Advanced tomography imaging for evaluating radioactive waste conformity for safe final disposal
The safe disposal of nuclear legacy waste remains a major challenge, as much of this material was conditioned decades ago under outdated practices, prior to the establishment of final waste acceptance criteria (WAC). In Belgium, the recent licensing of the category A waste (cAt) facility for near-surface disposal of low- and intermediate level short-lived radioactive waste requires verification of approximately 70,000 m³ of conditioned waste, predominantly stored in 400-liter drums. Significant uncertainties persist regarding chemical composition, packaging integrity, and compliance with the final WAC, particularly for critical components such as cellulose, chlorides, and sulphates.
Non-destructive examination (NDE) methods are essential to close these information gaps while preserving package integrity. Conventional radiographic techniques lack the penetration and resolution needed for dense, heterogeneous matrices. High-energy (megavolt, MV) computed tomography (CT) using a linear accelerator (LINAC) offers a promising solution, enabling three-dimensional visualization and, when combined with dual-energy CT (DECT), material discrimination and compositional analysis.
This project aims to establish a non-destructive MV-CT workflow for historical waste characterization at SCK CEN’s LNK (Laboratory of nuclear calibrations) facility. Key innovations include advanced physics-informed reconstruction algorithms and integration of simulation and AI-based analysis. An analytical Monte Carlo-inspired simulator will optimize system geometry and acquisition parameters, support sensitivity studies, and serve as a forward projector in image reconstruction. Dual-energy imaging will be explored for quantitative determination of electron density (ρₑ) and effective atomic number (Zeff), for the inference of WAC-relevant indicators. Machine learning will assist in artifact correction, segmentation, and material classification.
By combining experimental imaging, simulation, and data-driven interpretation, this approach will deliver high-resolution, reliable characterization of conditioned waste drums, reducing uncertainties and supporting safe, compliant disposal in Belgium’s near-surface repository.