TY - JOUR
T1 - Hyperspectral imaging based detection of PVC during Sellafield repackaging procedures
AU - Zabalza, J.
AU - Murray, P.
AU - Marshall, S.
AU - Ren, J.
AU - Bernard, R.
AU - Hepworth, S.
N1 - © 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.
PY - 2022/11/24
Y1 - 2022/11/24
N2 - 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.
AB - 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.
KW - support vector machines
KW - steel
KW - feature extraction
KW - hyperspectral imaging
KW - sensors
KW - inspection
KW - films
KW - special nuclear material
UR - https://rgu-repository.worktribe.com/output/1823692/hyperspectral-imaging-based-detection-of-pvc-during-sellafield-repackaging-procedures
U2 - 10.1109/jsen.2022.3221680
DO - 10.1109/jsen.2022.3221680
M3 - Article
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
SN - 1530-437X
ER -