MARIE: A Python-Based Framework for Comprehensive Fuel Recycling Modeling

One of the most pressing challenges for the continued deployment of nuclear energy systems is in the ultimate management and disposition of discharged fuel assemblies. While reprocessing and recovery of valuable materials from used nuclear fuel assemblies has been considered as part of an overall strategy to minimize the volume of reactor-based wastes to be managed, the deployment of commercial-scale reprocessing facilities presents an enormous economic challenge. The Model for the Assessment of Reprocessing and Recycle with Innovative Execution (MARIE) software package has been developed as a means of confronting this challenge. Representing components of a generic fuel reprocessing operation as individual physical processes, MARIE is designed as a modular framework intended to allow for analysis and cost optimization for a hypothetical reprocessing facility while realistically accounting for the physical characteristics of the used fuel source term, such as decay heat, activity, and radiation dose (informing corresponding shielding requirements). Capabilities supported by MARIE include head-end operations such as fuel shearing, voloxidation, and dissolution; generic solvent extraction operations informed by available open-literature data; a suite of unit operations intended to represent electrochemical processing of used fuel assemblies (i.e., oxide reduction, electrorefining, and electrowinning); and, finally, accounting for both the costs and physical features of discharged waste streams, which can be used to inform follow-on analyses such as the feasibility of deep-borehole disposal of high-level radioactive waste. This paper presents an overview of the MARIE software capabilities, including how individual unit operations are implemented to enable a larger-scale optimization of a hypothetical reprocessing operation on aspects such as cost and recovery of valuable materials.