Local partial least squares based online soft sensing method for multi-output processes with adaptive process states division

Chinese Journal of Chemical Engineering

Volumn 22, Issue 7, 2014, Pages 828-836

  Shao, Weiming ^a   Tian, Xuemin ^a   Wang, Ping ^a  

^a CHINA UNIVERSITY OF PETROLEUM   (China)

Author keywords

F test; Local learning; Multi output process; Online soft sensing; Partial least squares; Process state division

Indexed keywords

INDUSTRIAL PLANTS;

F-TEST; LOCAL LEARNING; MULTI-OUTPUT PROCESS; ONLINE SOFT SENSING; PARTIAL LEAST SQUARE (PLS); PROCESS STATE;

LEAST SQUARES APPROXIMATIONS;

EID: 84906316402     PISSN: 10049541     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cjche.2014.05.003     Document Type: Conference Paper

References (29)

1. L. Fortuna, S. Graziani, A. Rizzo, and G.M. Xibilia Soft sensors for monitoring and control of industrial processes 2007 Springer-Verlag London
2. P. Kadlec, B. Gabrys, and S. Strandt Data-driven soft sensors in the process industry Comput. Chem. Eng. 33 4 2009 795 814
3. Z.Q. Ge, and Z.H. Song Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples AIChE J. 57 8 2011 2109 2118
4. H.J. Galicia, Q.P. He, and W. Jin A reduced order soft sensor approach and its application to continuous digester J. Process Control 21 4 2011 489 500
5. J.C.B. Gonzaga, L.A.C. Meleiro, C. Kiang, and R. Maciel Filho ANN-based soft-sensor for real-time process motoring and control of an industrial polymerization process Comput. Chem. Eng. 33 1 2009 43 49
6. J. Yu A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses Comput. Chem. Eng. 41 1 2012 134 144
7. P. Kadlec, and R. Grbić Review of adaptation mechanisms for data-driven soft sensors Comput. Chem. Eng. 35 1 2011 1 24
8. J. Tang, W. Yu, T.Y. Chai, and L.J. Zhao On-line principal component analysis with application to process modeling Neurocomputing 87 4 2012 167 178
9. S.J. Qin Recursive PLS algorithms for adaptive data modeling Comput. Chem. Eng. 22 4-5 1998 503 514
10. P. Wang, H.G. Tian, X.M. Tian, and D.X. Huang A new approach for online adaptive modeling using incremental support vector regression CISEC J. 61 8 2010 2040 2045
11. O. Carlos, and O. Edward Efficient disk-based k-means clustering for relational databases IEEE Trans. Knowl. Data Eng. 16 8 2004 909 921
12. Y.F. Fu, H.Y. Su, Y. Zhang, and J. Chu Adaptive soft-sensor modeling algorithm based on FCMISVM and its application in PX adsorption separation process Chin. J. Chem. Eng. 16 5 2008 746 751
13. J. Yu Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach Chem. Eng. Sci. 82 2012 22 30
14. K. Fujiwara, M. Kano, S. Hasebe, and A. Takinami Soft-sensor development using correlation-based just-in-time modeling AIChE J. 55 7 2009 1754 1764
15. W.D. Ni, S.K. Tan, W.J. Ng, and S.D. Brown Localized, adaptive recursive partial least squares regression for dynamic system modeling Ind. Eng. Chem. Res. 51 8 2012 8025 8039
16. P. Kadlec, and B. Gabrys Local learning-based adaptive soft sensor for catalyst activation prediction AIChE J. 57 5 2009 1288 1301
17. S. Khatibisepehr, B. Huang, F.W. Xu, and A. Espejo A Bayesian approach to design of adaptive multi-model inferential soft sensors with application in oil sand industry J. Process Control 22 10 2012 1913 1929
18. S.N. Zhang, F.L. Wang, D.K. He, and R.D. Jia Real-time product quality control for batch processes based on stacked least-squares support vector regression models Comput. Chem. Eng. 36 10 2012 217 226
19. C. Cheng, and M.S. Chiu A new data-based methodology for nonlinear process modeling Chem. Eng. Sci. 59 13 2004 2801 2810
20. K. Chen, J. Ji, H.Q. Wang, and Z.H. Song Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes Chem. Eng. Res. Des. 89 10 2011 2117 2124
21. Y. Liu, Z.L. Gao, P. Li, and H.Q. Wang Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes Ind. Eng. Chem. Res. 51 11 2012 4313 4327
22. Y.Q. Liu, D.P. Huang, and Y. Li Development of interval soft sensors using enhanced just-in-time learning and inductive confidence predictor Ind. Eng. Chem. Res. 51 8 2012 3356 3367
23. P. Kadlec, and B. Gabrys Adaptive on-line prediction soft sensing without historical data Proceedings of the 2010 International Joint Conference on Neural Networks, Barcelona 2010
24. P. Geladi, and R.K. Bruce Partial least-squares regression: a tutorial Anal. Chim. Acta. 185 1986 1 17
25. F. Lindgren, P. Geladi, and S. Wold The kernel algorithm for PLS J. Chemom. 7 1 1993 45 59
26. B.S. Dayal, and J.F. MacGregor Improved PLS algorithms J. Chemom. 11 1 1997 73 85
27. J.L. Liu Development of self-validating soft sensors using fast moving window partial least squares Ind. Eng. Chem. Res. 49 22 2010 11530 11546
28. X.Q. He multivariate statistical analysis 2004 China Renmin University Press Beijing
29. Y. Liu, H.Q. Wang, J. Yu, and P. Li Selective recursive kernel learning for online identification of nonlinear systems with NARX form J. Process Control 20 2 2010 181 194