reprocessing facilities in the future, providing the ability to monitor these activities ... capable of monitoring process and waste streams at nuclear reprocessing ...
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Procedia Chemistry 7 (2012) 716 – 724
ATALANTE 2012 International Conference on Nuclear Chemistry for Sustainable Fuel Cycles
Advanced process monitoring safeguards technologies at Pacific Northwest National Laboratory J.M. Schwantes, S.A. Bryan, C.R. Orton, Tatiana G. Levitskaia, S.H. Pratt, C.G. Fraga, J.B. Coble Pacific Northwest National Laboratory, Richland, Washington, USA
Abstract There is a renewed interest worldwide to promote the use of nuclear power and close the nuclear fuel cycle. The long term successful use of nuclear power is critically dependent upon adequate and safe processing and disposition of the spent nuclear fuel. Liquid-liquid extraction is a separation technique commonly employed for the processing of the dissolved spent nuclear fuel. The instrumentation used to monitor these processes must be robust, require little or no maintenance, and be able to withstand harsh environments such as high radiation fields and aggressive chemical matrices. In addition, the ability for continuous on-line monitoring allows for numerous benefits. Researchers from Pacific Northwest National Laboratory (PNNL) are developing three non-destructive assay technologies designed to meet safeguards needs at reprocessing facilities in the future, providing the ability to monitor these activities autonomously, continuously, and in near-real-time (NRT). Raman and spectrophotometric techniques are being evaluated for on-line real-time monitoring of the U(VI)/nitrate ion/nitric acid and Pu(IV)/Np(V)/Nd(III), respectively. Both techniques demonstrated robust performance in the repetitive batch measurements of each analyte in a wide concentration range. Static spectroscopic measurements serve as training sets for the multivariate data analysis to obtain partial least squares predictive models. These models have been validated using on-line centrifugal contactor extraction tests. Another technology being developed is the Multi-Isotope Process (MIP) Monitor, which relies on the collection and NRT multivariate analysis of gamma spectra taken at discrete locations within a recycling facility. Using PCA (principal components analysis) or another multivariate technique, it is possible to automatically compare spectral patterns from the gamma-emitting constituents in process streams for statistically relevant signs of changes in the process chemistry or dissolved fuel characteristics in NRT. The MIP monitor is designed to identify small changes in the gamma ray spectra over time emanating from process streams that may indicate an unplanned change in process conditions. These “off-normal” conditions may be caused by incidental upsets within the system or may indicate something more serious, such as an intentional effort to divert special nuclear material for the system by altering process conditions. Initial simulations and experiments have illustrated the MIP monitor’s ability to detect and identify changes in process streams through gamma spectra. © 2012 2012 The by by Elsevier B.V.B.V. Selection and /or peer-review under responsibility of the Chairman of the © TheAuthors. Authors.Published Published Elsevier Selection and/or2012 peer-review under responsibility the Chairman of BY-NC-ND the ATALANTE 2012 Program ATALANTA Program Committee Openofaccess under CC license.
1876-6196 © 2012 The Authors. Published by Elsevier B.V. Selection and /or peer-review under responsibility of the Chairman of the ATALANTA 2012 Program Committee Open access under CC BY-NC-ND license. doi:10.1016/j.proche.2012.10.109
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1. Introduction Pacific Northwest National Laboratory (PNNL) is currently developing and demonstrating two technologies capable of monitoring process and waste streams at nuclear reprocessing plants online, non-destructively, and in near-real-time (NRT). These technologies include the optical spectroscopy-based monitor [1-5], which utilizes ultraviolet-visible-near infrared (UV-vis-NIR) and Raman spectroscopy, and the Multi-Isotope Process (MIP) monitor [6-11]. Raman [3, 12-14] and UV-vis-NIR [3, 14-20] spectroscopies are analytical techniques that have extensively been used for measuring concentrations of various organic and inorganic compounds, including actinides [3, 14, 21]. In addition, measuring dielectric properties has also been proposed for on-line monitoring of fuel reprocessing systems [19]. The feasibility of on-line control of nuclear fuel reprocessing streams by using analytical techniques has been investigated since the 1970s [22]. Here, fiber-optic multiplexer systems in conjunction with spectroscopic equipment are implemented for the on-line process monitoring of centrifugal contactor systems. To test the on-line and real-time aspects of process monitoring, we attached the opticalmultiplexer system to the counter-current solvent extraction