Aprendizaje profundo para la predicción de temperatura en las paredes refractarias de un horno de arco eléctrico

dc.contributorTibaduiza Burgos, Diego Alexander
dc.contributorLeon-Medina, Jersson Xavier
dc.contributorGrupo de Investigación en Electrónica de Alta Frecuencia y Telecomunicaciones (Cmun)
dc.contributorDiego F. Godoy-Rojas [0000-0002-1639-7992]
dc.creatorGodoy Rojas, Diego Fernando
dc.date.accessioned2023-06-02T14:26:53Z
dc.date.accessioned2023-06-07T00:06:38Z
dc.date.available2023-06-02T14:26:53Z
dc.date.available2023-06-07T00:06:38Z
dc.date.created2023-06-02T14:26:53Z
dc.date.issued2022
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/83956
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6651736
dc.description.abstractEn el presente documento se detalla el flujo de trabajo llevado a cabo para el desarrollo de modelos de aprendizaje profundo para la estimación de temperatura de pared media en dos hornos de arco eléctrico pertenecientes a la empresa Cerro Matoso S.A. El documento inicia con una introducción al contexto bajo el cual se desarrolló el trabajo final de maestría, dando paso a la descripción teórica de todos los aspectos relevantes y generalidades sobre el funcionamiento de la planta, las series de tiempo y el aprendizaje profundo requeridas durante el desarrollo del proyecto. El flujo de trabajo se divide en una metodología de 3 pasos, empezando por el estudio y preparación del conjunto de datos brindado por CMSA, seguido por el desarrollo, entrenamiento y selección de diversos modelos de aprendizaje profundo usados en predicciones con datos de un conjunto de prueba obteniendo errores RMSE entre 1-2 °C y finalizando con una etapa de validación que estudia el desempeño de los diversos modelos obtenidos frente a diversas variaciones en las condiciones de los parámetros de entrenamiento. (Texto tomado de la fuente)
dc.description.abstractThis document details the workflow followed for the development of deep learning models for the estimation of mean wall temperature in two electric arc furnaces belonging to the company Cerro Matoso S.A. The document begins by establishing the development context of the final master's degree project. Afterwards, the theoretical description of all the relevant aspects and generalities about the operation of the plant, the time series and the deep learning required during the development of the project is given. The workflow is divided into a 3-step methodology starting with the study and preparation of the data set provided by CMSA, followed by the development, training and selection of various deep learning models used in predictions with data from a test set. obtaining RMSE errors between 1-2 °C and ending with a validation stage that studies the performance of the various models obtained against various variations in the conditions of the training parameters.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherBogotá - Ingeniería - Maestría en Ingeniería - Automatización Industrial
dc.publisherFacultad de Ingeniería
dc.publisherBogotá, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
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dc.relationIT-0003-A28-C3-V1-18.11.2019 - Informe preliminar con análisis estadístico de datos y correlaciones posibles.
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dc.rightsAtribución-NoComercial 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleAprendizaje profundo para la predicción de temperatura en las paredes refractarias de un horno de arco eléctrico
dc.typeTrabajo de grado - Maestría


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