<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-22T22:08:52Z</responseDate><request verb="GetRecord" identifier="oai:uvadoc.uva.es:10324/33735" metadataPrefix="mods">https://uvadoc.uva.es/oai/request</request><GetRecord><record><header><identifier>oai:uvadoc.uva.es:10324/33735</identifier><datestamp>2021-06-23T11:22:05Z</datestamp><setSpec>com_10324_1168</setSpec><setSpec>com_10324_931</setSpec><setSpec>com_10324_894</setSpec><setSpec>col_10324_1950</setSpec></header><metadata><mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:name>
<mods:namePart>Pitarch Pérez, José Luis</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Prada Moraga, César de</mods:namePart>
</mods:name>
<mods:extension>
<mods:dateAvailable encoding="iso8601">2019-01-09T11:36:18Z</mods:dateAvailable>
</mods:extension>
<mods:extension>
<mods:dateAccessioned encoding="iso8601">2019-01-09T11:36:18Z</mods:dateAccessioned>
</mods:extension>
<mods:originInfo>
<mods:dateIssued encoding="iso8601">2018</mods:dateIssued>
</mods:originInfo>
<mods:identifier type="citation">J. L. Pitarch and C. de Prada, 2018. D2. 1-Report on dynamic data reconciliation of large-scale processes. Outcomes of the CoPro Project.</mods:identifier>
<mods:identifier type="uri">http://uvadoc.uva.es/handle/10324/33735</mods:identifier>
<mods:abstract>Availability of reliable process information in real time is key in any decision-making procedure. Thus, good industrial decision-support implementations require dealing with gross errors and consideration of process transients in order to get a set of measurements which will be coherent with the basic underlying process dynamics. This report presents dynamic data reconciliation methods and tools adapted to the requirements of industrial environments (large-scale systems and noisy/faulty data). Moreover, basic concepts in literature are extended to artificially increase system redundancy as well as to cope with time-varying parameter estimation. The procedure summarized in this report has been tested in the Lenzing case study.</mods:abstract>
<mods:language>
<mods:languageTerm>eng</mods:languageTerm>
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<mods:accessCondition type="useAndReproduction">info:eu-repo/semantics/openAccess</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by-sa/4.0/</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">EU-SPIRE</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">Attribution-ShareAlike 4.0 International</mods:accessCondition>
<mods:titleInfo>
<mods:title>D2. 1–Report on Dynamic Data Reconciliation of Large-Scale Processes</mods:title>
</mods:titleInfo>
<mods:genre>info:eu-repo/semantics/report</mods:genre>
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