Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDamarla, S K-
dc.contributor.authorKundu, M-
dc.identifier.citationResearch Journal of Chemical Sciences, Vol. 1(3) June (2011)en
dc.descriptionCopyright for this paper belongs to International Science Congress Associationen
dc.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
dc.format.extent106194 bytes-
dc.publisherInternational Science Congress Associationen
dc.subjectmoving windowen
dc.subjectPattern matchingen
dc.subjectk-means clusteringen
dc.titleMonitoring of Bioreactor using Statistical Techniquesen
Appears in Collections:Journal Articles

Files in This Item:
File Description SizeFormat 
Monitoring of Bioreactor using Statistical Techniques.pdf103.71 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.