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Feasibility Study on the Use of On-line Multivariate Statistical Process Control for Safeguards Applications in Natural Uranium Conversion Plants

机译:在线多变量统计过程控制在天然铀转化工厂中作为保障措施的可行性研究

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The objective of this work was to determine the feasibility of using on-line multivariate statistical process control (MSPC) for safeguards applications in natural uranium conversion plants. Multivariate statistical process control is commonly used throughout industry for the detection of faults. For safeguards applications in uranium conversion plants, faults could include the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride. This study was limited to a 100 metric ton of uranium (MTU) per year natural uranium conversion plant (NUCP) using the wet solvent extraction method for the purification of uranium ore concentrate. A key component in the multivariate statistical methodology is the Principal Component Analysis (PCA) approach for the analysis of data, development of the base case model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix where the data matrix represents normal operation of the plant. Component mole balances were used to model each of the process units in the NUCP. However, this approach could be applied to any data set. The monitoring 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. This approach can be used to identify the key monitoring locations, as well as locations where monitoring is unimportant. Detection limits at the key monitoring locations can also be established using this technique. Several faulty scenarios were developed to test the monitoring framework after the base case or "normal operating conditions" of the PCA model were established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the stated objective.
机译:这项工作的目的是确定将在线多元统计过程控制(MSPC)用于天然铀转化工厂中的保障措施的可行性。多元统计过程控制在整个行业中通常用于检测故障。对于铀转化工厂的保障应用,故障可能包括中间产品的转移,例如二氧化铀,四氟化铀和六氟化铀。这项研究仅限于使用湿溶剂萃取法提纯铀精矿的每年100吨铀(MTU)的天然铀转化工厂(NUCP)。多元统计方法中的一个关键组成部分是主成分分析(PCA)方法,用于数据分析,基础案例模型的开发以及对未来运营的评估。 PCA方法是通过使用数据矩阵的奇异值分解来实现的,其中数据矩阵代表工厂的正常运行。组分摩尔天平用于在NUCP中对每个处理单元进行建模。但是,该方法可以应用于任何数据集。这项研究中开发的监控框架可用于确定NUCP是否作为国际原子能机构(IAEA)保障体系的一部分进行了材料转移。此方法可用于标识关键监视位置以及监视不重要的位置。也可以使用此技术建立关键监视位置的检测极限。在建立了PCA模型的基本情况或“正常操作条件”之后,开发了几种有故障的情况来测试监视框架。在所有情况下,监视框架都能够检测到故障。总体而言,这项研究成功地实现了既定目标。

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