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Sadergaski LR, Andrews HB, Wilson BA. Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel. Anal Chem 2024; 96:1759-1766. [PMID: 38227702 DOI: 10.1021/acs.analchem.3c04911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0-100 μg mL-1), europium (0-150 μg mL-1), samarium (0-250 μg mL-1), praseodymium (0-350 μg mL-1), neodymium (0-1000 μg mL-1), and HNO3 (2-4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet-visible-near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9-5.7% for the seven factors. The optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.
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Affiliation(s)
- Luke R Sadergaski
- Radioisotope Science and Technology Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37831, United States
| | - Hunter B Andrews
- Radioisotope Science and Technology Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37831, United States
| | - Brandon A Wilson
- Nuclear Energy and Fuel Cycle Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37831, United States
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2
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Andrews HB, Sadergaski LR. Leveraging visible and near-infrared spectroelectrochemistry to calibrate a robust model for Vanadium(IV/V) in varying nitric acid and temperature levels. Talanta 2023; 259:124554. [PMID: 37080075 DOI: 10.1016/j.talanta.2023.124554] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/10/2023] [Accepted: 04/11/2023] [Indexed: 04/22/2023]
Abstract
Spectroelectrochemistry and optimal design of experiments can be used to rapidly build accurate models for species quantification and enable a greater level of process awareness. Optical spectroscopy can provide vital elemental and molecular information, but several hurdles must be overcome before it can become a widely adopted analytical method for remote analysis in the nuclear field. Analytes with varying oxidation state, acid concentration, and fluctuating temperature must be efficiently accounted for to minimize time and resources in restrictive hot cell environments. The classic one-factor-at-a-time approach is not suitable for frequent calibration/maintenance operations in this setting. Therefore, a novel alternative was developed to characterize a system containing vanadium(IV/V) (0.01-0.1 M), nitric acid (0.1-4 M), and varying temperatures (20-45 °C). Spectroelectrochemistry methods were used to acquire a sample set selected by optimal design of experiments. This new approach allows for the accurate analysis of vanadium and HNO3 concentration by leveraging UV-Vis-NIR absorption spectroscopy with robust and accurate chemometric models. The top model's root mean squared error of prediction percent values were 3.47%, 4.06%, 3.40%, and 10.9% for V(IV), V(V), HNO3, and temperature, respectively. These models, efficiently developed using the designed approach, exhibited strong predictive accuracy for vanadium and acid with varying oxidation states and temperature using only spectrophotometry, which advances current technology for real-world hot cell applications. Additionally, Nernstian analysis of the V(IV/V) standard potential was performed using traditional absorbance methods and multivariate curve resolution (MCR). The successful tests demonstrated that MCR Nernst tests may be valuable in highly convoluted spectral systems to better understand the redox processes' behavior.
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Affiliation(s)
- Hunter B Andrews
- Radioisotope Science and Technology Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd., Oak Ridge, TN, 37980, USA.
| | - Luke R Sadergaski
- Radioisotope Science and Technology Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd., Oak Ridge, TN, 37980, USA
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3
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Sadergaski LR, Irvine SB, Andrews HB. Partial Least Squares, Experimental Design, and Near-Infrared Spectrophotometry for the Remote Quantification of Nitric Acid Concentration and Temperature. Molecules 2023; 28:molecules28073224. [PMID: 37049987 PMCID: PMC10096128 DOI: 10.3390/molecules28073224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/22/2023] [Accepted: 04/02/2023] [Indexed: 04/14/2023] Open
Abstract
Near-infrared spectrophotometry and partial least squares regression (PLSR) were evaluated to create a pleasantly simple yet effective approach for measuring HNO3 concentration with varying temperature levels. A training set, which covered HNO3 concentrations (0.1-8 M) and temperature (10-40 °C), was selected using a D-optimal design to minimize the number of samples required in the calibration set for PLSR analysis. The top D-optimal-selected PLSR models had root mean squared error of prediction values of 1.4% for HNO3 and 4.0% for temperature. The PLSR models built from spectra collected on static samples were validated against flow tests including HNO3 concentration and temperature gradients to test abnormal conditions (e.g., bubbles) and the model performance between sample points in the factor space. Based on cross-validation and prediction modeling statistics, the designed near-infrared absorption approach can provide remote, quantitative analysis of HNO3 concentration and temperature for production-oriented applications in facilities where laser safety challenges would inhibit the implementation of other optical techniques (e.g., Raman spectroscopy) and in which space, time, and/or resources are constrained. The experimental design approach effectively minimized the number of samples in the training set and maintained or improved PLSR model performance, which makes the described chemometric approach more amenable to nuclear field applications.
