Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes

Industrial and Engineering Chemistry Research

Volumn 53, Issue 35, 2014, Pages 13736-13749

  Yuan, Xiaofeng ^a   Ge, Zhiqiang ^a   Song, Zhihuan ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

FERMENTATION; FORECASTING; REGRESSION ANALYSIS;

DEBUTANIZER COLUMNS; JUST-IN-TIME LEARNING; KERNEL PRINCIPAL COMPONENT; PRINCIPAL COMPONENT REGRESSION; ROOT-MEAN-SQUARE ERRORS; SENSING PERFORMANCE; SIMULATED FERMENTATION; SOFT-SENSING APPROACH;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84906872234     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie4041252     Document Type: Article

References (41)

1. Ge, Z.; Song, Z.; Gao, F. Review of recent research on data-based process monitoring Ind. Eng. Chem. Res. 2013, 52 (10) 3543-3562
2. Xu, X.; Xiao, F.; Wang, S. Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods Applied Thermal Engineering 2008, 28 (2) 226-237
3. Liu, Y.; Hu, N. P.; Wang, H. Q.; Li, P. Soft chemical analyzer development using adaptive least-squares support vector regression with selective pruning and variable moving window size Ind. Eng. Chem. Res. 2009, 48, 5731-5741
4. Khatibisepehr, S.; Huang, B.; Khare, S. Design of inferential sensors in the process industry: A review of Bayesian methods Journal of Process Control 2013, 23 (10) 1575-1596
5. Wang, S.; Cui, J. Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method Appl. Energy 2005, 82 (3) 197-213
6. Ge, Z. Q.; Gao, F. R.; Song, Z. H. Mixture probabilistic PCR model for soft sensing of multimode processes Chem. Intell. Lab. Syst. 2011, 105, 91-105
7. Yu, J. A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses Comput. Chem. Eng. 2012, 41, 134-144
8. Kim, S.; Okajima, R.; Kano, M.; Hasebe, S. Development of soft-sensor using locally weighted PLS with adaptive similarity measure Chemometrics and Intelligent Laboratory Systems 2013, 124, 43-49
9. Ge, Z. Q.; Chen, T.; Song, Z. H. Quality prediction for polypropylene production process based on CLGPR model Control Engineering Practice 2011, 19 (5) 423-432
10. Undey, C.; Tatara, E.; Cinar, A. Intelligent real-time performance monitoring and quality prediction for batch/fed-batch cultivations J. Biotechnol. 2004, 108 (1) 61-77
11. Facco, P.; Bezzo, F.; Barolo, M. Nearest-Neighbor Method for the Automatic Maintenance of Multivariate Statistical Soft Sensors in Batch Processing Ind. Eng. Chem. Res. 2010, 49 (5) 2336-2347
12. Huixin, T.; Zhizhong, M.; Shu, W.; Kun, L. Application of Genetic Algorithm Combined with BP Neural Network in Soft Sensor of Molten Steel Temperature. The Sixth World Congress on Intelligent Control and Automation, Dalian, June 21-23, 2006; pp 7742-7745.
13. Kano, M.; Nakagawa, Y. Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry Comput. Chem. Eng. 2008, 32 (1-2) 12-24
14. Bosca, S.; Fissore, D. Design and validation of an innovative soft-sensor for pharmaceuticals freeze-drying monitoring Chem. Eng. Sci. 2011, 66 (21) 5127-5136
15. Nakagawa, H.; Tajima, T.; Kano, M.; Kim, S.; Hasebe, S.; Suzuki, T.; Nakagami, H. Evaluation of Infrared-Reflection Absorption Spectroscopy Measurement and Locally Weighted Partial Least-Squares for Rapid Analysis of Residual Drug Substances in Cleaning Processes Anal. Chem. 2012, 84 (8) 3820-3826
16. Yuan, X.; Zhang, H.; Song, Z. A soft-sensor for estimating copper quality by image analysis technology. 2013 10th IEEE International Conference on Control and Automation (ICCA), Hangzou, June 12-14, 2013; pp 991-996.
17. Ge, Z.; Song, Z.; Zhao, L.; Gao, F. Two-level PLS model for quality prediction of multiphase batch processes Chemometrics and Intelligent Laboratory Systems 2014, 130, 29-36
