Hyperspectral imaging based detection of PVC during Sellafield repackaging procedures.

Zabalza, Jaime; Murray, Paul; Marshall, Stephen; Ren, Jinchang; Bernard, Robert; Hepworth, Steve

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.

Citation

ZABALZA, J., MURRAY, P., MARSHALL, S., REN, J., BERNARD, R. and HEPWORTH, S. 2023. Hyperspectral imaging based detection of PVC during Sellafield repackaging procedures. IEEE sensors journal [online], 23(1), pages 452-459. Available from: https://doi.org/10.1109/JSEN.2022.3221680

• Journal Article Type: Article
• Acceptance Date: Nov 24, 2022
• Online Publication Date: Nov 24, 2022
• Publication Date: Jan 1, 2023
• Deposit Date: Dec 1, 2022
• Publicly Available Date: Dec 1, 2022
• Journal: IEEE Sensors Journal
• Print ISSN: 1530-437X
• Electronic ISSN: 1558-1748
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 1
• Pages: 452-459
• DOI: https://doi.org/10.1109/JSEN.2022.3221680
• Keywords: Hyperspectral imaging; PVC; Repackaging; Special nuclear material; Support vector machines; Steel; Feature extraction; Sensors; Inspection; Films; Polyvinyl chloride (PVC); Repackaging
• Public URL: https://rgu-repository.worktribe.com/output/1823692