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dc.contributor.author
Cedeño Viteri, Marco Vinicio
dc.contributor.author
Rodriguez Aguilar, Leandro Pedro Faustino
dc.contributor.author
Sanchez, Mabel Cristina
dc.date.available
2017-10-23T19:24:16Z
dc.date.issued
2016-08-17
dc.identifier.citation
Cedeño Viteri, Marco Vinicio; Rodriguez Aguilar, Leandro Pedro Faustino; Sanchez, Mabel Cristina; Bioprocess statistical control: Identification stage based on hierarchical clustering; Elsevier; Process Biochemistry; 51; 12; 17-8-2016; 1919-1929
dc.identifier.issn
1359-5113
dc.identifier.uri
http://hdl.handle.net/11336/26934
dc.description.abstract
Bioprocesses are characterized by the fact that small variations in operating conditions may have a substantial impact on the final batch quality. Therefore, the early detection and isolation of faults allow implementing corrective actions before the effects of deviations from the normal operation have a detrimental effect on production. In this work a new strategy for the statistical monitoring of batch processes is presented, and it is applied to monitor the operation of a fermentation process. The methodology works in the original variable space, therefore it only uses the Hotelling statistic for detection purposes. To determine the set of measurements by which the fault is revealed, the nearest in control neighbor to the observation point is calculated, and the distance between these two points is used to evaluate the contribution of each observation to the inflated statistic. In contrast to the existing latent-variable and original-variable based approaches, a simple hierarchical clustering technique allows to identify the set of suspicious measurements, without assuming the probability density function of the variable contributions. Furthermore, the performance of the proposed identification procedure is compared to the one achieved using other monitoring techniques. A well-known fed-batch fermentation benchmark is employed with this purpose, and the comparison is based on the results of a comprehensive set of simulated fault scenarios.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Statistical Control
dc.subject
Bioprocess
dc.subject
Fault Diagnosis
dc.subject
Multivariate Process Control
dc.subject
Hotelling Statistic
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Fault Identification
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Clusters
dc.subject
Fermentation
dc.subject.classification
Otras Ingeniería Química
dc.subject.classification
Ingeniería Química
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Bioprocess statistical control: Identification stage based on hierarchical clustering
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-10-09T15:35:30Z
dc.journal.volume
51
dc.journal.number
12
dc.journal.pagination
1919-1929
dc.journal.pais
Países Bajos
dc.journal.ciudad
Ámsterdam
dc.description.fil
Fil: Cedeño Viteri, Marco Vinicio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
dc.description.fil
Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
dc.description.fil
Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
dc.journal.title
Process Biochemistry
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S135951131630335X
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.procbio.2016.08.020


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