Soft measurement model and its application in raw meal calcination process

Journal of Process Control

Volumn 22, Issue 1, 2012, Pages 344-351

  Qiao, Jinghui ^a,b   Chai, Tianyou ^a,b  

^a STATE KEY LABORATORY OF SYNTHETICAL AUTOMATION FOR PROCESS INDUSTRIES   (China)

^b NORTHEASTERN UNIVERSITY   (China)

Author keywords

Least squares support vector machines; Raw meal calcination process; Recursive fixed memory principal component analysis; Soft measurement model

Indexed keywords

CALCINERS; DECOMPOSITION PROCESS; LEAST SQUARES SUPPORT VECTOR MACHINES; MISSING DATA; PRINCIPAL COMPONENTS; RAW MEAL; SOFT-MEASUREMENT MODEL; TEMPERATURE SETTING; TRAINING SPEED;

CALCINATION; CEMENT PLANTS; INDUSTRIAL APPLICATIONS; STATISTICS; SUPPORT VECTOR MACHINES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84655167149     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2011.08.005     Document Type: Conference Paper

References (32)

1. Y. Yang, and T.Y. Chai Soft sensing based on artificial neural network Proceedings of the 1997 American Control Conference, vol. 1 1997
2. P. Angelov, and R. Buswell Identification of evolving fuzzy rule-based models IEEE Transaction on Fuzzy Systems 10 2002 667
3. T. Kourti Process analysis and abnormal situation detection: from theory to practice Control Systems Magazine 22 2002 10 25 (Pubitemid 35298306)
4. P. Kadlec, B. Gabrys, and S. Strandt Data-driven soft sensors in the process industry Computers and Chemical Engineering 33 2009 795 814
5. S. Borguet, and O. Léonard Coupling principal component analysis and Kalman filtering algorithms for on-line aircraft engine diagnostics Control Engineering Practice 17 2009 494 502
6. S. Narasimhan, and S.L. Shah Model identification and error covariance matrix estimation from noisy data using PCA Control Engineering Practice 16 2008 146 155 (Pubitemid 350029028)
7. C. Zhao, F. Wang, N. Lu, and M. Jia Stage-based soft-transition multiple pca modeling and on-line monitoring strategy for batch processes Journal of Process Control 17 2007 728 741 (Pubitemid 47539886)
8. S.W. Choi, J.H. Park, and I.B. Lee Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis Computers and Chemical Engineering 28 2004 1377 1387 (Pubitemid 38609089)
9. X. Wang, U. Kruger, and B. Lennox Recursive partial least squares algorithms for monitoring complex industrial processes Control Engineering Practice 11 2003 613 632 (Pubitemid 36777464)
10. J. Zhang, S.J. Zhao, and Y.M. Xu Performance monitoring of processes with multiple operating modes through multiple PLS models Journal of Process Control 16 2006 763 772
11. C.-C. Hsu, M.-C. Chen, and L.-S. Chen A novel process monitoring approach with dynamic independent component analysis Control Engineering Practice 18 2010 242 253
12. J.M. Lee, C.K. Yoo, and I.B. Lee Statistical process monitoring with independent component analysis Journal of Process Control 14 2004 467 485
13. I.T. Jolliffe Principal Component Analysis 2002 Springer
14. J.A.K. Suykens, and J. Vandewalle Least squares support vector machine classifiers Neural Processing Letters 9 3 1999 293 300
15. V.N. Vapnik The Nature of Statistical Learning Theory 1998 Wiley New York pp. 401-454
16. B.Y. Liu, and R.G. Yang A novel method based on PCA and LS-SVM for power load forecasting Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies 2008 759 763
17. T.F. Ming, G. He, and H. Wang PCA based characteristic parameter extraction and failure recognition using LS-SVM International Conference on Computational Intelligence and Software Engineering 2009 1 4
18. X.H. Wang Soft sensor modeling method for freezing point of diesel fuel based on PCA and LS-SVM 7th World Congress on Intelligent Control and Automation 2008 9157 9161
19. M. Afshin, and A. Sadeghian PCA-based least squares support vector machines in week-ahead load forecasting Industrial & Commercial Power Systems Technical Conference 2007 1 6
20. J.H. Xie KPCA based on LS-SVM for face recognition Second International Symposium on Intelligent Information Technology Application 2008 638 641
21. G.A. Cherry, and S.J. Qin Monitoring non-normal data with principal component analysis and adaptive density estimation Proceeding of the 46th IEEE Conference on Decision and Control 2007 352 359 (Pubitemid 351277178)
22. W. Li, H. Yue, S. Valle, and S.J. Qin Recursive PCA for adaptive process monitoring Journal of Process Control 10 2000 471 486
23. B. Lin, B. Reck, J.K.H. Hnudsen, and S.B. JØrgensen A systematic approach for soft sensor development Computers and Chemical Engineering 31 2007 419 425 (Pubitemid 46282062)
24. R. Ratcliff Methods for dealing with reaction time outliers Psychological Bulletin 114 3 1993 510 532
25. P.J. Huber Robust Statistics 1981 Wiley New York
26. A.G. Akritas, and G.I. Malaschonok Applications of singular-value decomposition (SVD) Mathematics and Computers in Simulation 67 2004 15 31
27. J.K. Baksalary, and O.M. Baksalary Relationships between generalized inverses of a matrix and generalized inverses of its rank-one-modifications Linear Algebra and its Applications 388 2004 31 44
28. I. Hnětynkov, and Z. Strakoš Lanczos tridiagonalization and core problems Linear Algebra and its Applications 421 2007 243 251 (Pubitemid 46096982)
29. V. Zubko, Y.J. Kaufman, R.I. Burg, and J.V. Martins Principal component analysis of remote sensing of aerosols over oceans IEEE Transaction on Geoscience and Remote Sensing 45 2007 730 745 (Pubitemid 46375745)
30. R. Dunia, and J. Qin A subspace approach to multidimensional fault identification and reconstruction AIChE Journal 44 1998 1813 1831 (Pubitemid 28540264)
31. V.N. Vapnik Statistical Learning Theory 1998 Wiley New York
32. N. Cristianini, and C. Campbell Dynamically adapting kernels in support vector machines Processing Systems 11 1999 204 210