{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T16:35:05Z","timestamp":1773938105328,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007637","name":"Departamento Administrativo de Ciencia, Tecnolog\u00eda e Innovaci\u00f3n (COLCIENCIAS)","doi-asserted-by":"publisher","award":["786"],"award-info":[{"award-number":["786"]}],"id":[{"id":"10.13039\/100007637","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The analysis of data from sensors in structures subjected to extreme conditions such as the ones used in smelting processes is a great decision tool that allows knowing the behavior of the structure under different operational conditions. In this industry, the furnaces and the different elements are fully instrumented, including sensors to measure variables such as temperature, pressure, level, flow, power, electrode positions, among others. From the point of view of engineering and data analytics, this quantity of data presents an opportunity to understand the operation of the system under normal conditions or to explore new ways of operation by using information from models provided by using deep learning approaches. Although some approaches have been developed with application to this industry, it is still an open research area. As a contribution, this paper presents an applied deep learning temperature prediction model for a 75 MW electric arc furnace, which is used for ferronickel production. In general, the methodology proposed considers two steps: first, a data cleaning process to increase the quality of the data, eliminating both redundant information as well as atypical and unusual data, and second, a multivariate time series deep learning model to predict the temperatures in the furnace lining. The developed deep learning model is a sequential one based on GRU (gated recurrent unit) layer plus a dense layer. The GRU + Dense model achieved an average root mean square error (RMSE) of 1.19 \u00b0C in the test set of 16 different thermocouples radially distributed on the furnace.<\/jats:p>","DOI":"10.3390\/s21206894","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:31:26Z","timestamp":1634765486000},"page":"6894","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Temperature Prediction Using Multivariate Time Series Deep Learning in the Lining of an Electric Arc Furnace for Ferronickel Production"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9198-1996","authenticated-orcid":false,"given":"Jersson X.","family":"Leon-Medina","sequence":"first","affiliation":[{"name":"Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d\u2019Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Bes\u00f2s (CDB), Universitat Polit\u00e8cnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain"},{"name":"Departamento de Ingenier\u00eda Mec\u00e1nica y Mecatr\u00f3nica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogot\u00e1 111321, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3444-4203","authenticated-orcid":false,"given":"Jaiber","family":"Camacho","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda El\u00e9ctrica y Electr\u00f3nica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogot\u00e1 111321, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9113-1369","authenticated-orcid":false,"given":"Camilo","family":"Gutierrez-Osorio","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda de Sistemas e Industrial, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogot\u00e1 111321, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2007-0108","authenticated-orcid":false,"given":"Juli\u00e1n Esteban","family":"Salom\u00f3n","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda de Sistemas e Industrial, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogot\u00e1 111321, Colombia"}]},{"given":"Bernardo","family":"Rueda","sequence":"additional","affiliation":[{"name":"South32-Cerro Matoso S.A., Km 22 Highway SO Montelibano, C\u00f3rdoba 234001, Colombia"}]},{"given":"Whilmar","family":"Vargas","sequence":"additional","affiliation":[{"name":"South32-Cerro Matoso S.A., Km 22 Highway SO Montelibano, C\u00f3rdoba 234001, Colombia"}]},{"given":"Jorge","family":"Sofrony","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Mec\u00e1nica y Mecatr\u00f3nica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogot\u00e1 111321, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4226-1324","authenticated-orcid":false,"given":"Felipe","family":"Restrepo-Calle","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda de Sistemas e Industrial, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogot\u00e1 111321, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6687-1429","authenticated-orcid":false,"given":"Cesar","family":"Pedraza","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda de Sistemas e Industrial, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogot\u00e1 111321, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4498-596X","authenticated-orcid":false,"given":"Diego","family":"Tibaduiza","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda El\u00e9ctrica y Electr\u00f3nica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogot\u00e1 111321, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"ref_1","unstructured":"Anaya, M. 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