Year
2014
Abstract
The objective of this work wa s 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 ura nium conversion plants, faults c ould include the diversion of intermediate products such as uranium dioxide, uran ium 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 compone nt in the multivariate statistical methodology i s 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 singula r 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 monitori ng 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 w ere established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the state d objective.