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Analysis of Reconstruction Energy Efficiency in EIT and ECT 3D Tomography Based on Elastic Net

cris.lastimport.scopus2024-09-19T01:30:53Z
dc.abstract.enThe main goal of this paper is to research and analyze the problem of image reconstruction performance using machine learning methods in 3D electrical capacitance tomography (ECT) and electrical impedance tomography (EIT) by comparing the areas inside the tank to determine the finite elements for which one of the method reconstructions is more effective. The research was conducted on 5000 simulated cases, which ranged from one to five inclusions generated for a cylindrical tank. The authors first used the elastic net learning method to perform the reconstruction and then proposed a method for testing the effectiveness of reconstruction. Based on this approach, the reconstructions obtained by each method were compared, and the areas within the object were identified. Finally, the results obtained from the simulation tests were verified on real measurements made with two types of tomographs. It was found that areas closer to the edge of the tank were more effectively reconstructed by EIT, while ECT reconstructed areas closer to the center of the tank. Extensive analysis of the inclusions makes it possible to use this measurement for energy optimization of industrial processes and biogas plant operation.
dc.affiliationTransportu i Informatyki
dc.contributor.authorBartosz Przysucha
dc.contributor.authorDariusz Wójcik
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorKrzysztof Król
dc.contributor.authorEdward Kozłowski
dc.contributor.authorMarcin Gąsior
dc.date.accessioned2024-04-12T08:09:13Z
dc.date.available2024-04-12T08:09:13Z
dc.date.issued2023
dc.description.abstract<jats:p>The main goal of this paper is to research and analyze the problem of image reconstruction performance using machine learning methods in 3D electrical capacitance tomography (ECT) and electrical impedance tomography (EIT) by comparing the areas inside the tank to determine the finite elements for which one of the method reconstructions is more effective. The research was conducted on 5000 simulated cases, which ranged from one to five inclusions generated for a cylindrical tank. The authors first used the elastic net learning method to perform the reconstruction and then proposed a method for testing the effectiveness of reconstruction. Based on this approach, the reconstructions obtained by each method were compared, and the areas within the object were identified. Finally, the results obtained from the simulation tests were verified on real measurements made with two types of tomographs. It was found that areas closer to the edge of the tank were more effectively reconstructed by EIT, while ECT reconstructed areas closer to the center of the tank. Extensive analysis of the inclusions makes it possible to use this measurement for energy optimization of industrial processes and biogas plant operation.</jats:p>
dc.identifier.doi10.3390/en16031490
dc.identifier.issn1996-1073
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/184
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofEnergies
dc.rightsCC-BY
dc.subject.enelectrical impedance tomography
dc.subject.enelectrical capacitance tomography
dc.subject.enmachine learning
dc.subject.eneffectiveness analysis
dc.subject.enenergy efficiency
dc.subject.enenergy consumption
dc.titleAnalysis of Reconstruction Energy Efficiency in EIT and ECT 3D Tomography Based on Elastic Net
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue3
oaire.citation.volume16