乏核燃料
普雷克斯
化学
核燃料
硝酸
拉曼光谱
钚
磷酸三丁酯
核后处理
水溶液
分析化学(期刊)
萃取(化学)
放射化学
核化学
无机化学
溶剂萃取
环境化学
色谱法
物理化学
物理
光学
作者
Samuel A. Bryan,Tatiana G. Levitskaia,Amanda M. Johnsen,Christopher R. Orton,James M. Peterson
出处
期刊:Radiochimica Acta
[R. Oldenbourg Verlag]
日期:2011-06-09
卷期号:99 (9): 563-572
被引量:71
标识
DOI:10.1524/ract.2011.1865
摘要
Abstract The potential of using optical spectroscopic techniques, such as Raman and visible/near infrared (Vis/NIR), for on-line process control and special nuclear materials accountability applications at a spent nuclear fuel reprocessing facility was evaluated. The availability of on-line, real-time techniques that directly measure process concentrations of nuclear materials will enhance the performance and proliferation resistance of the solvent extraction processes. Further, on-line monitoring of radiochemical streams will also improve reprocessing plant operation and safety. This paper reviews the current state of development of the spectroscopic on-line monitoring techniques for such solutions. To further examine the applicability of optical spectroscopy for this application, segments of a spent nuclear fuel, with approximate burn-up values of 70 MW d/kg M, were dissolved in concentrated nitric acid and adjusted to varying final concentrations of HNO 3 . The resulting spent fuel solutions were batch-contacted with tributyl phosphate/ n -dodecane organic solvent. The feed and equilibrium aqueous and loaded organic solutions were subjected to optical measurements. The obtained spectra showed the presence of quantifiable Raman bands due to NO 3 − and UO 2 2+ and Vis/NIR bands due to multiple species of Pu(IV), Pu(VI), Np(V), the Np(V)-U(VI) cation–cation complex, and Nd(III) in fuel solutions. This result justifies spectroscopic techniques as a promising methodology for monitoring spent fuel processing solutions in real-time. The fuel solution was quantitatively evaluated based on spectroscopic measurements and was compared to inductively coupled plasma-mass spectroscopy analysis and Oak Ridge Isotope Generator (ORIGEN)-based estimates.
科研通智能强力驱动
Strongly Powered by AbleSci AI