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Innovative methods of neural reconstruction for tomographic images in maintenance of tank industrial reactors

cris.lastimport.scopus2024-09-15T01:30:59Z
dc.abstract.enThe article presents an innovative concept of improving the monitoring and optimization of industrial processes. The developed method is based on a system of many separately trained neural networks, in which each network generates a single point of the output image. Thanks to the elastic net method, the implemented algorithm reduces the correlated and irrelevant variables from the input measurement vector, making it more resistant to the phenomenon of data noises. The advantage of the described solution over known non-invasive methods is to obtain a higher resolution of images dynamically appearing inside the reactor of artifacts (crystals or gas bubbles), which essentially contributes to the early detection of hazards and problems associated with the operation of industrial systems, and thus increases the efficiency of chemical process control.
dc.affiliationTransportu i Informatyki
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorGrzegorz Kłosowski
dc.date.accessioned2024-05-07T10:30:05Z
dc.date.available2024-05-07T10:30:05Z
dc.date.issued2019
dc.identifier.doi10.17531/ein.2019.2.10
dc.identifier.issn1507-2711
dc.identifier.issn2956-3860
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/280
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofEksploatacja i Niezawodność – Maintenance and Reliability
dc.rightsCC-BY-NC
dc.subject.enelectrical tomography
dc.subject.enindustrial processes
dc.subject.enprocess control
dc.subject.enneural networks
dc.subject.enmachine learning
dc.titleInnovative methods of neural reconstruction for tomographic images in maintenance of tank industrial reactors
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue2
oaire.citation.volume21