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Titolo:
MONITORING BATCH PROCESSES USING MULTIWAY PRINCIPAL COMPONENT ANALYSIS
Autore:
NOMIKOS P; MACGREGOR JF;
Indirizzi:
MCMASTER UNIV,DEPT CHEM ENGN HAMILTON L8S 4L7 ONTARIO CANADA
Titolo Testata:
AIChE journal
fascicolo: 8, volume: 40, anno: 1994,
pagine: 1361 - 1375
SICI:
0001-1541(1994)40:8<1361:MBPUMP>2.0.ZU;2-9
Fonte:
ISI
Lingua:
ENG
Soggetto:
KNOWLEDGE-BASED REDUNDANCY; PROCESS FAULT-DIAGNOSIS; PARAMETER-ESTIMATION; DYNAMIC-SYSTEMS; NEURAL NETWORK; REACTORS; STATE; DESIGN; FRAMEWORK; QUALITY;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
63
Recensione:
Indirizzi per estratti:
Citazione:
P. Nomikos e J.F. Macgregor, "MONITORING BATCH PROCESSES USING MULTIWAY PRINCIPAL COMPONENT ANALYSIS", AIChE journal, 40(8), 1994, pp. 1361-1375

Abstract

Multivariate statistical Procedures for monitoring the progress of batch Processes are developed. The only information needed to exploit the procedures is a historical database of past successful batches. Multiway PrinciPal component analysis is used to extract the information in the multivariate trajectory data by projecting them onto low-dimensional spaces defined by the latent variables or principal components. This leads to simple monitoring charts, consistent with the philosophy of statistical process control, which are capable of tracking the progress of new batch runs and detecting the occurrence of observable upsets. The approach is contrasted with other approaches which use theoretical or knowledge-based models, and its potential is illustrated usinga detailed simulation study of a semibatch reactor for the productionof styrene-butadiene latex.

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Documento generato il 02/12/20 alle ore 13:53:05