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dc.contributor.author
Llanos, Claudia Elizabeth
dc.contributor.author
Sanchez, Mabel Cristina
dc.contributor.author
Maronna, Ricardo Antonio
dc.date.available
2018-04-23T15:32:35Z
dc.date.issued
2017-07
dc.identifier.citation
Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation; American Chemical Society; Industrial & Engineering Chemical Research; 56; 34; 7-2017; 9617-9628
dc.identifier.issn
0888-5885
dc.identifier.uri
http://hdl.handle.net/11336/43006
dc.description.abstract
A robust data reconciliation strategy provides unbiased variable estimates in the presence of a moderate quantity of atypical measurements. However, estimates get worse if systematic measurement errors that persist in time (e.g., biases and drifts) are undetected and the breakdown point of the robust strategy is surpassed. The detection and classification of those errors allow taking corrective actions on the inputs of the robust data reconciliation that preserve the instrumentation system redundancy while the faulty sensor is repaired. In this work, a new methodology for variable estimation and systematic error classification, which is based on the concepts of robust statistics, is presented. It has been devised to be part of the real-time optimization loop of an industrial plant; therefore, it runs for processes operating under steady-state conditions. The robust measurement test is proposed in this article and used to detect the presence of sporadic and continuous systematic errors. Also, the robust linear regression of the data contained in a moving window is applied to classify the continuous errors as biases or drifts. Results highlight the performance of the proposed methodology to detect and classify outliers, biases, and drifts for linear and nonlinear benchmarks.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Chemical Society
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Systematic Measurement Errors
dc.subject
Data Reconciliation
dc.subject
Robust Statistics
dc.subject.classification
Otras Ingeniería Química
dc.subject.classification
Ingeniería Química
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-04-18T15:11:02Z
dc.journal.volume
56
dc.journal.number
34
dc.journal.pagination
9617-9628
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Washington
dc.description.fil
Fil: Llanos, Claudia Elizabeth. 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.description.fil
Fil: Maronna, Ricardo Antonio. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; Argentina
dc.journal.title
Industrial & Engineering Chemical Research
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acs.iecr.7b00726
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1021/acs.iecr.7b00726


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