Air bubble detection in water flow by means of ai-assisted infrared reflection system
| dc.contributor.author | Gracia Moisés, Ander | |
| dc.contributor.author | Vitoria Pascual, Ignacio | |
| dc.contributor.author | Imas González, José Javier | |
| dc.contributor.author | Ruiz Zamarreño, Carlos | |
| dc.contributor.department | Ingeniería Eléctrica, Electrónica y de Comunicación | es_ES |
| dc.contributor.department | Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.date.accessioned | 2024-11-22T15:53:03Z | |
| dc.date.available | 2024-11-22T15:53:03Z | |
| dc.date.issued | 2024-06-26 | |
| dc.date.updated | 2024-11-22T15:26:36Z | |
| dc.description.abstract | 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. | en |
| dc.description.sponsorship | 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. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Gracia 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.doi | 10.1109/LSENS.2024.3419253 | |
| dc.identifier.issn | 2475-1472 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/52573 | |
| dc.language.iso | eng | |
| dc.publisher | IEEE | |
| dc.relation.ispartof | IEEE Sensors Letters 2024, 8(10), 3502804, 1-4 | |
| dc.relation.projectID | info:eu-repo/grantAgreement/Gobierno de Navarra/// | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI//PID2022-1374370B-100/ | |
| dc.relation.publisherversion | https://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.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Electromagnetic wave sensors | en |
| dc.subject | Artificial intelligence | en |
| dc.subject | Bubble detection | en |
| dc.subject | Machine learning | en |
| dc.subject | Principal component analysis (PCA) | en |
| dc.subject | Support vector machine (SVM) | en |
| dc.title | Air bubble detection in water flow by means of ai-assisted infrared reflection system | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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