The FIT 2.0 Model - Fuel-cycle Integration and Tradeoffs

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, Piet2010b] are steps by the Fuel Cycle Technology program toward an analysis that accounts for the requirements and capabilities of each fuel cycle 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. This report describes FIT 2, an update of the original FIT model.[Piet2010c] FIT is a method to analyze different fuel cycles; in particular, to determine how changes in one part of a fuel cycle (say, fuel burnup, cooling, or separation efficiencies) chemically affect other parts of the fuel cycle. FIT provides the following: Rough estimate of physics and mass balance feasibility of combinations of technologies. If feasibility is an issue, it provides an estimate of how performance would have to change to achieve feasibility. Estimate of impurities in fuel and impurities in waste as function of separation performance, fuel fabrication, reactor, uranium source, etc

The FIT Model - Fuel-cycle Integration and Tradeoffs

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

The FIT Model - Fuel-cycle Integration and Tradeoffs

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

Assessment of Tools and Data for System-Level Dynamic Analyses

The only fuel cycle for which dynamic analyses and assessments are not needed is the null fuel cycle - no nuclear power. For every other concept, dynamic analyses are needed and can influence relative desirability of options. Dynamic analyses show how a fuel cycle might work during transitions from today's partial fuel cycle to something more complete, impact of technology deployments, location of choke points, the key time lags, when benefits can manifest, and how well parts of fuel cycles work together. This report summarizes the readiness of existing Fuel Cycle Technology (FCT) tools and data for conducting dynamic analyses on the range of options. VISION is the primary dynamic analysis tool. Not only does it model mass flows, as do other dynamic system analysis models, but it allows users to explore various potential constraints. The only fuel cycle for which constraints are not important are those in concept advocates PowerPoint presentations; in contrast, comparative analyses of fuel cycles must address what constraints exist and how they could impact performance. The most immediate tool need is extending VISION to the thorium/U233 fuel cycle. Depending on further clarification of waste management strategies in general and for specific fuel cycle candidates, waste management sub-models in VISION may need enhancement, e.g., more on 'co-flows' of non-fuel materials, constraints in waste streams, or automatic classification of waste streams on the basis of user-specified rules. VISION originally had an economic sub-model. The economic calculations were deemed unnecessary in later versions so it was retired. Eventually, the program will need to restore and improve the economics sub-model of VISION to at least the cash flow stage and possibly to incorporating cost constraints and feedbacks. There are multiple sources of data that dynamic analyses can draw on. In this report, 'data' means experimental data, data from more detailed theoretical or empirical calculations on technology performance, and assumptions such as the earliest date a technology can be deployed. The only fuel cycles for which we currently have adequate data are those we are sure we will never build, e.g., a PUREX plant in the U.S. For actual candidates, even for once through LWRs, there remain missing data such as how the fuel cycle would be completed with a geologic repository. The most immediate data needs are probably basic reactor physics data for new concepts and data associated with waste management for anything other than current technology. The readiness of tools and data is fluid and depends on what purposes are envisioned to drive upcoming analyses and further definition of the waste-related characteristics of fuel cycle candidates. Tools and data sets evolve as needs evolve. Thus, much of the document explains that if the FCT program wants a certain type of analysis, then the tools and data needs are as indicated. For example, functions can be treated as either commodities or facilities. Reactors, separation, fuel fabrication, repository are treated as facility types. Other functions such as uranium mining, conversion, enrichment, and waste packaging and non-repository disposal are treated as commodities and therefore not modeled as extensively. In summary, the tools are functional and can answer many fuel cycle questions but some analyses will require that the tools be modified to support those analyses

