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. (2023) Hyperspectral imaging based detection of PVC during Sellafield repackaging procedures. IEEE Sensors Journal, 23 (1). pp. 452-459. ISSN 1530-437X (https://doi.org/10.1109/jsen.2022.3221680)

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Traditionally, special nuclear material (SNM) at Sellafield has been stored in multilayered packages, consisting of metallic cans and an overlayer of plasticized polyvinyl chloride (PVC) as an intermediate layer when transitioning between areas of different radiological classification. However, it has been found that the 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. The 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 954-1700 nm of the electromagnetic spectrum. Results suggest that the 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.