Dissemination

In 2011 the ActiWiz code was developed at CERN in order to optimize the choice of materials for accelerator equipment from a radiological point of view. Since then the code has been extended to allow for calculating complete nuclide inventories and provide evaluations with respect to radiotoxicity, inhalation doses, etc.

> ActiWiz – optimizing your nuclide inventory at proton accelerators with a computer code
> ActiWiz 3 – an overview of the latest developments and their application
Authors: H. Vincke, C. Theis

 The radiological characterization of waste includes the establishment of the list of produced radionuclides, called “radionuclide inventory”, and the estimation of their activity. The process adopted at CERN by our team consists of measuring and estimating the activity of produced radionuclides either by experimental methods or statistical and numerical approaches. The following paper describes the “scaling factor” approach:

> A new approach to characterize very-low-level radioactive waste produced at hadron accelerators
Authors: B. Zaffora, M. Magistris, et al.

Easy-to-measure nuclides, like γ-emitters and high-energy X-rays, can be measured via non-destructive nuclear techniques from outside a waste package. Some radionuclides are difficult-to-measure (DTM) from outside a package because they are α- or β-emitters. To quantify the activity of DTMs, a correlation between their activity and the activity of ETMs is established. However, that correlation can only be checked if the sampling technique used to measure DTM activity follows certain criteria. This is described in the following paper:

> Statistical sampling applied to the radiological characterization of historical waste
Authors: B. Zaffora, M. Magistris, et al.

Material activation can sometimes cause large heterogeneities in the distribution of radioactivity (hotspots). Moreover, the sample geometry parameters are not always well known. When performing gamma-spectroscopy to quantify the radionuclide inventory in activated materials, often predefined models are used to represent the sample geometry. In the following paper, a new approach, based on ISOCS/LabSOCS to quantify and reduce uncertainties originating from the geometry model is introduced.

> A novel technique for the optimization and reduction of gamma spectroscopy geometry uncertainties
Authors: T. Frosio, M. Magistris, et al.

Using total gamma counting and the fingerprint method to radiologically characterize burnable waste

> Radiological characterisation for the clearance of burnable waste produced at CERN
Authors: R. Harbon, et al.

Developing a data-driven probabilistic approach that combines activation simulations with classical machine learning in order to address the diversity of activation scenarios, radionuclide inventories and material compositions.

> Radiological clearance at CERN: classical machine learning methods for waste classification
Authors: A. Gomes, M. Magistris, et al.

Developing a methodology to characterize massive, large electromagnets based on their dose rate

> Radiological characterization of large electromagnets in view of their elimination as very low-level wastes
Authors: T. Frosio, M. Magistris, et al.