testing apparatus for use with separations processing. This system was tested with the TBP/dodecane solvent extraction system to characterize the mass balance being achieved during the two-phase extraction of Nd(III) in the PUREX solvent system in NRT. The MIP Monitor is designed to track plant conditions indirectly and non-destructively by monitoring numerous gamma-emitting radioactive constituents within process streams. The behavior of many fission and activation products within a reprocessing facility will be dictated by a number of operational variables including (but not limited to), acid concentration, TBP concentration, temperature, burnup, cooling time. In these bi-phase systems, small amounts of both product and contaminants remain in the other phase. The distribution of each element between the organic and aqueous phases is determined by major process variables such as acid concentration, organic ligand concentration, organic Extract to aqueous Feed phase volume ratio (E/F ratio), reduction potential, and temperature. Hence for a consistent industrial process (i.e., steady-state and reproducible conditions), the distribution of each element between the organic and aqueous phases should be relatively constant. Consequently, the pattern of elements distributing into product and waste streams at each stage in the facility should be relatively reproducible (within normal industrial variations) resulting in a signature indicative of “normal” process conditions. Under abnormal conditions (such as those expected under some protracted diversion scenarios), patterns of elements within the various streams would be expected to change measurably. The MIP monitoring approach capitalizes on these expected changes in the distribution patterns of gammaemitting elements in the aqueous and organic phases to serve as an indication of “off-normal” conditions in the process chemistry. The MIP monitor is designed to track numerous radioactive constituents within process streams to indirectly monitor conditions in that stream. The Monitor relies on small, medium-resolution gamma detectors that do not require cryogenic cooling and that can be easily placed throughout an existing facility. Multivariate analysis is used to autonomously process the spectra from these detectors in NRT, continuously monitoring for indications in changing stream conditions. Initial simulations and experiments have documented the performance and potential of the MIP Monitor [8-11, 23-26]. However, experimental investigations to date have been limited to only batch experimental efforts. Here we present the first experiments to test the MIP Monitor performance under flowing conditions. 2. Materials and Methods 2.1. Experimental Setup for Optical Spectroscopy Experiments A bank of contactors was instrumented with an optical-multiplexer allowing for multiple sample measurement locations for both the Raman and UV-vis-NIR spectroscopies. The counter-current design is based on multiple
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banks of 2-cm centrifugal contactors. One bank of four contactors was installed in the cold test system for mockup testing. Figure 1 shows a diagram of the setup used for this experiment. More details of the experimental system are provided by Bryan et al. (In Press).
Figure 1. A) Photograph of centrifugal contactor system instrumented with fiber-optic cables used for connecting vis-NIR and Raman spectrometer probes to spectroscopic instrumentation. Spectroscopic instrumentation is located outside of field of view. B) Schematic of Centrifugal Contactor System. The location of Feed, Raffinate, Organic inlet, and loaded Organic product are shown. The vis-NIR and Raman monitoring probes are positioned on the inlet and outlet of each stream.
2.2. Experimental Setup for Dynamic Testing of the MIP Monitor System A bench-scale flow loop apparatus was developed to test the MIP Monitor capability to categorize classes of different fuel types that may be processed at a single reprocessing facility over short periods of time. The apparatus consisted of a multi-head Gilson Minipuls3 pump and a series of capillary tubes affixed to the front of a Cerium doped, 5.2cm x 5.2cm LaBr3(Ce) (Saint Gobaine-Brilliance 380) detector. Each capillary tube runs to and from a reservoir containing a particular spent nuclear fuel. This allows us to simulate the gamma ray
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environment of mixtures of different spent nuclear fuels without actually having to mix or even open these solutions. A desired volume of liquid from each type of fuel is simply pumped into the capillary tubes and counted. Three well characterized dissolved spent nuclear fuels were used during this experiment including ATM 105a, ATM 105p, and ATM 109. Table 1 summarizes key characteristics about these fuels. Details about the dissolution process used for each have been described elsewhere. Table 1. Characteristics of spent nuclear fuel used for dynamic testing of the MIP Monitor (adapted from [27]). Fuel ID
