Bilinear modeling of batch processes. Part III: Parameter stability

Journal of Chemometrics

Volumn 28, Issue 1, 2014, Pages 10-27

  González Martínez, Jose Maria ^a   Camacho, Jose ^b   Ferrer, Alberto ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

Multivariate statistical process control; Principal component analysis; Stability; Synchronization; Uncertainty; Unfolding

Indexed keywords

BATCH DATA PROCESSING; FAULT DETECTION; HIERARCHICAL SYSTEMS; MULTIVARIANT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; STABILITY; STATISTICAL PROCESS CONTROL; UNCERTAINTY ANALYSIS;

BATCH PROCESS; BILINEAR MODELS; MONITORING SYSTEM; MULTIVARIATE STATISTICAL PROCESS CONTROL; PARAMETER STABILITY; PARAMETERS ESTIMATES; PERFORMANCE; PRINCIPAL-COMPONENT ANALYSIS; UNCERTAINTY; UNFOLDINGS;

SYNCHRONIZATION;

EID: 84892674126     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.2562     Document Type: Article

References (39)

1. Kourti T. Process analysis and abnormal situation detection: from theory to practice. IEEE Control Syst. Mag. 2002; 22: 10-25.
2. Ündey C, Çinar A. Statistical monitoring of multistage, multiphase batch processes. IEEE Control Syst. Mag. 2002; 22: 40-52.
3. Kourti T. Application of latent variable methods to process control and multivariate statistical process control in industry. Int. J. Adapt. Control Signal Process. 2005; 19: 213-246.
4. Wold S, Kettaneh-Wold N, MacGregor J, Dunn K. Comprehensive Chemometrics, vol.2. Elsevier: Amsterdam, 2009, 163-195.
5. Jackson J. A User's Guide to Principal Components. Wiley-Interscience: England, 2003.
6. Camacho J, Picó J, Ferrer A. Bilinear modelling of batch processes. Part I: theoretical discussion. J. Chemom. 2008; 22: 299-308.
7. Camacho J, Picó J, Ferrer A. Bilinear modelling of batch processes. Part II: a comparison of PLS soft-sensors. J. Chemom. 2008; 22: 533-547.
8. González-Martínez J, Vitale R, de Noord O, Ferrer A. Does synchronization matter in bilinear batch process monitoring? submitted.
9. Eriksson L, Johansson E, Kettaneh-Wold N, Trygg J, Wikström C, Wold S. Multi- and Megavariate Data Analysis Part I: Basic Principles and Applications. Umetrics Inc: Ume, Sweden, 2006.
10. Umetrics AB. SIMCA Program, Version 13.0.3 for Windows, Graphical Software for Multivariate Process Modeling: Umea, Sweden, 2013. www.umetrics.com [3 April 2013].
11. Garcia-Muñoz S, Kourti T, MacGregor J. Troubleshooting of an industrial batch process using multivariate methods. Ind. Eng. Chem. Res. 2003; 42: 3592-3601.
12. Zarzo M, Ferrer A. Batch process diagnosis: PLS with variable selection versus block-wise PCR. Chemom. Intell. Lab. Syst. 2004; 73: 15-27.
13. Wallace D. Prosensus multivariate v12. 02, 2010.
14. Louwerse DJ, Tates AA, Smilde AK, Koot GLM, Berndt H. PLS discriminant analysis with contribution plots to determine differences between parallel batch reactors in the process industry. Chemom. Intell. Lab. Syst. 1999; 46: 197-206.
15. Nomikos P, MacGregor J. Monitoring batch processes using multiway principal components. AlChE J. 1994; 40: 1361-1375.
16. Kaitsha N, Moore CF. Extraction of event times in batch profiles for time synchronization and quality predictions. Ind. Eng. Chem. Res. 2001; 40: 252-260.
17. Ramsay J, Silverman B. Functional Data Analysis. Springer-Verlag: New York, 1997.
18. Andersen SW, Runger GC. Automated feature extraction from profiles with application to a batch fermentation process. J. Roy. Stat. Soc. C-App. 2012; 61: 327-344.
19. Kassidas A, MacGregor J, Taylor P. Synchronization of batch trajectories using dynamic time warping. AlChE J. 1998; 44: 864-875.
20. González-Martínez J, Ferrer A, Westerhuis J. Real-time synchronization of batch trajectories for on-line multivariate statistical process control using dynamic time warping. Chemom. Intell. Lab. Syst. 2011; 105: 195-206.
21. Zhang Y, Edgar TF. A robust dynamic time warping algorithm for batch trajectory synchronization: Seattle, Washington, USA, 2008, Proceedings of American Control Conference, 2864-2860.
22. Gins G, Vanden Kerkhof P, VanImpe JFM. Hybrid derivative dynamic time warping for online industrial batch-end quality estimation. Ind. Eng. Chem. Res. 2012; 51: 6071-6084.
23. Gurden S, Westerhuis J, Bijlsma S, Smilde A. Modelling of spectroscopy batch process data using grey models to incorporate external information. J. Chemom. 2001; 15: 101-121.
24. Kourti T. Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. J. Chemom. 2003; 17: 93-109.
25. Westerhuis J, Kourti T, MacGregor J. Comparing alternative approaches for multivariate statistical analysis of batch process data. J. Chemom. 1999; 13: 397-413.
26. Nomikos P, MacGregor J. Multivariate SPC charts for monitoring batch processes. Technometrics 1995; 37: 41-59.
27. Wold S, Kettaneh N, Friden H, Holmberg A. Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemom. Intell. Lab. Syst. 1998; 44: 331-340.
28. Chen J, Liu K. On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chem. Eng. Sci. 2002; 57: 63-75.
29. Ramaker H, van Sprang E, Westerhuis J, Smilde A. Fault detection properties of global, local and time evolving models for batch process monitoring. J. Process Control 2005; 15: 799-805.
30. Lennox B, Montague G, Hiden H, Kornfeld G, Goulding P. Process monitoring of an industrial fed-batch fermentation. Biotechnol. Bioeng. 2001; 74(2): 125-135.
31. Ündey C, Ertunç S, Çinar A. Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis. Ind. Eng. Chem. Res. 2003; 42: 4645-4658.
32. Camacho J, Picó J. Multi-phase principal component analysis for batch processes modelling. Chemom. Intell. Lab. Syst. 2006; 81: 127-136.
33. Rännar S, MacGregor J, Wold S. Adaptive batch monitoring using hierarchical PCA. Chemom. Intell. Lab. Syst. 1998; 41: 73-81.
34. Camacho J, Picó J, Ferrer A. On-line monitoring of batch processes based on pca: does the modelling structure matter? Anal. Chim. Acta 2009; 642: 59-69.
35. van Sprang E, Ramaker H, Westerhuis J, Gurden S, Smilde A. Critical evaluation of approaches for on-line batch process monitoring. Chem. Eng. Sci. 2002; 57: 3979-3991.
36. Lei F, Rotbøll M, Jørgensen S. A biochemically structured model for Saccharomyces cerevisiae. Asian J. Biotechnol. 2001; 88: 205-221.
37. Camacho J, González-Martínez J, Ferrer A. Multi-phase (MP) toolbox, 2013. Available from: http://mseg.webs.upv.es/Software.html.
38. Jolliffe I T. Principal Component Analysis. Springer-Verlag: NY, USA, 2002.
39. Camacho J, Picó J, Ferrer A. Multi-phase analysis framework for handling batch process data. J. Chemom. 2008; 22: 632.