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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1485

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contributor.authorDamarla, S K-
contributor.authorKundu, M-
date.accessioned2011-07-12T06:16:56Z-
date.available2011-07-12T06:16:56Z-
date.issued2011-06-
identifier.citationResearch Journal of Chemical Sciences, Vol. 1(3) June (2011)en
identifier.issn2231-606X-
identifier.urihttp://hdl.handle.net/2080/1485-
descriptionCopyright for this paper belongs to International Science Congress Associationen
description.abstractPresent study addresses the monitoring of a continuous bioreactor operation. New methodologies; based on clustering time series data and moving window based pattern matching have been proposed for the detection of fault in the chosen bioreactor process. A modified k-means clustering algorithm using similarity measure as a convergence criterion has been adopted for discriminating among time series data pertaining to various operating conditions. The proposed distance and PCA based combined similarity along with the moving window approach were used to discriminate among the normal operating conditions as well as detection of fault for the process taken up.en
format.extent106194 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherInternational Science Congress Associationen
subjectPCAen
subjectmoving windowen
subjectPattern matchingen
subjectbioreactoren
subjectk-means clusteringen
titleMonitoring of Bioreactor using Statistical Techniquesen
typeArticleen
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