Air bubble detection in water flow by means of ai-assisted infrared reflection system

This letter introduces an innovative, cost-effective solution for detecting air bubbles in water flow systems using an AI-assisted infrared reflection system. In industries, such as chemical, mechanical, oil, and nuclear, the presence of air bubbles in fluids can compromise both product quality and...

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Detalles Bibliográficos
Autores: Gracia Moisés, Ander, Vitoria Pascual, Ignacio, Imas González, José Javier, Ruiz Zamarreño, Carlos
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/52573
Acceso en línea:https://hdl.handle.net/2454/52573
Access Level:acceso abierto
Palabra clave:Electromagnetic wave sensors
Artificial intelligence
Bubble detection
Machine learning
Principal component analysis (PCA)
Support vector machine (SVM)
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spelling Air bubble detection in water flow by means of ai-assisted infrared reflection systemGracia Moisés, AnderVitoria Pascual, IgnacioImas González, José JavierRuiz Zamarreño, CarlosElectromagnetic wave sensorsArtificial intelligenceBubble detectionMachine learningPrincipal component analysis (PCA)Support vector machine (SVM)This letter introduces an innovative, cost-effective solution for detecting air bubbles in water flow systems using an AI-assisted infrared reflection system. In industries, such as chemical, mechanical, oil, and nuclear, the presence of air bubbles in fluids can compromise both product quality and process efficiency. Our research develops a system that combines infrared optical sensors with machine learning algorithms to detect and quantify bubble presence effectively. The system’s design utilizes infrared emitters and photodetectors arranged around a pipe to capture detailed data on bubble characteristics, which is then analyzed using a support vector machine (SVM) model to predict bubble concentrations. Experimental results demonstrate the system’s ability to accurately identify different levels of bubble presence, offering significant improvements over existing methods. Key performance metrics include a mean squared error of 0.0694, a root mean squared error of 0.2634, and a coefficient of determination of 0.9765, indicating high accuracy and reliability. This approach not only enhances operational reliability and safety but also provides a scalable solution adaptable to various industrial settings.This work was supported in part by the Government of Navarra through Industrial Doctorate grants 2021 and in part by theMinistry of Science and Innovation under Grant PID2022-1374370B-100.IEEEIngeniería Eléctrica, Electrónica y de ComunicaciónIngeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio IngeniaritzaInstitute of Smart Cities - ISC2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2454/52573reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/Gobierno de Navarra//info:eu-repo/grantAgreement/AEI//PID2022-1374370B-100© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/525732026-06-17T12:41:47Z
dc.title.none.fl_str_mv Air bubble detection in water flow by means of ai-assisted infrared reflection system
title Air bubble detection in water flow by means of ai-assisted infrared reflection system
spellingShingle Air bubble detection in water flow by means of ai-assisted infrared reflection system
Gracia Moisés, Ander
Electromagnetic wave sensors
Artificial intelligence
Bubble detection
Machine learning
Principal component analysis (PCA)
Support vector machine (SVM)
title_short Air bubble detection in water flow by means of ai-assisted infrared reflection system
title_full Air bubble detection in water flow by means of ai-assisted infrared reflection system
title_fullStr Air bubble detection in water flow by means of ai-assisted infrared reflection system
title_full_unstemmed Air bubble detection in water flow by means of ai-assisted infrared reflection system
title_sort Air bubble detection in water flow by means of ai-assisted infrared reflection system
dc.creator.none.fl_str_mv Gracia Moisés, Ander
Vitoria Pascual, Ignacio
Imas González, José Javier
Ruiz Zamarreño, Carlos
author Gracia Moisés, Ander
author_facet Gracia Moisés, Ander
Vitoria Pascual, Ignacio
Imas González, José Javier
Ruiz Zamarreño, Carlos
author_role author
author2 Vitoria Pascual, Ignacio
Imas González, José Javier
Ruiz Zamarreño, Carlos
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería Eléctrica, Electrónica y de Comunicación
Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza
Institute of Smart Cities - ISC
dc.subject.none.fl_str_mv Electromagnetic wave sensors
Artificial intelligence
Bubble detection
Machine learning
Principal component analysis (PCA)
Support vector machine (SVM)
topic Electromagnetic wave sensors
Artificial intelligence
Bubble detection
Machine learning
Principal component analysis (PCA)
Support vector machine (SVM)
description This letter introduces an innovative, cost-effective solution for detecting air bubbles in water flow systems using an AI-assisted infrared reflection system. In industries, such as chemical, mechanical, oil, and nuclear, the presence of air bubbles in fluids can compromise both product quality and process efficiency. Our research develops a system that combines infrared optical sensors with machine learning algorithms to detect and quantify bubble presence effectively. The system’s design utilizes infrared emitters and photodetectors arranged around a pipe to capture detailed data on bubble characteristics, which is then analyzed using a support vector machine (SVM) model to predict bubble concentrations. Experimental results demonstrate the system’s ability to accurately identify different levels of bubble presence, offering significant improvements over existing methods. Key performance metrics include a mean squared error of 0.0694, a root mean squared error of 0.2634, and a coefficient of determination of 0.9765, indicating high accuracy and reliability. This approach not only enhances operational reliability and safety but also provides a scalable solution adaptable to various industrial settings.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/52573
url https://hdl.handle.net/2454/52573
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/Gobierno de Navarra//
info:eu-repo/grantAgreement/AEI//PID2022-1374370B-100
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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