Show simple item record

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
dc.conicet.fuente Crossref


Archivos asociados

This item appears in the following Collection(s)

Show simple item record

info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)