Session: 02-02 Risk Assessment and Waste Optimization

Paper Number: 109668

109668 - Geostatistics Alara Principle for Waste Classification During Radiological Caracterization 

The objective of radiological characterization is to find a suitable balance between gathering data (constrained by cost, deadlines, accessibility or radiation) and managing the issues (waste volumes, levels of activity or exposure). It is necessary to have enough information to have confidence in the results without multiplying useless data.

Geostatistics processing of data considers all available pieces of information: historical data, non-destructive measurements and laboratory analyses of samples. The spatial structure modelling is then used to produce maps and to estimate the extent of radioactive contamination (surface and depth). Quantifications of local and global uncertainties are powerful decision-making tools for better management of remediation projects at contaminated sites, and for decontamination and dismantling projects at nuclear facilities. They can be used to identify hot spots, estimate contamination of surfaces and volumes, classify radioactive waste according to various radiological thresholds, estimate source terms, and so on.

This paper deals with feedback experience over years in the use of geostatistics for cost-benefit analyses about material segregation integrating estimation uncertainty and decision support impact. This approach puts the emphasis on one interesting geostatistics output: probability of exceeding a threshold.

From a global point of view, geostatistics provide risk curves for surfaces or volumes on the one hand and for accumulation (radiological inventory) on the other hand. The key issue is to limit false negative risk (leaving in place activity levels above the threshold) while handling false positive one (extra volumes due to over-conservatism).

When considering an objective function as the sum of the inverse cumulated frequency for probability levels and the normalised cost for remediation / decontamination, the corresponding curve generally present a global minimum that is a good balance between the two misclassification risks. That can be seen as the ALARA adaptation for waste classification, providing an objective and defendable balance between removed contamination and related treated volumes.

Presenting Author: Yvon Desnoyers Geovariances

Presenting Author Biography: Yvon Desnoyers now has 15 years of experience in consulting, mentoring, and training as a geostatistician expert at Geovariances. His main proficiency fields are sampling optimization and radiological characterization of nuclear facilities and contaminated sites to drive decommissioning & dismantling, and remediation projects. In addition, Yvon is actively involved in numerous working groups and R&D projects (France, Europe, and international organizations).