Hyperspectral imaging based detection of PVC during Sellafield repackaging procedures

Zabalza, J. and Murray, P. and Marshall, S. and Ren, J. and Bernard, R. and Hepworth, S. (2022) Hyperspectral imaging based detection of PVC during Sellafield repackaging procedures. IEEE Sensors Journal. ISSN 1530-437X (https://doi.org/10.1109/jsen.2022.3221680)

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Abstract

Traditionally, Special Nuclear Material (SNM) at Sellafield has been stored in multi-layered packages, consisting of metallic cans and an over-layer of plasticized Polyvinyl Chloride (PVC) as an intermediate layer when transitioning between areas of different radiological classification. However, it has been found that plasticized PVC can break down in the presence of both radiation and heat, releasing hydrochloric acid which can corrode these metallic containers. Therefore, internal repackaging procedures at Sellafield have focused recently on the removal of these PVC films from containers, where as much degraded and often adhered PVC as possible is manually removed based on visual inspection. This manual operation is time-consuming and it is possible that residual fragments of PVC could remain, leading to corrosion-related issues in future. In this work, Hyperspectral Imaging (HSI) was evaluated as a new tool for detecting PVC on metallic surfaces. Samples of stainless steel type 1.4404 – also known as 316L, the same as is used to construct SNM cans – and PVC were imaged in our experiments, and Support Vector Machine (SVM) classification models were used to generate detection maps. In these maps, pixels were classified into either PVC or 316L based on their spectral responses in the range 954-1700nm of the electromagnetic spectrum. Results suggest that HSI could be used for an effective automated detection and quantification of PVC during repackaging procedures, detection and quantification that could be extended to other similar applications.

ORCID iDs

Zabalza, J. ORCID logoORCID: https://orcid.org/0000-0002-0634-1725, Murray, P. ORCID logoORCID: https://orcid.org/0000-0002-6980-9276, Marshall, S. ORCID logoORCID: https://orcid.org/0000-0001-7079-5628, Ren, J., Bernard, R. and Hepworth, S.;
  • Item type:Article
    ID code:83511
    Dates:
    Date
    Event
    24 November 2022
    Published
    24 November 2022
    Published Online
    24 November 2022
    Accepted
    Notes:© 2022 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 works.
    Keywords:support vector machines, steel, feature extraction, hyperspectral imaging, sensors, inspection, films, special nuclear material, Electrical Apparatus and Materials, Instrumentation, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering > Electrical apparatus and materials
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:13 Dec 2022 13:37
    Last modified:25 May 2023 11:10
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/83511
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