Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0
El consumo de energía es un tema de interés creciente a nivel mundial. La cantidad de emisión de gases tipo invernadero que genera y la disminución significativa de los recursos renovables que se utilizan como materias primas en los sistemas de manufactura han motivado programas a nivel mundial para...
- Autores:
-
Gutiérrez Marroquín, William
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2023
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- spa
- OAI Identifier:
- oai:red.uao.edu.co:10614/16269
- Acceso en línea:
- https://hdl.handle.net/10614/16269
https://red.uao.edu.co/
- Palabra clave:
- Doctorado en Ingeniería
Analítica de datos
Consumo energético
Inteligencia artificial
Modelo de información
Pyme
OPC UA
Transformación digital
Data analytics
Energy consumption
Artificial intelligence
Information model
SMEs
OPC UA
Digital transformation
- Rights
- openAccess
- License
- Derechos reservados - Universidad Autónoma de Occidente, 2023
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Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0 |
| title |
Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0 |
| spellingShingle |
Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0 Doctorado en Ingeniería Analítica de datos Consumo energético Inteligencia artificial Modelo de información Pyme OPC UA Transformación digital Data analytics Energy consumption Artificial intelligence Information model SMEs OPC UA Digital transformation |
| title_short |
Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0 |
| title_full |
Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0 |
| title_fullStr |
Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0 |
| title_full_unstemmed |
Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0 |
| title_sort |
Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0 |
| dc.creator.fl_str_mv |
Gutiérrez Marroquín, William |
| dc.contributor.advisor.none.fl_str_mv |
López Sotelo, Jesús Alfonso Ortíz, Jesús Hamilton |
| dc.contributor.author.none.fl_str_mv |
Gutiérrez Marroquín, William |
| dc.contributor.corporatename.spa.fl_str_mv |
Universidad Autónoma de Occidente |
| dc.contributor.jury.none.fl_str_mv |
García Melo, José Isidro Rivera Abarca, Marco Esteban |
| dc.subject.proposal.spa.fl_str_mv |
Doctorado en Ingeniería Analítica de datos Consumo energético Inteligencia artificial Modelo de información Pyme OPC UA Transformación digital |
| topic |
Doctorado en Ingeniería Analítica de datos Consumo energético Inteligencia artificial Modelo de información Pyme OPC UA Transformación digital Data analytics Energy consumption Artificial intelligence Information model SMEs OPC UA Digital transformation |
| dc.subject.proposal.eng.fl_str_mv |
Data analytics Energy consumption Artificial intelligence Information model SMEs OPC UA Digital transformation |
| description |
El consumo de energía es un tema de interés creciente a nivel mundial. La cantidad de emisión de gases tipo invernadero que genera y la disminución significativa de los recursos renovables que se utilizan como materias primas en los sistemas de manufactura han motivado programas a nivel mundial para mitigar el impacto en el uso de la energía. Los principales consumidores de energía son las fábricas, por ello, se demanda la generación de estrategias para la medición, el seguimiento y la gestión del consumo de energía. En esta tesis doctoral se implementa una plataforma que permite estimar el consumo energético mediante técnicas de inteligencia artificial, bajo el concepto de Industria 4.0, que permiten la transformación digital de la empresa de manufactura. La metodología de desarrollo de la tesis comprende las cuatro fases del ciclo PHVA: planeación, en la cual se establecen las condiciones necesarias para resolver el problema planteado y el diseño de las estrategias propuestas; implementación, en donde se elaboran todos los componentes de la propuesta de solución teniendo en cuenta las especificaciones de diseño; verificación, en la que se presenta el funcionamiento de la plataforma implementada de conformidad con las especificaciones definidas; y acción, en la cual se desarrollan actividades de validación de la propuesta implementada. La gran contribución de esta tesis es el desarrollo de una plataforma de estimación de consumos energéticos que integra un sistema automático para la adquisición de los datos del proceso industrial, en la cual la información de todos y cada uno de los dispositivos que integran la máquina o proceso industrial se adquieren en conexión directa con el equipo de control, utilizando protocolos de comunicación abiertos, aprovechando la tecnología disponible en el sistema