Type, fuel manufacturer, reactor
Cooling time (years) as of 09/2011
Distance from top (cm)
Approx. Burnup (MWd/kgU)
105a
BWR, GE, Cooper (Nebraska)
27.6
142-146
15.5-17.5
105p
BWR, GE, Cooper (Nebraska)
27.6
52-56
29-31
109
BWR, GE, Quad Cities I
19
n/a
67-70
Initially, static spectra for the system containing only one fuel type were collected as calibration data. In this case, 300s spectra were collected for both one- and two-full channels of a single fuel type for each of the three fuels; these spectra were collected in triplicate. Additionally, two 300s spectra were collected for each mixture of fuel types wherein each channel was full of a different fuel, resulting in duplicate spectra for ATM 105a and ATM 105p, ATM 105a and ATM 109, and ATM 105p and ATM 109. Dynamic spectra were collected using the bench-top flow loop under the following conditions: first a 300s spectrum was collected with a single channel full of one fuel type. Then, a series of 30s count were initiated to begin with the filling of a second channel with a different dissolved fuel. A total of six to fourteen 30s spectra were collected for each run. Once the second channel was completely filled, a 300s spectrum was recorded. 3. Results and Discussion 3.1. Optical Monitoring of Nd and NO3- species during continuous flow PUREX separations We have recently determined the applicability of the quantitative determination of neodymium in fuel feed reprocessing solutions within a PUREX solvent system [3, 28]. For Nd3+, the vis-NIR spectra have multiple bands suitable for use in this regard. These spectra were used to construct a database for use in interpreting the concentrations of new spectra measured during a contactor run with variable concentrations of Nd(NO3)3 in solution. Data analysis of vis-NIR and Raman spectra for quantitative determination of Nd3+ (from vis-NIR) and NO3- (Raman) were performed using a commercial software package (MATLAB®). The aqueous feed solution used for the contactor run contained 0, 10, or 20 mM Nd(NO3)3 and 4.0 M NaNO3 in 0.1 M HNO3. The organic solvent used in this demonstration consisted of 30 vol% TBP in n-dodecane. The centrifugal contactor system test was initially started with an aqueous feed composed of 0 mM Nd(NO3)3 in 4M NaNO3/0.1M HNO3, and an organic phase at flow rates of approximately 12 and 8 g/min respectively. The centrifugal contactor system was allowed to completely fill with the feed and organic solvent. At this time, solutions started to flow from the raffinate and organic product stream outlets. When the aqueous feed/raffinate and organic solvent/product flows reached steady state (i.e., the same volumes flowing in and out of the system), the test was started. After approximately 70 min of stable flow through the system, the feed solution was switched from 0 mM Nd to a solution containing 10 mM Nd(NO3)3 in 4M NaNO3/0.1M HNO3. This was allowed to flow at the same flow rate (12 g/min) until a run time of approximately 130 min, whereupon the solution was switched back to 0 mM Nd(NO3)3 in 4M NaNO3/0.1M HNO3 for the remainder of the test.
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Spectroscopic monitoring of the feed, raffinate, solvent, and organic product steams was recorded during the entire flow test. Chemometric analysis was performed on the spectroscopic data from the feed, product, raffinate, and solvent streams, and the results are shown in Figure 2. From this figure, the feed solution shows an increase in concentration at approximately 70 min (run time) when the 10 mM Nd feed was introduced. A short time later (at approximately 80 min run time) the organic product and raffinate showed an increase in Nd concentration as well. At approximately 130 min run time, the feed solution was switched back to 0 mM Nd, and consequently, the concentration of Nd measured in the feed, organic product, and raffinate slowly returned to near zero. Over the entire experimental run, the solvent (inlet) concentration was determined to be approximately zero in Nd, as expected. Coupling the mass flow information with the concentration data, a total integrated inventory of the Nd in the system can be performed. Figure 3 contains the plot of the integrated Nd (in mmole) for all four streams, feed, product, raffinate, and solvent. The difference between the inlet streams(feed + solvent) and the outlet streams (product + raffinate) is also shown in this figure. At the termination of the experiment (~ 190 min run time), the difference is approximately zero, indicating a mass balance for this experiment was achieved. This shows that the total mass of Nd(III) initially introduced into the feed, was successfully monitoring and quantitatively measured, as it was partially partitioned (extracted) from the aqueous feed into the organic solvent.
Figure 2. Concentration of Nd3+ Measured using the Spectroscopic Data from the Feed, Product, Raffinate, and Solvent streams
Figure 3. Integrated Amount of Nd3+ Measured using a Combination of Spectroscopic and Mass Flow Data from the Feed, Product, Raffinate, and Solvent streams.
