The FIT Model - Fuel-cycle Integration and Tradeoffs

Piet, Steven J. ; Soelberg, Nick R. ; Bays, Samuel E. ; Pereira, Candido ; Pincock, Layne F. ; Shaber, Eric L. ; Teague, Meliisa C ; Teske, Gregory M ; Vedros, Kurt G

Idaho National Laboratory

关键词: Global Analysis;    Reprocessing;    Storage;    Waste Management;    Mass Balance;   

DOI  :  10.2172/1010680
RP-ID  :  INL/EXT-10-20190
RP-ID  :  DE-AC07-05ID14517
RP-ID  :  1010680

美国|英语

来源: UNT Digital Library

【 摘 要 】

All mass streams from fuel separation and fabrication are products that must meet some set of product criteria – fuel feedstock impurity limits, waste acceptance criteria (WAC), material storage (if any), or recycle material purity requirements such as zirconium for cladding or lanthanides for industrial use. These must be considered in a systematic and comprehensive way. The FIT model and the “system losses study” team that developed it [Shropshire2009, Piet2010] are an initial step by the FCR&D program toward a global analysis that accounts for the requirements and capabilities of each component, as well as major material flows within an integrated fuel cycle. This will help the program identify near-term R&D needs and set longer-term goals. The question originally posed to the “system losses study” was the cost of separation, fuel fabrication, waste management, etc. versus the separation efficiency. In other words, are the costs associated with marginal reductions in separations losses (or improvements in product recovery) justified by the gains in the performance of other systems? We have learned that that is the wrong question. The right question is: how does one adjust the compositions and quantities of all mass streams, given uncertain product criteria, to balance competing objectives including cost? FIT is a method to analyze different fuel cycles using common bases to determine how chemical performance changes in one part of a fuel cycle (say used fuel cooling times or separation efficiencies) affect other parts of the fuel cycle. FIT estimates impurities in fuel and waste via a rough estimate of physics and mass balance for a set of technologies. If feasibility is an issue for a set, as it is for “minimum fuel treatment” approaches such as melt refining and AIROX, it can help to make an estimate of how performances would have to change to achieve feasibility.