Development of soft sensor models based on time difference of process variables with accounting for nonlinear relationship

Industrial and Engineering Chemistry Research

Volumn 50, Issue 18, 2011, Pages 10643-10651

  Kaneko, Hiromasa ^a   Funatsu, Kimito ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

EXPLANATORY VARIABLES; GRADUAL CHANGES; INDUSTRIAL POLYMERS; NON-LINEAR REGRESSION METHOD; NON-LINEAR RELATIONSHIPS; NON-LINEARITY; PHYSICAL MODEL; PROCESS VARIABLES; REGRESSION MODEL; SOFT SENSOR MODELS; SOFT SENSORS; TIME-DIFFERENCES;

REGRESSION ANALYSIS; SENSORS;

MODELS;

EID: 80052838846     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie200692m     Document Type: Article

References (22)

1. 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, 12
2. Kadlec, P.; Gabrys, B; Strandt, S. Data-driven Soft Sensors in the Process Industry Comput. Chem. Eng. 2009, 33, 795
3. Wold, S.; Sjöström, M.; Eriksson, L. PLS-regression: a Basic Tool of Chemometrics Chemom. Intell. Lab. Syst. 2001, 58, 109
4. Lin, B.; Recke, B.; Knudsen, J. K. H.; Jorgensen, S. B. A Systematic Approach for Soft Sensor Development Comput. Chem. Eng. 2007, 31, 419
5. Qin, S. J. Recursive PLS Algorithms for Adaptive Data Modelling Comput. Chem. Eng. 1998, 22, 503
6. Cheng, C.; Chiu, M. S. A New Data-based Methodology for Nonlinear Process Modeling Chem. Eng. Sci. 2004, 59, 2801
7. Kaneko, H.; Funatsu, K. Maintenance-Free Soft Sensor Models with Time Difference of Process Variables Chemom. Intell. Lab. Syst. 2011, 107, 312
8. Kaneko, H.; Arakawa, M.; Funatsu, K. Development of a New Soft Sensor Method Using Independent Component Analysis and Partial Least Squares AIChE J. 2009, 55, 87
9. Kaneko, H.; Arakawa, M.; Funatsu, K. Applicability Domains and Accuracy of Prediction of Soft Sensor Models AIChE J. 2011, 57, 1506
10. Ookita, K. Operation and Quality Control for Chemical Plants by Soft Sensors CICSJ Bull. 2006, 24, 31 (in Japanese)
11. Dai, X. Z.; Wang, W. C.; Ding, Y. H.; Sun, Z. Y. Assumed Inherent Sensor Inversion Based ANN Dynamic Soft-sensing Method and its Application in Erythromycin Fermentation Process Comput. Chem. Eng. 2006, 30, 1203
12. Qin, S. J.; Yue, H. Y.; Dunia, R. Self-validating Inferential Sensors with Application to Air Emission Monitoring Ind. Eng. Chem. Res. 1997, 36, 1675
13. Kadlec, P.; Gabrys, B. Architecture for Development of Adaptive On-line Prediction Models Memetic Comput. 2009, 1, 241
14. McAuley, K. B.; MacGregor, F. J. On-line Inference of Polymer Properties in an Industrial Polyethylene Reactor AIChE J. 1991, 37, 825-835 (Pubitemid 21700341)
15. Vapnik, V. N. The Nature of Statistical Learning Theory; Springer: Berlin, 1995.
16. Yan, W. W.; Shao, H. H.; Wang, X. F. Soft Sensing Modeling Based on Support Vector Machine and Bayesian Model Selection Comput. Chem. Eng. 2004, 28, 1489
17. Whitley, D. A. Genetic Algorithm Tutorial Stat. Comput. 1994, 4, 65
18. Kaneko, H.; Arakawa, M.; Funatsu, K. Development of a New Regression Analysis Method Using Independent Component Analysis J. Chem. Inf. Model. 2008, 48, 534
19. Kim, M.; Lee, Y. H.; Han, I. S.; Han, C. Clustering-based Hybrid Soft Sensor for an Industrial Polypropylene Process with Grade Changeover Operation Ind. Eng. Chem. Res. 2005, 44, 334-342 (Pubitemid 40096205)
20. Lee, D. E.; Song, J. H.; Song, S. O.; Yoon, E. S. Weighted Support Vector Machine for Quality Estimation in the Polymerization Process Ind. Eng. Chem. Res. 2005, 44, 2101
21. Vijayakumar, S.; D'Souza, A.; Schaal, S. Incremental Online Learning in High Dimensions Neural Comput. 2005, 17, 2602
22. Kaneko, H.; Arakawa, M.; Funatsu, K. Novel Soft Sensor Method for Detecting Completion of Transition in Industrial Polymer Processes Comput. Chem. Eng. 2011, 35, 1135