3 research outputs found

    Separation of Fluoride Residue Arising from Fluoride Volatility Recovery of Uranium from Spent Nuclear Fuel

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    The overall objective of this study was to support an alternative hybrid process to meet Advanced Fuel Cycle Initiative (AFCI) goals, using fluorination and aqueous processing techniques, for treatment of spent nuclear fuel (SNF). The specific goal was to develop a simple aqueous dissolution process to separate two high-heat fission products, cesium and strontium, from SNF fluoride residues. This separation study was based on solubility differences examined by modeling using the HSC Chemistry 5.0 and OLI Stream Analyzer 1.2 programs. HSC automatically utilizes an extensive thermochemical database, which contains enthalpy (H), entropy (S), and heat capacity (Cp) data for more than 17,000 chemical compounds. The OLI Stream Analyzer 1.2 program is the result of over 30 years of effort and represents the state-of-the-art technology in aqueous solution simulation. The work focused on the fluoride residues from the voloxidation and fluorination steps of the fluoride volatility process and was limited to SNF from commercial light-water reactors. Material balances were used to estimate the quantity of residue. A representative SNF was considered to be one with a burnup of 33,000 megawatt days per metric tonne initial heavy metal (MWd/MTIHM) after a 10-year cooling period, from a pressurized-water reactor (PWR). The dry fluorination method was used for uranium removal. The work described in this paper was based solely on computer modeling, which may serve as the basis for any necessary follow-on laboratory validation experiments. Observations from this study showed that the separation of fluoride residues by a simplified, alternative aqueous process is practical. The simulated process could be carried out at 1 atm and 30-50oC. The OLI model showed separation of cesium and strontium was possible with only one dissolution with water, whereas the HSC model indicated two dissolutions would be required. Plutonium and Np were removed together, which would maintain proliferation resistance. Because this research was based on computer modeling, follow-on laboratory experiments are necessary to validate the results and to improve the process flow diagram. Further development of the process flow diagram, with equipment design and cost estimation, is also recommended

    Development of a Monitoring Framework for the Detection of Diversion of Intermediate Products in a Generic Natural Uranium Conversion Plant

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    The objective of this work is the development of an on-line monitoring and data analysis framework that could detect the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride in a natural uranium conversion plant (NUCP) using a multivariate statistical approach. This was an initial effort to determine the feasibility of this approach for safeguards applications. This study was limited to a 100 metric ton of uranium (MTU) per year NUCP using the wet solvent extraction method for the purification of uranium ore concentrate. A key component in the multivariate statistical methodology was the Principal Component Analysis (PCA) approach for the analysis of data, development of the base model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix. Component mole balances were used to model each of the process units in the NUCP. The decision framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. The IAEA goal for NUCPs of this size is to have a 50% probability of detecting the diversion of 10 MTU over a period of one year; this was also used as the goal of detection for the monitoring framework. An initial sensitivity analysis was also performed on the relationship between the component molar flow rates (state variables) and the process parameters. This sensitivity study identified a few parameters to which some of the state variables were highly sensitive. Several faulty scenarios were developed to test the monitoring framework after the base case or “normal operating conditions” of the PCA model was established. In nearly all of the scenarios, the monitoring framework was able to detect the fault. The detection limit varied depending on the scenario, but it satisfied the limit stated above in nearly of the all cases. For the cases that the goal was not achieved, additional scaling may be able to lower the detection limit to satisfy the goal. Overall this study was successful at meeting the stated objective
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