de fabricación. El sistema de información se ha construido bajo la arquitectura RAMI, he incluye cada una de las especificaciones técnicas de los equipos que conforman la máquina o proceso industrial y abre el camino hacia su transformación digital, estableciendo así su integración vertical y horizontal, como se propone en la Industria 4.0. La implementación de un prototipo de fábrica de refrescos, en el que se integran los componentes de la plataforma de estimación de consumo energético, permite estructurar una metodología para abordar el proceso de transformación digital en una pequeña y mediana empresa. La plataforma de estimación desarrollada facilita la toma de decisiones a nivel de operación o mantenimiento en el sistema de fabricación implementado, a través de los modelos de estimación de consumo obtenidos mediante técnicas de aprendizaje automático. |
| publishDate |
2023 |
| dc.date.available.none.fl_str_mv |
2023 2025-08-19T19:14:31Z |
| dc.date.issued.none.fl_str_mv |
2023-05-16 |
| dc.date.accessioned.none.fl_str_mv |
2025-08-19T19:14:31Z |
| dc.type.none.fl_str_mv |
Trabajo de grado - Doctorado |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/doctoralThesis |
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http://purl.org/redcol/resource_type/TD |
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info:eu-repo/semantics/publishedVersion |
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Gutiérrez Marroquín, W. (2023). Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0. (Tesis). Universidad Autónoma de Occidente. Cali. Colombia. https://hdl.handle.net/10614/16269 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10614/16269 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Autónoma de Occidente |
| dc.identifier.reponame.spa.fl_str_mv |
Respositorio Educativo Digital UAO |
| dc.identifier.repourl.none.fl_str_mv |
https://red.uao.edu.co/ |
| identifier_str_mv |
Gutiérrez Marroquín, W. (2023). Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0. (Tesis). Universidad Autónoma de Occidente. Cali. Colombia. https://hdl.handle.net/10614/16269 Universidad Autónoma de Occidente Respositorio Educativo Digital UAO |
| url |
https://hdl.handle.net/10614/16269 https://red.uao.edu.co/ |
| dc.language.iso.spa.fl_str_mv |
spa |
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spa |
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López Sotelo, Jesús Alfonsovirtual::6257-1Ortíz, Jesús HamiltonGutiérrez Marroquín, WilliamUniversidad Autónoma de OccidenteGarcía Melo, José IsidroRivera Abarca, Marco Esteban2025-08-19T19:14:31Z20232025-08-19T19:14:31Z2023-05-16Gutiérrez Marroquín, W. (2023). Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0. (Tesis). Universidad Autónoma de Occidente. Cali. Colombia. https://hdl.handle.net/10614/16269https://hdl.handle.net/10614/16269Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/El consumo de energía es un tema de interés creciente a nivel mundial. La cantidad de emisión de gases tipo invernadero que genera y la disminución significativa de los recursos renovables que se utilizan como materias primas en los sistemas de manufactura han motivado programas a nivel mundial para mitigar el impacto en el uso de la energía. Los principales consumidores de energía son las fábricas, por ello, se demanda la generación de estrategias para la medición, el seguimiento y la gestión del consumo de energía. En esta tesis doctoral se implementa una plataforma que permite estimar el consumo energético mediante técnicas de inteligencia artificial, bajo el concepto de Industria 4.0, que permiten la transformación digital de la empresa de manufactura. La metodología de desarrollo de la tesis comprende las cuatro fases del ciclo PHVA: planeación, en la cual se establecen las condiciones necesarias para resolver el problema planteado y el diseño de las estrategias propuestas; implementación, en donde se elaboran todos los componentes de la propuesta de solución teniendo en cuenta las especificaciones de diseño; verificación, en la que se presenta el funcionamiento de la plataforma implementada de conformidad con las especificaciones definidas; y acción, en la cual se desarrollan actividades de validación de la propuesta implementada. La gran contribución de esta tesis es el desarrollo de una plataforma de estimación de consumos energéticos que integra un sistema automático para la adquisición de los datos del proceso industrial, en la cual la información de todos y cada uno de los dispositivos que integran la máquina o proceso industrial se adquieren en conexión directa con el equipo de control, utilizando protocolos de comunicación abiertos, aprovechando la tecnología disponible en el sistema de