A Bayesian framework to update scaling factors for radioactive waste characterization

Area of Science:

• Nuclear Engineering
• Radiochemistry
• Environmental Science

Background:

• Radioactive waste management relies on the scaling factor (SF) method to quantify difficult-to-measure (DTM) radionuclides.
• This method uses a relationship between an easy-to-measure (ETM) key nuclide (KN) and DTM radionuclides in waste samples.
• The accuracy of SFs can degrade over time due to evolving waste characteristics.

Purpose of the Study:

• To develop a simple Bayesian framework for updating scaling factors.
• To ensure the continued accuracy of DTM radionuclide activity quantification in radioactive waste.
• To provide a method adaptable to various waste streams and facilities.

Main Methods:

• A Bayesian statistical framework was developed to update scaling factors.
• The framework incorporates new data sets to refine the scaling factor distribution.
• The method was validated using radioactive waste data from CERN.

Main Results:

• The proposed Bayesian framework effectively updates scaling factors with new data.
• The method demonstrated accuracy in quantifying DTM radionuclides in CERN's radioactive waste.
• The framework is shown to be adaptable for radioactive waste from different origins.

Conclusions:

• The Bayesian framework offers a robust and adaptable solution for updating scaling factors in radioactive waste management.
• This approach enhances the reliability of DTM radionuclide activity measurements.
• The method facilitates more accurate characterization and management of radioactive waste over time.