Assessment of Tools and Data for System-Level Dynamic Analyses

The only fuel cycle for which dynamic analyses and assessments are not needed is the null fuel cycle - no nuclear power. For every other concept, dynamic analyses are needed and can influence relative desirability of options. Dynamic analyses show how a fuel cycle might work during transitions from today's partial fuel cycle to something more complete, impact of technology deployments, location of choke points, the key time lags, when benefits can manifest, and how well parts of fuel cycles work together. This report summarizes the readiness of existing Fuel Cycle Technology (FCT) tools and data for conducting dynamic analyses on the range of options. VISION is the primary dynamic analysis tool. Not only does it model mass flows, as do other dynamic system analysis models, but it allows users to explore various potential constraints. The only fuel cycle for which constraints are not important are those in concept advocates PowerPoint presentations; in contrast, comparative analyses of fuel cycles must address what constraints exist and how they could impact performance. The most immediate tool need is extending VISION to the thorium/U233 fuel cycle. Depending on further clarification of waste management strategies in general and for specific fuel cycle candidates, waste management sub-models in VISION may need enhancement, e.g., more on 'co-flows' of non-fuel materials, constraints in waste streams, or automatic classification of waste streams on the basis of user-specified rules. VISION originally had an economic sub-model. The economic calculations were deemed unnecessary in later versions so it was retired. Eventually, the program will need to restore and improve the economics sub-model of VISION to at least the cash flow stage and possibly to incorporating cost constraints and feedbacks. There are multiple sources of data that dynamic analyses can draw on. In this report, 'data' means experimental data, data from more detailed theoretical or empirical calculations on technology performance, and assumptions such as the earliest date a technology can be deployed. The only fuel cycles for which we currently have adequate data are those we are sure we will never build, e.g., a PUREX plant in the U.S. For actual candidates, even for once through LWRs, there remain missing data such as how the fuel cycle would be completed with a geologic repository. The most immediate data needs are probably basic reactor physics data for new concepts and data associated with waste management for anything other than current technology. The readiness of tools and data is fluid and depends on what purposes are envisioned to drive upcoming analyses and further definition of the waste-related characteristics of fuel cycle candidates. Tools and data sets evolve as needs evolve. Thus, much of the document explains that if the FCT program wants a certain type of analysis, then the tools and data needs are as indicated. For example, functions can be treated as either commodities or facilities. Reactors, separation, fuel fabrication, repository are treated as facility types. Other functions such as uranium mining, conversion, enrichment, and waste packaging and non-repository disposal are treated as commodities and therefore not modeled as extensively. In summary, the tools are functional and can answer many fuel cycle questions but some analyses will require that the tools be modified to support those analyses

Waste Classification based on Waste Form Heat Generation in Advanced Nuclear Fuel Cycles Using the Fuel-Cycle Integration and Tradeoffs (FIT) Model

This study explores the impact of wastes generated from potential future fuel cycles and the issues presented by classifying these under current classification criteria, and discusses the possibility of a comprehensive and consistent characteristics-based classification framework based on new waste streams created from advanced fuel cycles. A static mass flow model, Fuel-Cycle Integration and Tradeoffs (FIT), was used to calculate the composition of waste streams resulting from different nuclear fuel cycle choices. This analysis focuses on the impact of waste form heat load on waste classification practices, although classifying by metrics of radiotoxicity, mass, and volume is also possible. The value of separation of heat-generating fission products and actinides in different fuel cycles is discussed. It was shown that the benefits of reducing the short-term fission-product heat load of waste destined for geologic disposal are neglected under the current source-based radioactive waste classification system , and that it is useful to classify waste streams based on how favorable the impact of interim storage is in increasing repository capacity

Waste Classification based on Waste Form Heat Generation in Advanced Nuclear Fuel Cycles Using the Fuel-Cycle Integration and Tradeoffs (FIT) Model -13413

ABSTRACT This study explores the impact of wastes generated from potential future fuel cycles and the issues presented by classifying these under current classification criteria, and discusses the possibility of a comprehensive and consistent characteristics-based classification framework based on new waste streams created from advanced fuel cycles. A static mass flow model, Fuel-Cycle Integration and Tradeoffs (FIT), was used to calculate the composition of waste streams resulting from different nuclear fuel cycle choices. This analysis focuses on the impact of waste form heat load on waste classification practices, although classifying by metrics of radiotoxicity, mass, and volume is also possible. The value of separation of heat-generating fission products and actinides in different fuel cycles is discussed. It was shown that the benefits of reducing the short-term fission-product heat load of waste destined for geologic disposal are neglected under the current source-based radioactive waste classification system, and that it is useful to classify waste streams based on how favorable the impact of interim storage is in increasing repository capacity