fabricación. El sistema de información se ha construido bajo la arquitectura RAMI, he incluye cada una de las especificaciones técnicas de los equipos que conforman la máquina o proceso industrial y abre el camino hacia su transformación digital, estableciendo así su integración vertical y horizontal, como se propone en la Industria 4.0. La implementación de un prototipo de fábrica de refrescos, en el que se integran los componentes de la plataforma de estimación de consumo energético, permite estructurar una metodología para abordar el proceso de transformación digital en una pequeña y mediana empresa. La plataforma de estimación desarrollada facilita la toma de decisiones a nivel de operación o mantenimiento en el sistema de fabricación implementado, a través de los modelos de estimación de consumo obtenidos mediante técnicas de aprendizaje automático.Energy consumption is a topic of growing interest worldwide. The amount of greenhouse gas emissions it generates and the significant decreasing in renewable resources, which are used as raw materials in manufacturing systems, have motivated worldwide programs to mitigate the impact on energy use. Main consumers of energy are the factories; therefore, the generation of strategies for the measurement, monitoring and management of energy consumption is required. In this doctoral thesis, it is implemented a platform that allows estimating energy consumption through artificial intelligence techniques, under the Industry 4.0 concept, allowing the digital transformation of the manufacturing companies. The thesis development methodology comprises the four phases of the PDCA cycle: planning, in which the necessary conditions to solve the problem and design the proposed strategies are established; doing (implementation), where the components of the solution proposal are elaborated according to the design specifications; checking, in which the operation of the implemented platform is presented in accordance with the defined specifications; and action, where validation activities of the implemented proposal are developed. Major contribution of this thesis is the development of an energy consumption estimation platform that integrates an automatic system for the acquisition of industrial process data which the information of each and every one of the devices that make up the machine or industrial process are acquired in direct connection with the control equipment by using open communication protocols, taking advantage of the technology available in the manufacturing system. The information system has been built under the RAMI architecture, which includes each of the technical specifications of the equipment that makes up the machine or industrial process and allows opening the way towards its digital transformation, thus establishing its vertical and horizontal integration, the way it is proposed by Industry 4.0. The implementation of a soft drink factory prototype, in which the components of the energy consumption estimation platform are integrated, makes it possible to structure a methodology to address the digital transformation process in a small and medium-sized company. The developed estimation platform facilitates decision-making at the operation or maintenance level in the implemented manufacturing system, through consumption estimation models obtained through automatic learning techniquesTesis (Doctor en Ingeniería)-- Universidad Autónoma de Occidente, 2023DoctoradoDoctor(a) en Ingeniería321 páginasapplication/pdfspaUniversidad Autónoma de OccidenteDoctorado en IngenieríaFacultad de Ingeniería y Ciencias BásicasCaliDerechos reservados - Universidad Autónoma de Occidente, 2023https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto de la industria 4.0Trabajo de grado - Doctoradohttp://purl.org/coar/resource_type/c_db06Textinfo:eu-repo/semantics/doctoralThesishttp://purl.org/redcol/resource_type/TDinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85[1] R. Del Pilar Castrillon and A. M. Quintero, “The energy planning according to the ISO 50001 contribute to the consolidation of a Sustainable Campus to the Universidad Autonoma de Occidente,” in IEEE ICA-ACCA 2018 - IEEE International Conference on Automation/23rd Congress of the Chilean Association of Automatic Control: Towards an Industry 4.0 - Proceedings, 2019, doi: 10.1109/ICA-ACCA.2018.8609765.[2] A. M. Gontarz, D. Hampl, L. Weiss, y K. Wegener, “Resource Consumption Monitoring in Manufacturing Environments,” Procedia CIRP, vol. 26, pp. 264–269, 2015, doi: https://doi.org/10.1016/j.procir.2014.07.098.[3] H. Kathiriya, A. Pandya, V. Dubay, y A. Bavarva, “State of Art: Energy Efficient Protocols for Self-Powered Wireless Sensor Network in IIoT to Support Industry 4.0,” in ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), 2020, pp. 1311–1314, doi: 10.1109/ICRITO48877.2020.9198036.