; PREDICTIVE CAPABILITIES; PREDICTIVE PERFORMANCE; PROCESS CHANGE; PROCESS DYNAMICS; PROCESS OUTPUT; PROCESS PARAMETERS; PROPYLENE POLYMERIZATION; SIMULTANEOUS REMOVAL; SOFT SENSORS; TIME-VARYING CHANGES; TIME-VARYING VARIANCE;

CATALYST ACTIVITY; NONLINEAR DYNAMICAL SYSTEMS; POLYMERIZATION;

DYNAMICS;

EID: 84861071787     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie201898a     Document Type: Article

References (39)

1. Larimore, W. E. Canonical variate analysis for system identification, filtering and adaptive control. In Proceedings of the 29th IEEE Conference on Decision and Control; IEEE Press: Piscataway, NJ, 1990; Vol. 1, pp 596-694.
2. Juricek, B. C.; Seborg, D. E.; Larimore, W. E. Fault detection using canonical variate analysis Ind. Eng. Chem. Res. 2004, 43, 458-474 (Pubitemid 38090814)
3. Shi, R.; MacGregor, J. F. Modeling of dynamic systems using latent variable and subspace methods J. Chemom. 2000, 14, 423-439
4. Negiz, A.; Çinar, A. PLS, balanced, and canonical variate realization techniques for identifying VARMA models in state space Chemom. Intell. Lab. Syst. 1997, 38, 209-221 (Pubitemid 27512496)
5. Kadlec, P.; Grbić, R.; Gabrys, B. Review of adaptation mechanisms for data-driven soft sensors Comput. Chem. Eng. 2010, 35, 1-24
6. McAvoy, T. Intelligent "control" applications in the process industries Annu. Rev. Control 2002, 26, 75-86 (Pubitemid 35170481)
7. Fortuna, L.; Rizzo, A.; Sinatra, M.; Xibilia, M. G. Soft analyzers for a sulfur recovery unit Control Eng. Pract. 2003, 11, 1491-1500 (Pubitemid 37551617)
8. Simoglou, A.; Martin, E. B.; Morris, A. J. A comparison of canonical variate analysis and partial least squares for the identification of dynamic processes. In Proceedings of the American Control Conference; IEEE Press: Piscataway, NJ, 1999; pp 832-837. (Pubitemid 32078845)
9. Simoglou, A.; Martin, E. B.; Morris, A. J. Dynamic multivariate statistical process control using partial least squares and canonical variate analysis Comput. Chem. Eng. 1999, 23 (Suppl.) S277-S280
10. Helland, K.; Berntsen, H. E.; Borgen, O. S.; Martens, H. Recursive algorithm for partial least squares regression Chemom. Intell. Lab. Syst. 1992, 14, 129-137
11. Dayal, B. S.; MacGregor, J. F. Recursive exponentially weighted PLS and its applications to adaptive control and prediction J. Process Control 1997, 7, 169-179 (Pubitemid 127372162)
12. Qin, S. J. Recursive PLS algorithms for adaptive data modeling Comput. Chem. Eng. 1998, 22, 503-514 (Pubitemid 128714151)
13. Mu, S.; Zeng, Y.; Liu, R.; Wu, P.; Su, H.; Chu, J. Online dual updating with recursive PLS model and its application in predicting crystal size of purified terephthalic acid (PTA) process J. Process Control 2006, 16, 557-566
14. Ahmed, F.; Nazier, S.; Yeo, Y. K. A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant Korean J. Chem. Eng. 2009, 26, 14-20
15. Li, C.; Ye, H.; Wang, G.; Zhang, J. A recursive nonlinear PLS algorithm for adaptive nonlinear process modeling Chem. Eng. Technol. 2005, 28, 141-152 (Pubitemid 40367847)
16. Kadlec, P.; Gabrys, B. Local learning-based adaptive soft sensor for catalyst activation prediction AIChE J. 2011, 57, 1288-1301
17. Radhakrishnan, V.; Mohamed, A. Neural networks for the identification and control of blast furnace hot metal quality J. Process Control 2000, 10, 509-524