18. Rani, A.; Singh, V.; Gupta, J. Development of soft sensor for neural network based control of distillation column Isa Transactions 2013, 52, 438-449
19. Ge, Z. Q.; Song, Z. H.; Gao, F. R. Nonlinear quality prediction for multiphase batch processes AIChE J. 2012, 58, 1778-1787
20. Hotelling, H. Analysis of a complex of statistical variables into principal components Journal of educational psychology 1933, 24 (6) 417
21. Kadlec, P.; Gabrys, B.; Strandt, S. Data-driven soft sensors in the process industry Comput. Chem. Eng. 2009, 33 (4) 795-814
22. Hotelling, H. The relations of the newer multivariate statistical methods to factor analysis British Journal of Statistical Psycholo gy 1957, 10 (2) 69-79
23. Kendall, M. G. A Course in Multivariate Analysis; Charles Griffin and Company, Ltd.: London, England, 1957.
24. Schölkopf, B.; Smola, A.; Müller, K.-R. Kernel principal component analysis. In Artificial Neural Networks-ICANN'97, Gerstner, W.; Germond, A.; Hasler, M.; Nicoud, J.-D., Eds.; Springer: Berlin Heidelberg, 1997; Vol. 1327, pp 583-588.
25. Rosipal, R.; Girolami, M.; Trejo, L. J.; Cichocki, A. Kernel PCA for feature extraction and de-noising in nonlinear regression Neural Computing & Applications 2001, 10 (3) 231-243
26. Rosipal, R.; Trejo, L. J. Kernel partial least squares regression in reproducing kernel hilbert space Journal of Machine Learning Research 2002, 2, 97-123
27. Dayal, B. S.; MacGregor, J. F. Recursive exponentially weighted PLS and its applications to adaptive control and prediction Journal of Process Control 1997, 7 (3) 169-179
28. Ahmed, F.; Nazir, S.; Yeo, Y. K. Y. A new soft sensor based on recursive partial least squares for online melt index predictions in grade-changing hdpe operations Chemical Product and Process Modeling 2009, 10.2202/1934-2659.1271
29. Wold, S. Exponentially weighted moving principal components analysis and projections to latent structures Chemometrics and Intelligent Laboratory Systems 1994, 23 (1) 149-161
30. Kaneko, H.; Arakawa, M.; Funatsu, K. Applicability domains and accuracy of prediction of soft sensor models AIChE J. 2011, 57 (6) 1506-1513
31. Bittanti, S.; Picci, G. Identification, adaptation, learning: the science of learning models from data; Springer, 1996; Vol. 153.
32. Cybenko, G. Just-in-time learning and estimation Nato asi series for computer and systems sciences 1996, 153, 423-434
33. Cheng, C.; Chiu, M.-S. A new data-based methodology for nonlinear process modeling Chem. Eng. Sci. 2004, 59 (13) 2801-2810
34. Bontempi, G.; Bersini, H.; Birattari, M. The local paradigm for modeling and control: from neuro-fuzzy to lazy learning Fuzzy sets and systems 2001, 121 (1) 59-72
35. Aha, D. W.; Kibler, D.; Albert, M. K. Instance-based learning algorithms Machine learning 1991, 6 (1) 37-66
36. Cleveland, W. S.; Devlin, S. J. Locally weighted regression: an approach to regression analysis by local fitting Journal of the American Statistical Association 1988, 83 (403) 596-610
37. Schaal, S.; Atkeson, C. G.; Vijayakumar, S. Scalable techniques from nonparametric statistics for real time robot learning Applied Intelligence 2002, 17 (1) 49-60
38. Smola, A. J.; Schölkopf, B. Learning with kernels; Citeseer, 1998.
39. Fortuna, L.; Graziani, S.; Xibilia, M. G. Soft sensors for product quality monitoring in debutanizer distillation columns Control Engineering Practice 2005, 13 (4) 499-508
40. Fortuna, L. Soft sensors for monitoring and control of industrial processes; Springer, 2007.
41. Birol, G.; Ündey, C.; Cinar, A. A modular simulation package for fed-batch fermentation: penicillin production Comput. Chem. Eng. 2002, 26 (11) 1553-1565