Multi-Isotope Process Monitor Research Articles

Monitoring Aqueous Reprocessing Systems for Detection of Facility Misuse

Traditional approaches to material control and accounting at used nuclear fuel reprocessing facilities rely in large part on laboratory-based destructive assay of periodic samples taken from key stages. These techniques are highly accurate, but limited inspection frequency may limit the size and operation of a reprocessing facility. Online monitoring techniques using in-line detectors can be applied to fuel reprocessing systems to improve the situational awareness as the facility is operating to provide additional indicators of the evolving state of the facility between normal inspection periods, potentially alerting regulators to the need for additional inspection. The multi-isotope process (MIP) monitor combines medium-resolution gamma detectors with statistical data analysis techniques to monitor reprocessing systems for anomalous conditions resulting from process control issues or malfeasance. The MIP monitor is attractive for process and safeguards monitoring because of its unsupervised approach to detection and its inherent data obfuscation. This paper presents the application of the MIP monitor to detect a notional diversion scenario in an operating aqueous reprocessing facility by analyzing gamma spectra with a combination of principal component analysis (PCA) and the sequential probability ratio test. Gamma spectra collected from the H-Canyon reprocessing facility were analyzed to demonstrate the efficacy of the MIP Monitor for detecting notional diversion scenarios. The proposed analysis successfully detected small, protracted diversions, which would alert regulators of the need for additional inspection.

Multivariate analysis of gamma spectra to characterize used nuclear fuel

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. This approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.

Geant4 model validation of Compton suppressed system for process monitoring of spent fuel

Nuclear material accountancy is of continuous concern for the regulatory, safeguards, and verification communities. In particular, spent nuclear fuel reprocessing facilities pose one of the most difficult accountancy challenges: monitoring highly radioactive, fluid sample streams in near real-time. The Multi-Isotope Process monitor will allow for near-real-time indication of process alterations using passive gamma-ray detection coupled with multivariate analysis techniques to guard against potential material diversion or to enhance domestic process monitoring. The Compton continuum from the dominant 661.7 keV 137Cs fission product peak obscures lower energy lines which could be used for spectral and multivariate analysis. Compton suppression may be able to mitigate the challenges posed by the high continuum caused by scattering. A Monte Carlo simulation using the Geant4 toolkit is being developed to predict the expected suppressed spectrum from spent fuel samples to estimate the reduction in the Compton continuum. Despite the lack of timing information between decay events in the particle management of Geant4, encouraging results were recorded utilizing only the information within individual decays without accounting for accidental coincidences. The model has been validated with single and cascade decay emitters in two steps: as an unsuppressed system and with suppression activated. Results of the Geant4 model validation will be presented.

Advanced Process Monitoring Safeguards Technologies at Pacific Northwest National Laboratory

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.

Proof of concept experiments of the multi-isotope process monitor: An online, nondestructive, near real-time monitor for spent nuclear fuel reprocessing facilities

Operators, national regulatory agencies and the IAEA will require the development of advanced technologies to efficiently control and safeguard nuclear material at increasingly large-scale nuclear recycling facilities. Ideally, the envisioned technologies would be capable of non-destructive, near real-time (NRT), autonomous process monitoring. This paper describes results from proof-of-principle experiments designed to test the multi-isotope process (MIP) monitor, a novel approach to monitoring and safeguarding reprocessing facilities. The MIP Monitor combines the detection of intrinsic gamma ray signatures emitted from process solutions with multivariate analysis to detect off-normal conditions in process streams nondestructively and in NRT. Commercial spent nuclear fuel of various irradiation histories was dissolved and separated using a PUREX-based batch solvent extraction. Extractions were performed at various nitric acid concentrations to mimic both normal and off-normal industrial plant operating conditions. Principal component analysis (PCA) was applied to the simulated gamma spectra to investigate pattern variations as a function of acid concentration, burnup and cooling time. Partial least squares (PLS) regression was applied to attempt to quantify both the acid concentration and burnup of the dissolved spent fuel during the initial separation stage of recycle. The MIP Monitor demonstrated sensitivity to induced variations of acid concentration, including the distinction of ±1.3M variation from normal process conditions by way of PCA. Acid concentration was predicted using measurements from the organic extract and PLS resulting in predictions with <0.7M relative error. Quantification of burnup levels from dissolved fuel spectra using PLS was demonstrated to be within 2.5% of previously measured values.

Proof of concept simulations of the Multi-Isotope Process monitor: An online, nondestructive, near-real-time safeguards monitor for nuclear fuel reprocessing facilities

The International Atomic Energy Agency will require the development of advanced technologies to effectively safeguard nuclear material at increasingly large-scale nuclear recycling facilities. Ideally, the envisioned technologies would be capable of nondestructive, near-real-time, autonomous process monitoring. This paper describes recent results from model simulations designed to test the Multi-Isotope Process (MIP) monitor, a novel addition to a safeguards system for reprocessing facilities. The MIP monitor combines the detection of intrinsic gamma ray signatures emitted from process solutions with multivariate analysis to detect off-normal conditions in process streams nondestructively and in near-real-time. Three computer models including ORIGEN-ARP, AMUSE, and SYNTH were used in series to predict spent nuclear fuel composition, estimate element partitioning during separation, and simulate spectra from product and raffinate streams using a variety of gamma detectors, respectively. Simulations were generated for fuel with various irradiation histories and under a variety of plant operating conditions. Principal component analysis was applied to the simulated gamma spectra to investigate pattern variations as a function of acid concentration, burnup, and cooling time. Hierarchical cluster analysis and partial least squares (PLS) were also used in the analysis. The MIP monitor was found to be sensitive to induced variations of several operating parameters including distinguishing ±2.5% variation from normal process acid concentrations. The ability of PLS to predict burnup levels from simulated spectra was also demonstrated to be within 3.5% of measured values.