Dynamic Simulation Tool for Electrochemical Separation of Nuclear Fuel

John Caputo

Research Aide

Iowa State University

Argonne National Laboratory

 

Dynamic Simulation Tool for Electrochemical Separation of Nuclear Fuel

The disposition of used nuclear fuel is significant discussion area at Argonne National Laboratory. One method of reprocessing used fuel, known as electrorefining, uses high-temperature electrochemical dissolution of fuel from fast or light water reactors in order to separate fission products from uranium and transuranic elements that can then be recycled in fast reactor fuel. In this process, chopped fuel is placed in a basket at the anode and submerged in a molten LiCl-KCl eutectic mixture at 500°C; purified products are collected at cathodes also immersed in the salt. A potential applied across the system causes the fuel at the anode to dissolve while uranium and transuranic metals accumulate on the cathodes. Uranium(III) chloride is added as an oxidant and transport species. By adjusting the applied potential, the operator can adjust the relative concentrations of metal in the amalgam and the rate of deposition. The collected products are processed to remove any adhered salts prior and consolidated. Previous pyroprocessing modeling efforts have been focused on defining a steady state mass balance related to flow sheet development. The focus of this work has been on development of a dynamic simulation tool using MATLAB^® to model material flows in the electrorefiner. Accurate simulation of this process will improve material tracking and aid in design of process monitoring systems. The chemistry calculations are based on Butler-Volmer kinetics, and the overall process is assumed to be diffusion limited. The simulation allows for a number of user defined operational parameters including applied voltage, oxidant concentration, and fuel composition. The initial code will be compared with experimental data to ensure that the dynamic character is adequately captured, while further enhancements will center on improved models of the complex process chemistry.