18. Johansen, T. A.; Shorten, R.; Murray-Smith, R. On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models IEEE Trans. Fuzzy Syst. 2000, 8, 297-313
19. Bukkapatnam, S. T. S.; Cheng, C. Forecasting the evolution of nonlinear and nonstationary systems using recurrence-based local Gaussian process models Phys. Rev. E. 2010, 82, 056206-1-12
20. Grancharova, A.; Kocijan, J.; Johansen, T. A. Explicit stochastic predictive control of combustion plants based on Gaussian process models Automatica 2008, 44, 1621-1631
21. di Sciascio, F.; Amicarelli, A. N. Biomass estimation in batch biotechnological process by Bayesian Gaussian process regression Comput. Chem. Eng. 2008, 32, 3264-3273
22. Brahim-Belhouari, S.; Bermak, A. Gaussian process for nonstationary time series prediction Comput. Stat. Data Anal. 2004, 47, 705-712
23. Ažman, K.; Kocijan, J. Application of Gaussian processes for black-box modeling of biosystems ISA Trans. 2007, 46, 443-457 (Pubitemid 47284076)
24. Ažman, K.; Kocijan, J. Dynamical systems identification using Gaussian process models with incorporated local models Eng. Appl. Artif. Intell. 2011, 24, 398-408
25. O'Hagan, A. Curve fitting and optimal design for prediction (with discussion) J. R. Stat. Soc. B 1978, 40, 1-42
26. Neal, R. M. Bayesian Learning for Neural Networks; Springer-Verlag: NewYork. 1996.
27. Chen, T.; Morris, J.; Martin, E. Gaussian process regression for multivariate spectroscopic calibration Chemom. Intell. Lab. Syst. 2007, 87, 59-71
28. Chen, T.; Wang, B. Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers Neurocomputing 2010, 73, 2718-2726
29. Gregorčič, G.; Lightbody, G. Nonlinear system identification: From multiple-model networks to Gaussian processes Eng. Appl. Artif. Intell. 2008, 21, 1035-1055
30. Deisenroth, M. P.; Rasmussen, C. E.; Peters, J. Gaussian process dynamic programming Neurocomputing 2009, 72, 1508-1524
31. Gregorčič, G.; Lightbody, G. Gaussian process approach for modeling of nonlinear systems Eng. Appl. Artif. Intell. 2009, 22, 522-533
32. Ni, W.; Tan, S. K.; Ng, W. J. Recursive GPR for nonlinear dynamic process modeling Chem. Eng. J. 2011, 173, 636-643
33. Wang, X.; Kruger, U.; Irwin, G. W. Process monitoring approach using fast moving window PCA Ind. Eng. Chem. Res. 2005, 44, 5691-5702
34. Choi, S. W.; Martin, E. B.; Morris, A. J.; Lee, I. B. Adaptive multivariate statistical process for monitoring time-varying processes Ind. Eng. Chem. Res. 2006, 45, 3108-3118
35. Savitzky, A.; Golay, M. J. E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures Anal. Chem. 1964, 36, 1627-1639
36. Rasmussen, C. E. Evaluation of Gaussian processes and other methods for non-linear regression. Ph.D. Thesis, University of Toronto, Toronto, Canada, 1996.
37. Rasmussen, C. E.; Williams, C. K. I. Gaussian Processes for Machine Learning; The MIT Press: Cambridge, MA, 2006.
38. Akaike, H. Canonical correlation analysis of time series and the use of an information criterion. In System Identification: Advances and Case Studies; Mehra, R. K.; Lainiotis, D. G., Eds.; Academic Press: New York, 1976; pp 27-96.
39. Ni, W.; Tan, S. K.; Ng, W. J.; Brown, S. D. A localized, adaptive recursive partial least squares regression for dynamic system modeling. Ind. Eng. Chem. Res., manuscript submitted.