Process Models For Electrochemical Recycling Of Used Nuclear Fuel

Within the Materials Protection, Accounting, and Control Technologies (MPACT) Campaign, of the U.S. Department of Energy Office of Nuclear Energy, unit operation modeling capabilities have been developed and integrated into a used fuel treatment facility model, to support development of advanced safeguards and security technologies. The Argonne Model for Pyrochemical Recycling (AMPYRE) calculates a time-dependent material mass balance in an electrochemical treatment facility. The model, created in MATLAB™, consists of an interconnected set of modular unit operations models. It predicts dynamic product and waste compositions and buffering of material inventories at each unit operation as spent nuclear fuel is processed. AMPYRE tracks all major fuel components and several key components affecting process chemistry and bulk mass projection. All significant material movements are captured, including recovery and recycle of electrorefiner salt via supporting unit operations. Detailed unit operations models interface with AMPYRE. The Dynamic Electrorefiner (DyER) code simulates distribution of material between major phases in an electrorefiner (anode, cathodes, and molten salt). The time-dependent DyER code predicts chemistry-based material separations using thermodynamic and kinetic parameters from the open literature or values supplied by the user. Transient conditions in the electrorefiner can be explored, and outcomes arising from time-varying or off-normal inputs or conditions can be predicted. Additional electrochemical operations for treatment of process salts within the facility are represented with a modified DyER code, as the fundamental electrochemistry is the same. Models with varying levels of complexity represent other non-electrochemical unit operations (i.e. salt distillation, waste salt treatment, off-gas capture). AMPYRE output is also being visualized as a 3D plant simulation using Blender™ software. This imparts dimensionality and motion to process vessels and material streams, for assessing the interoperability of unit operations and to inform placement and selection of sensors. Together, these modeling capabilities support development of safeguards and security strategies where direct measurement is not practical during process operations, or when a fully integrated set of processes is not available.