Bilinear modelling of batch processes. Part I: Theoretical discussion

Journal of Chemometrics

Volumn 22, Issue 5, 2008, Pages 299-308

  Camacho, José ^a   Picó, Jesús ^a   Ferrer, Alberto ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

Batch processes; Partial least squares; Principal component analysis; Unfolding methods

Indexed keywords

BATCH DATA PROCESSING; LEAST SQUARES APPROXIMATIONS;

BATCH PROCESS; BILINEAR MODELS; MODELING APPROACH; ONLINE MONITORING; PARTIAL LEAST SQUARES MODELS; PARTIAL LEAST-SQUARES; PRINCIPAL-COMPONENT ANALYSIS; PROCESS DATA; UNFOLDING METHOD; UNFOLDINGS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 53649083927     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1113     Document Type: Article

References (35)

1. Nomikos P, MacGregor JF. Multivariate SPC charts for monitoring batch processes. Technometrics 1995; 37: 41-59.
2. Kassidas A, MacGregor JF, Taylor PA. Synchronization of batch trajectories using dynamic time warping. AiChE J. 1998; 44: 864-875.
3. Wold S, Kettanch N, Friden H, Holmberg A. Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics Intell. Lab. Syst. 1998; 44: 331-340.
4. Wise BM, Gallagher NB, Martin EB. Application of PARAFAC2 to fault detection and diagnosis in semiconductor etch. J. Chemometrics 2001 ; 15: 285-296.
5. Lu N, Gao F, Yang Y, Wang F. PCA-based modelling and on-line monitoring strategy for uneven-length batch processes. Ind. Eng. Chem. Res. 2004; 43: 3343-3352.
6. Fransson M, Folestad S. Real-time alignment of batch process data using COW for on-line process monitoring. Chemometrics Intell. Lab. Syst. 2006; 84: 56-61.
7. Tucker IR. The extension of factor analysis to three-dimensional matrices. In Contributions to Mathematical Psychology, Frederiksen N, Gulliksen H (eds). Holt, Rinehart and Winston: New York, 1964; 110-162.
8. Bro R. Multiway calibration. multi-linear PLS.J. Chemometrics 1996; 10: 47-61.
9. Bro R. PARAFAC. Tutorial and applications. Chemometrics Intell. Lab. Syst. 1997; 38: 149-171.
10. Smilde AK, Bro R, Geladi P. Multi-Way Analysis, Application in the Chemical Sciences. John Wiley & Sons: England, 2003.
11. Nomikos P, MacGregor JF. Monitoring batch processes using multiway principal components analysis. AiChE J. 1994; 40:1361-1375.
12. Wold S, Geladi P, Esbensen K, Ohman J. Multi-way principal components and PLS analysis. J. Chemometrics 1987; 1:41-56.
13. Kourti T. Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. J. Chemometrics 2003; 17: 93-109.
14. Nelson PRC, Taylor PA, MacGregor JF. Missing data methods in PCA ans PLS: score calculations with incomplete observations. Chemometrics Intell. Lab. Syst. 1996; 35: 45-65.
15. Arteaga F, Ferrer A. Dealing with missing data in MSPC: several methods, different interpretations, some examples. J. Chemometrics 2002; 16: 408-418.
16. 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.
17. Chen J, Liu K. On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chem. Eng. Sci. 2002; 57: 63-75.
18. Camacho J, Picó J. Online monitoring of batch processes using multiphase principal component analysis. J. Process Control 2006; 10: 1021-1035.
19. Rännar S, MacGregor JF, Wold S. Adaptive batch monitoring using hierarchical PCA. Chemometrics Intell. Lab. Syst. 1998; 41: 73-81.
20. Ramaker H, Sprang ENM, Westerhuis JA, Smilde AK. Fault detection properties of global, local and time evolving models for batch process monitoring. J. Process Control 2005; 15: 799-805.
21. Lennox B, Montague GA, Hiden HG, Kornfeld G, Goulding PR. Process monitoring of an industrial fed-batch fermentation. Biotechnol. Bioeng. 2001; 74: 125-135.
22. Kosanovich KA, Dahl KS, Piovoso MJ. Improved process understanding using multiway principal component analysis. Eng. Chem. Res. 1996; 35: 138-146.
23. Ündey C, Çinar A. Statistical monitoring of multistage, multiphase batch processes. IEEE Control Syst. Mag. 2002; 22: 40-52.
24. Lu N, Gao F, Wang F. Sub-PCA modeling and on-line monitoring strategy for batch processes. AIChE J. 2004; 50: 255-259.
25. Camacho J, Pico J. Multi-phase principal component analysis for batch processes modelling. Chemometrics Intell. Lab. Syst. 2006; 81: 127-136.
26. Aguado D, Ferrer A, Ferrer J, Seco A. Multivariate SPC of a sequencing batch reactor for wastewater treatment. Chemometrics Intell. Lab. Syst. 2007; 85: 82-93.
27. Dayal BS, MacGregor JF. Recursive exponentially weightes PLS and its applications to adaptive control and prediction. J. Process Control 1997; 7: 169-179.
28. Qin SJ. Recursive PLS algorithms for adaptive data modeling. Comput. Chem. Eng. 1998; 22: 503-514.
29. Tipping ME, Bishop ChM. Mixtures of probabilistic principal component analysers. Neural Comput., MIT Press. 1999; 11: 443-482.
30. Zhao C, Wang F, Lu N, Jia M. Stage-based soft-transition multiple PCA modeling. J. Process Control 2007, DOI:10.1016/j.jprocont.2007.02.005
31. Camacho J, Picó J. Monitorización de procesos por lotes mediante PCA multifase. Rev. Iberoam. Automat. Inf. Ind. 2006; 3: 78-91.
32. Camacho J, Picó J, Ferrer A. Multi-phase analysis framework for handling batch process data. Submitted to J. Chemometrics 2007.
33. Kourti T. Process analysis and abnormal situation detection: from theory to practice. IEEE Control Syst. Mag. 2002; 22: 10-25.
34. Lei F, Rotbøll M, Jørgensen SB. A biochemically structured model for Saccharomyces cerevisiae. J. Biotechnol. 2001; 88: 205-221.
35. Camacho J, Picó J, Ferrer A. Bilinear modelling of batch processes. Part II: PLS comparative. Submitted to J. Chemometrics 2007.