dc.creatorLlanos, Claudia Elizabeth
dc.creatorSanchez, Mabel Cristina
dc.creatorMaronna, Ricardo Antonio
dc.date.accessioned2018-04-23T15:32:35Z
dc.date.accessioned2018-11-06T13:18:05Z
dc.date.available2018-04-23T15:32:35Z
dc.date.available2018-11-06T13:18:05Z
dc.date.created2018-04-23T15:32:35Z
dc.date.issued2017-07
dc.identifierLlanos, 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.identifier0888-5885
dc.identifierhttp://hdl.handle.net/11336/43006
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1874057
dc.description.abstractA 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.languageeng
dc.publisherAmerican Chemical Society
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acs.iecr.7b00726
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1021/acs.iecr.7b00726
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectSYSTEMATIC MEASUREMENT ERRORS
dc.subjectDATA RECONCILIATION
dc.subjectROBUST STATISTICS
dc.titleClassification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
dc.typeArtículos de revistas
dc.typeArtículos de revistas
dc.typeArtículos de revistas


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