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D2. 1–Report on Dynamic Data Reconciliation of Large-Scale Processes Report on Dynamic Data Reconciliation of Large-Scale Processes Pitarch Pérez, José Luis Prada Moraga, César de EU-SPIRE Dynamic data reconciliation gross errors Parameter estimation Variables estimation Producción Científica 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. Ingeniería de Sistemas y Automática European Union Horizon 2020 program (grant nº 723575) 2019-01-09T11:36:18Z 2019-01-09T11:36:18Z 2018 info:eu-repo/semantics/report J. L. Pitarch and C. de Prada, 2018. D2. 1-Report on dynamic data reconciliation of large-scale processes. Outcomes of the CoPro Project. http://uvadoc.uva.es/handle/10324/33735 eng https://www.spire2030.eu/copro info:eu-repo/grantAgreement/EC/H2020/723575 Attribution-ShareAlike 4.0 International info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-sa/4.0/ EU-SPIRE application/pdf https://uvadoc.uva.es/bitstream/10324/33735/6/CoPro_D2.1_Report%20on%20dynamic%20data_.pdf.jpg Hispana TEXT http://creativecommons.org/licenses/by-sa/4.0/ UVaDOC. Repositorio Documental de la Universidad de Valladolid http://uvadoc.uva.es/handle/10324/33735