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Titolo:
Performance monitoring of a multi-product semi-batch process
Autore:
Lane, S; Martin, EB; Kooijmans, R; Morris, AJ;
Indirizzi:
Univ Newcastle, Ctr Proc Analyt & Control Technol, Newcastle Upon Tyne NE17RU, Tyne & Wear, England Univ Newcastle Newcastle Upon Tyne Tyne & Wear England NE1 7RU r, England Unilever Res Labs Vlaardingen, NL-31310 AC Vlaardingen, Netherlands Unilever Res Labs Vlaardingen Vlaardingen Netherlands NL-31310 AC BCands
Titolo Testata:
JOURNAL OF PROCESS CONTROL
fascicolo: 1, volume: 11, anno: 2001,
pagine: 1 - 11
SICI:
0959-1524(200102)11:1<1:PMOAMS>2.0.ZU;2-G
Fonte:
ISI
Lingua:
ENG
Soggetto:
PRINCIPAL COMPONENT ANALYSIS;
Keywords:
multi-group model; process performance monitoring; semi-batch process;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
17
Recensione:
Indirizzi per estratti:
Indirizzo: Martin, EB Univ Newcastle, Ctr Proc Analyt & Control Technol, Newcastle Upon Tyne NE17RU, Tyne & Wear, England Univ Newcastle Newcastle Upon Tyne Tyne & Wear England NE1 7RU
Citazione:
S. Lane et al., "Performance monitoring of a multi-product semi-batch process", J PROC CONT, 11(1), 2001, pp. 1-11

Abstract

Traditionally principal components analysis (PCA) has been viewed as a single-population method. In particular in multivariate statistical process control, PCA has been used to monitor single product production. An extensionto principal components analysis is presented which enables the simultaneous monitoring of a number of product grades or recipes. The method is basedupon the existence of a common eigenvector subspace for the sample variance-covariance matrices of the individual products. The pooled sample variance-covariance matrix of the individual products is then used to estimate theprincipal component loadings of the multi-group model. The methodology is applied to a semi-discrete industrial batch process manufacturing a number of recipes. The industrial application illustrates that the detection and diagnostic capabilities of the multi-group model are comparable to those achieved by developing a separate statistical representation for the individual products. (C) 2000 Elsevier Science Ltd. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 29/03/20 alle ore 12:15:59