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

dc.contributor.authorGracia Moisés, Ander
dc.contributor.authorVitoria Pascual, Ignacio
dc.contributor.authorImas González, José Javier
dc.contributor.authorRuiz Zamarreño, Carlos
dc.contributor.departmentIngeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentIngeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritzaeu
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.date.accessioned2024-11-22T15:53:03Z
dc.date.available2024-11-22T15:53:03Z
dc.date.issued2024-06-26
dc.date.updated2024-11-22T15:26:36Z
dc.description.abstractThis 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.en
dc.description.sponsorshipThis 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.
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGracia Moises, A., Vitoria Pascual, I., Imas Gonzalez, J. J., Ruiz-Zamarreno, C. (2024) Air bubble detection in water flow by means of ai-assisted infrared reflection system. IEEE Sensors Letters, 8(10), 1-4. https://doi.org/10.1109/LSENS.2024.3419253.
dc.identifier.doi10.1109/LSENS.2024.3419253
dc.identifier.issn2475-1472
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/52573
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofIEEE Sensors Letters 2024, 8(10), 3502804, 1-4
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Navarra///
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI//PID2022-1374370B-100/
dc.relation.publisherversionhttps://doi.org/10.1109/LSENS.2024.3419253
dc.rights© 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.
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subjectElectromagnetic wave sensorsen
dc.subjectArtificial intelligenceen
dc.subjectBubble detectionen
dc.subjectMachine learningen
dc.subjectPrincipal component analysis (PCA)en
dc.subjectSupport vector machine (SVM)en
dc.titleAir bubble detection in water flow by means of ai-assisted infrared reflection systemen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
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