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3.2. Dynamic Testing of the MIP Monitor To accommodate the available data, two different analyses were performed. The first analysis investigated classification of the spectra collected on samples with two full channels of fuel. The second analysis uses the available single-channel spectra and the two-channel, two-fuel spectra to build a model for dynamic fuel classification. The analysis and results are described. In both analyses, the data are normalized to the total number of counts in each spectra, and then the channels are mean-centered prior to performing PCA. This removes the amplitude of each energy bin as a source of variance from the origin without losing relative information about how the energy bin response varies across fuels. The proposed approach to performing dynamic fuel-type classification is to perform PCA on the spectra of two-channel single-fuel samples. New spectra observations resulting from fuel mixtures can be transformed to the principal component (PC) space and compared to the established single-fuel clusters. In this way, we can map how spectra move between fuel samples as fuel mixing occurs. This analysis could not be performed on all of the available data, but it was performed for a subset of appropriate data. The "normal" single-fuel spectra PC model was built using six available two-channel spectra, two for each fuel type. Based on these six spectra, the three fuel types can be differentiated by the score on the first PC. Figure 4 shows the results of PCA on the available two-channel spectra, indicating the normal scores for the individual fuel spectra. The score of a mixture spectrum resulting from fuels ATM 105p and ATM 105a is also shown, indicated by a black dot. As expected, the mixture spectrum lies between the two component spectra. To perform dynamic spectra classification, a new PCA model was developed. In this analysis, a classification model was built using six possible fuel classifications: 105a, 105p, 109, 105a+105p, 105a+109, and 105p+109. The three single-fuel classes are built on spectra collected from a single full channel of fuel; the three two-fuel classes are built on spectra collected from two-channel samples, with one full channel of each component fuel type. As new spectra are collected, they are transformed to the PC-space and compared to the existing classification map. Figure 5 shows one example of this type of classification, for a system moving from 105p fuel to 109 fuel indicated by connected black dots. Additional runs were evaluated for moving between all combinations of two fuel types, with similar results showing that fuel classification is possible using a PCAbased classification approach.
Figure 4. Principal component scores of a fuel mixture spectrum lies between the scores of the two individual fuel components.
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While the use of the bench-scale test loop proved adequate for dynamically testing the MIP Monitor, this experimental design did lead to a few drawbacks for multivariate analysis. Specifically, two problems inhibit the classification analysis: (1) a mismatch in counting time; and (2) a mismatch in the amount of fuel being counted at any time. The first problem is straightforward to address, since all collected spectra are normalized by the total number of counts in the spectra, resulting in normalized spectra with a total area of one. This maintains the overall shape of the spectra while eliminating the differing effects of amplitude introduced by the different counting times. Unfortunately, the second problem cannot be addressed with simple data processing. There is no easy way to artificially transform a spectrum collected from a sample of mass m to simulate one expected from a sample of mass 2m. However, developing a test system that failed to maintain constant volume was somewhat serendipitous since it will be difficult to deploy a system that is devoid of bubbles and perfectly maintains constant volume. 4. Conclusion The NRT spectroscopic and flow monitoring of the fuel simulant extraction system for the feed, raffinate, solvent (inlet) and organic product (outlet) streams were independently and simultaneously monitored, within a counter-current solvent extraction process. Using the on-line spectroscopic process monitoring technique, we have successfully demonstrated mass balance being maintained during the two-phase extraction of Nd(NO3)3 in the PUREX solvent system during a three-hour extraction run. This technique has application for process monitoring of commercial nuclear fuel reprocessing schemes. We have also successfully demonstrated the ability of the MIP monitoring approach to classify dissolved spent nuclear fuel within a continuous flow system containing two fuels at various mixing ratios changing over time. Two multi-variate approaches were developed: one simple case when constant volume can be assumed and one complex case when this assumption is not valid.
Figure 5. PCA classification map for six-class data and overlaid (shown with black symbols and dashed line) PC classification of dynamic fuel data moving from atm 105p to atm 109.
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Acknowledgements This research is sponsored by the U.S. Department of Energy’s Next Generation Safeguards Initiative (NGSI), Office of Nonproliferation and International Security (NIS), National Nuclear Security Administration (NNSA) and Fuel Cycle Research and Development (NE). We would also like to acknowledge Analytical Support Operations group at PNNL and in particular Larry Greenwood and Truc Trang-Le’s efforts in collecting the gamma spectra and performing the traditional gamma analysis. We also acknowledge Lisa Staudinger and Duane Balvage for their help with technical editing and graphics, respectively. This research was conducted by Pacific Northwest National Laboratory under DOE contract number DE-AC06-76RLO-1830..
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