The FIT 2.0 Model - Fuel-cycle Integration and Tradeoffs

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, Piet2010b] are steps by the Fuel Cycle Technology program toward an analysis that accounts for the requirements and capabilities of each fuel cycle 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. This report describes FIT 2, an update of the original FIT model.[Piet2010c] FIT is a method to analyze different fuel cycles; in particular, to determine how changes in one part of a fuel cycle (say, fuel burnup, cooling, or separation efficiencies) chemically affect other parts of the fuel cycle. FIT provides the following: Rough estimate of physics and mass balance feasibility of combinations of technologies. If feasibility is an issue, it provides an estimate of how performance would have to change to achieve feasibility. Estimate of impurities in fuel and impurities in waste as function of separation performance, fuel fabrication, reactor, uranium source, etc

Radioactive Iodine and Krypton Control for Nuclear Fuel Reprocessing Facilities

The removal of volatile radionuclides generated during used nuclear fuel reprocessing in the US is almost certain to be necessary for the licensing of a reprocessing facility in the US. Various control technologies have been developed, tested, or used over the past 50 years for control of volatile radionuclide emissions from used fuel reprocessing plants. The US DOE has sponsored, since 2009, an Off-gas Sigma Team to perform research and development focused on the most pressing volatile radionuclide control and immobilization problems. In this paper, we focus on the control requirements and methodologies for 85Kr and 129I. Numerous candidate technologies have been studied and developed at laboratory and pilot-plant scales in an effort to meet the need for high iodine control efficiency and to advance alternatives to cryogenic separations for krypton control. Several of these show promising results. Iodine decontamination factors as high as 105, iodine loading capacities, and other adsorption parameters including adsorption rates have been demonstrated under some conditions for both silver zeolite (AgZ) and Ag-functionalized aerogel. Sorbents, including an engineered form of AgZ and selected metal organic framework materials (MOFs), have been successfully demonstrated to capture Kr and Xe without the need for separations at cryogenic temperatures

System Losses Study - FIT (Fuel-cycle Integration and Tradeoffs)

This team aimed to understand the broad implications of changes of operating performance and parameters of a fuel cycle component on the entire system. In particular, this report documents the study of the impact of changing the loss of fission products into recycled fuel and the loss of actinides into waste. When the effort started in spring 2009, an over-simplified statement of the objective was “the number of nines” – how would the cost of separation, fuel fabrication, and waste management change as the number of nines of separation efficiency changed. The intent was to determine the optimum “losses” of TRU into waste for the single system that had been the focus of the Global Nuclear Energy Program (GNEP), namely sustained recycle in burner fast reactors, fed by transuranic (TRU) material recovered from used LWR UOX-51 fuel. That objective proved to be neither possible (insufficient details or attention to the former GNEP options, change in national waste management strategy from a Yucca Mountain focus) nor appropriate given the 2009-2010 change to a science-based program considering a wider range of options. Indeed, the definition of “losses” itself changed from the loss of TRU into waste to a generic definition that a “loss” is any material that ends up where it is undesired. All streams from either separation or fuel fabrication are products; fuel feed streams must lead to fuels with tolerable impurities and waste streams must meet waste acceptance criteria (WAC) for one or more disposal sites. And, these losses are linked in the sense that as the loss of TRU into waste is reduced, often the loss or carryover of waste into TRU or uranium is increased. The effort has provided a mechanism for connecting these three Campaigns at a technical level that had not previously occurred – asking smarter and smarter questions, sometimes answering them, discussing assumptions, identifying R&D needs, and gaining new insights. The FIT model has been a forcing function, helping the team in this endeavor. Models don’t like “TBD” as an input, forcing us to make assumptions and see if they matter. A major addition in FY 2010 was exploratory analysis of “modified open fuel” cycles, employing “minimum fuel treatment” as opposed to full aqueous or electrochemical separation treatment. This increased complexity in our analysis and analytical tool development because equilibrium conditions do not appear sustainable in minimum fuel treatment cases, as was assumed in FY 2009 work with conventional aqueous and electrochemical separation. It is no longer reasonable to assume an equilibrium situation exists in all cases