Material accountancy and process monitoring in a reprocessing facility is uniquely challenging due to the complexity of the highly radioactive process stream. The methods described here are an investigation of the feasibility of incorporating a Compton suppression system into a novel safeguards detection system called the Multi-Isotope Process (MIP) Monitor. The objective of the project is to build a methodology that detects subtle changes in the distribution of nuclides in a reprocessing stream using gamma ray spectra and multivariate analysis techniques autonomously in near-real time. The high concentration of ~(137)Cs in the aqueous process stream before the separation stage masks potential valuable minor gamma-ray lines because of the dominant 661.7 keV peak and subsequent Compton scattering effects. Compton suppression may be used to reduce the contribution of scattered gamma ray photons to the detector response, which allows small or totally obscured peaks to be better resolved. Pennsylvania State University is equipped with a commercially available Compton suppression system that was used to predict the suppressed Peak-to-Compton ratio of a spectrum from a sample of spent fuel. A Monte Carlo model generated using the simulation toolkit Geant-4 is being developed to predict the expected spectrum and to estimate reduction in the Compton continuum of the complex samples. This model will be beneficial as a basis for developing the simulations necessary when designing the detector and shielding geometries for the MIP monitor, but first a validation of the suppression modeling needs to be performed. Source definitions are generated using irradiation and decay calculations performed using ORIGEN-ARP. The model of the Compton suppression system is validated during development using the Penn State system and an array of sources combined to mimic the attributes of a spent fuel sample, i.e., a high ~(137)Cs contribution and several small, low-energy peaks. Results from the modeling and experiments of the Compton suppression technique's ability to resolve more low energy peaks in spent fuel samples are presented.
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