Multivariate statistical process control of batch processes based on three-way models

Chemical Engineering Science

Volumn 55, Issue 7, 2000, Pages 1225-1235

  Louwerse, D J ^a   Smilde, A K ^a  

^a Univ A   (Netherlands)

Author keywords

Batch MSPC; Control charts; PARAFAC; PCA; Process monitoring; Tucker

Indexed keywords

BATCH DATA PROCESSING; MATHEMATICAL MODELS; STATISTICAL PROCESS CONTROL;

MULTIVARIATE STATISTICAL PROCESS CONTROL (MSPC); PRINCIPAL COMPONENT ANALYSIS (PCA);

CHEMICAL OPERATIONS;

CONTROL MEASURE; CONTROL METHOD; PROCESS CONTROL; STATISTICAL ANALYSIS;

EID: 0034015053     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0009-2509(99)00408-X     Document Type: Article

References (22)

1. Boqué R., Smilde A.K. Monitoring and diagnosing batch processes with multiway regression models. AIChE Journal. 45(7):1999;1504-1520.
2. Broadhead T.O., Hamielec A.E., MacGregor J.F. Dynamic modeling of the batch, semi-batch and continuous production of styrene-butadiene copolymers by emulsion polymerisation. Makromolekulare Chemie Supplement. 10:1985;105-128.
3. Carroll J.D., Chang J.J. Analysis of individual differences in multidimensional scaling via a n-way generalization of "Eckart-Young" decomposition. Psychometrika. 35:1970;283-319.
4. Dong D., McAvoy T.J. Batch tracking via nonlinear principal component analysis. AIChE Journal. 42:1996;2199-2208.
5. Harshman R.A. Foundations of the PARAFAC procedure. model and conditions for an explanatory multi-mode factor analysis UCLA Working Papers in Phonetics. 16:1970;1-84.
6. Jackson J.E., Mudholkar G.S. Control procedures for residuals associated with principal component analysis. Technometrics. 21:1979;341-349.
7. Kiers H.A.L. Hierarchical relations among three-way methods. Psychometrika. 56:1991;449-470.
8. Kourti T., MacGregor J.F. Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemometrics and Intelligent Laboratory Systems. 28:1995;3-21.
9. Kourti T., Nomikos P., MacGregor J.F. Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. Journal Proc. Control. 5:1995;277.
10. Kroonenberg P.M., De Leeuw J. Principal component analysis of three-mode data by means of alternating least squares algorithms. Psychometrika. 45:1980;69-97.
11. Louwerse, D. J., Smilde, A. K., & Kiers, H. A. L. (1998). Cross-validation of multiway component models, Journal of Chemometrics, to appear.
12. Martin E.B., Morris A.J., Papazoglou M.C., Kiparissides C. Batch process monitoring for consistent production. Computers Chemical Engineering. 20:1996;S599-S604.
13. Miller P., Swanson R.E., Heckler C.E. Contribution plots: a missing link in multivariate quality control. Applied Mathematics and Computer Science. 8:1998;775-792.
14. Nomikos P. Detection and diagnosis of abnormal batch operations based on multi-way principal component analysis. ISA Transactions. 35:1996;259-266.
15. Nomikos P., MacGregor J.F. Monitoring batch processes using multiway principal component analysis. AIChE Journal. 40:1994;1361-1375.
16. Nomikos P., MacGregor J.F. Multivariate SPC charts for monitoring batch processes. Technometrics. 37:1995;41-59.
17. Rao, C. R., & Mitra, S. (1971). Generalized inverse of matrices and its applications (pp. 12-13), New York: Wiley.
18. Ränner S., MacGregor J.F., Wold S. Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems. 41:1998;73-81.
19. Seber G.A.F. Multivariate observations. 1984;Wiley, New York.
20. Tracy N.D., Young J.C., Mason R.L. Multivariate control charts for individual observations. Journal of Quality Technology. 24:1992;88-95.
21. Tucker L.R. Some mathematical notes on three-mode factor analysis. Psychometrika. 31:1966;279-311.
22. Wetherill G.B., Brown D.W. Statistical process control, theory and practice. 1991;Chapman & Hall, London.