[4] O. Laayati, M. Bouzi, y A. Chebak, “Smart energy management: Energy consumption metering, monitoring and prediction for mining industry,” 2020, doi: 10.1109/ICECOCS50124.2020.9314532.[5] Y. K. Dwivedi et al., “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” Int. J. Inf. Manage., p. 101994, 2019, doi: https://doi.org/10.1016/j.ijinfomgt.2019.08.002.[6] A. Mayr et al., “Machine Learning in Production – Potentials, Challenges and Exemplary Applications,” Procedia CIRP, vol. 86, pp. 49–54, 2019, doi: https://doi.org/10.1016/j.procir.2020.01.035.[7] Y. H. Kuo y A. Kusiak, “From data to big data in production research: the past and future trends,” Int. J. Prod. Res., vol. 57, no. 15–16, pp. 4828–4853, 2019, doi: 10.1080/00207543.2018.1443230.[8] T. 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Web Docs, “Tecnología para desarrolladores web,” MDN Web Docs, 2021. https://developer.mozilla.org/es/docs/Web.Doctorado en IngenieríaAnalítica de datosConsumo energéticoInteligencia artificialModelo de informaciónPymeOPC UATransformación digitalData analyticsEnergy consumptionArtificial intelligenceInformation modelSMEsOPC UADigital transformationComunidad generalPublicationhttps://scholar.google.com.au/citations?user=7PIjh_MAAAAJ&hl=envirtual::6257-10000-0002-9731-8458virtual::6257-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000249106virtual::6257-1fc227fb1-22ec-47f0-afe7-521c61fddd32virtual::6257-1fc227fb1-22ec-47f0-afe7-521c61fddd32virtual::6257-1LICENSElicense.txtlicense.txttext/plain; charset=utf-81672https://red.uao.edu.co/bitstreams/d21b5aa8-54fe-4493-a026-d858b14403bb/download6987b791264a2b5525252450f99b10d1MD51ORIGINALT11470_Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto.pdfT11470_Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto.pdfArchivo texto completo del trabajo de grado, PDFapplication/pdf9194480https://red.uao.edu.co/bitstreams/94d9dc59-ad21-4d9c-a418-7570a887d5b5/download700ee956633f1905a722ee9020a537afMD52FO-IN-16 Acta_Sustentación_Final_Tesis_Doctoral_William Gutiérrez Marroquín_3May2022 v2 vMarco.pdfFO-IN-16 Acta_Sustentación_Final_Tesis_Doctoral_William Gutiérrez Marroquín_3May2022 v2 vMarco.pdfapplication/pdf150900https://red.uao.edu.co/bitstreams/ba82297b-1b1f-497b-b964-4bec5d32b5e4/downloada6f015ef4fc11c99b2cd971141c4d713MD53TA11470_Autorización trabajo de grado.pdfTA11470_Autorización trabajo de grado.pdfAutorización para publicación del trabajo de gradoapplication/pdf209565https://red.uao.edu.co/bitstreams/bca9b059-42ca-45d1-89f3-328db605f947/download35332ed8046a1eebd0e626d80bf733b4MD54TEXTT11470_Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto.pdf.txtT11470_Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto.pdf.txtExtracted texttext/plain101870https://red.uao.edu.co/bitstreams/a74e9c69-0ee1-40ff-88a6-1b5e5d4cf4d6/downloada2cfe012c2c45a8040c6eab0a9bacf76MD55FO-IN-16 Acta_Sustentación_Final_Tesis_Doctoral_William Gutiérrez Marroquín_3May2022 v2 vMarco.pdf.txtFO-IN-16 Acta_Sustentación_Final_Tesis_Doctoral_William Gutiérrez Marroquín_3May2022 v2 vMarco.pdf.txtExtracted texttext/plain7081https://red.uao.edu.co/bitstreams/c388c4a5-cbf6-4c3a-8478-0ec9ad154400/downloadf23eb32a20a417b1f4c2ccf28a141b95MD57TA11470_Autorización trabajo de grado.pdf.txtTA11470_Autorización trabajo de grado.pdf.txtExtracted texttext/plain4353https://red.uao.edu.co/bitstreams/cebd1ecd-a30b-47cc-8180-d32aa9b64fe5/download5bcf8efbeedf46970e79b960660e6773MD59THUMBNAILT11470_Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto.pdf.jpgT11470_Plataforma inteligente de análisis de datos para estimar el consumo energético proyectado en los sistemas de fabricación de las pymes en el contexto.pdf.jpgGenerated Thumbnailimage/jpeg6822https://red.uao.edu.co/bitstreams/3905fcf5-ce7a-438e-be70-5467b6f74a28/download24b4f3601dea24d95c0668bc5a14d555MD56FO-IN-16 Acta_Sustentación_Final_Tesis_Doctoral_William Gutiérrez Marroquín_3May2022 v2 vMarco.pdf.jpgFO-IN-16 Acta_Sustentación_Final_Tesis_Doctoral_William Gutiérrez Marroquín_3May2022 v2 vMarco.pdf.jpgGenerated Thumbnailimage/jpeg12353https://red.uao.edu.co/bitstreams/27559763-e60b-40a1-85af-deb5dd9187a7/downloadae48508ff16251e936f415e90ff8e55fMD58TA11470_Autorización trabajo de grado.pdf.jpgTA11470_Autorización trabajo de grado.pdf.jpgGenerated Thumbnailimage/jpeg12704https://red.uao.edu.co/bitstreams/691b0fb9-dba1-4c06-8a56-53535971d476/downloadb99f77504e83855de4e9acc3cc88371cMD51010614/16269oai:red.uao.edu.co:10614/162692025-08-20 03:02:02.293https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - 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