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Affiliation(s)
- Luke R Sadergaski
- Radioisotope Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Sawyer B Irvine
- Isotope Processing and Manufacturing Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Hunter B Andrews
- Radioisotope Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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4
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Panchuk V, Petrov Y, Semenov V, Kirsanov D. Quantification of elements in spent nuclear fuel using intrinsic radioactivity for sample excitation and chemometric data processing. Anal Chim Acta 2023; 1239:340694. [PMID: 36628762 DOI: 10.1016/j.aca.2022.340694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/18/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022]
Abstract
Quantitative analysis of spent nuclear fuel (SNF) is a very challenging task. High radioactivity, complex chemical composition and personnel safety requirements severely limit the number of analytical tools suitable for this problem. There is an urgent need for the methods that would provide for remote on-line quantification of elements in spent nuclear fuel and its reprocessing technological solutions. Here we propose a novel approach based on the registration of X-ray fluorescence radiation from SNF samples induced by fission products radioactivity. In this case the X-ray excitation conditions will obviously vary from sample to sample; moreover the resulting spectra will be a complex superposition of numerous signals from soft gamma emitters and X-ray fluorescence of various nature. These complex spectra can be effectively treated with chemometric data processing for quantification of particular elements. We have demonstrated the validity of this approach for direct analysis of U, Zr and Mo in SNF raffinate.
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Affiliation(s)
- Vitaly Panchuk
- Institute of Chemistry, St. Petersburg University, St. Petersburg, Russia; Institute for Analytical Instrumentation RAS, St. Petersburg, Russia
| | - Yuriy Petrov
- Khlopin Radium Institute, St. Petersburg, Russia
| | - Valentin Semenov
- Institute of Chemistry, St. Petersburg University, St. Petersburg, Russia; Institute for Analytical Instrumentation RAS, St. Petersburg, Russia
| | - Dmitry Kirsanov
- Institute of Chemistry, St. Petersburg University, St. Petersburg, Russia.
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5
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Andrews H, Sadergaski LR, Cary SK. Pursuit of the Ultimate Regression Model for Samarium(III), Europium(III), and LiCl Using Laser-Induced Fluorescence, Design of Experiments, and a Genetic Algorithm for Feature Selection. ACS OMEGA 2023; 8:2281-2290. [PMID: 36687031 PMCID: PMC9850777 DOI: 10.1021/acsomega.2c06610] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Laser-induced fluorescence spectroscopy, Raman scattering, and partial least squares regression models were optimized for the quantification of samarium (0-150 μg mL-1), europium (0-75 μg mL-1), and lithium chloride (0.1-12 M) with a transformational preprocessing strategy. Selecting combinations of preprocessing methods to optimize the prediction performance of regression models is frequently a major bottleneck for chemometric analysis. Here, we propose an optimization tool using an innovative combination of optimal experimental designs for selecting preprocessing transformation and a genetic algorithm (GA) for feature selection. A D-optimal design containing 26 samples (i.e., combinations of preprocessing strategies) and a user-defined design (576 samples) did not statistically lower the root mean square error of the prediction (RMSEP). The greatest improvement in prediction performance was achieved when a GA was used for feature selection. This feature selection greatly lowered RMSEP statistics by an average of 53%, resulting in the top models with percent RMSEP values of 0.91, 3.5, and 2.1% for Sm(III), Eu(III), and LiCl, respectively. These results indicate that preprocessing corrections (e.g., scatter, scaling, noise, and baseline) alone cannot realize the optimal regression model; feature selection is a more crucial aspect to consider. This unique approach provides a powerful tool for approaching the true optimum prediction performance and can be applied to numerous fields of spectroscopy and chemometrics to rapidly construct models.
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Sadergaski LR, Myhre KG, Delmau LH. Multivariate chemometric methods and Vis-NIR spectrophotometry for monitoring plutonium-238 anion exchange column effluent in a radiochemical hot cell. TALANTA OPEN 2022. [DOI: 10.1016/j.talo.2022.100120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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7
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Sadergaski LR, Hager TJ, Andrews HB. Design of Experiments, Chemometrics, and Raman Spectroscopy for the Quantification of Hydroxylammonium, Nitrate, and Nitric Acid. ACS OMEGA 2022; 7:7287-7296. [PMID: 35252718 PMCID: PMC8892473 DOI: 10.1021/acsomega.1c07111] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 02/02/2022] [Indexed: 05/05/2023]
Abstract
Selecting optimal combinations of preprocessing methods is a major holdup for chemometric analysis. The analyst decides which method(s) to apply to the data, frequently by highly subjective or inefficient means, such as user experience or trial and error. Here, we present a user-friendly method using optimal experimental designs for selecting preprocessing transformations. We applied this strategy to optimize partial least square regression (PLSR) analysis of Stokes Raman spectra to quantify hydroxylammonium (0-0.5 M), nitric acid (0-1 M), and total nitrate (0-1.5 M) concentrations. The best PLSR model chosen by a determinant (D)-optimal design comprising 26 samples (i.e., combinations of preprocessing methods) was compared with PLSR models built with no preprocessing, a user-selected preprocessing method (i.e., trial and error), and a user-defined design strategy (576 samples). The D-optimal selection strategy improved PLSR prediction performance by more than 50% compared with the raw data and reduced the number of combinations by more than 95.5%.
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Affiliation(s)
- Luke R. Sadergaski
- Oak
Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37830, United States
- . Telephone: +1 (865) 574-1167
| | - Travis J. Hager
- Department
of Chemistry, University of Missouri, 125 Chemistry Building Columbia, Missouri 65211, United States
| | - Hunter B. Andrews
- Oak
Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37830, United States
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8
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Nonlinear Multivariate Regression Algorithms for Improving Precision of Multisensor Potentiometry in Analysis of Spent Nuclear Fuel Reprocessing Solutions. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10030090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Potentiometric multisensor systems were shown to be very promising tools for the quantification of numerous analytes in complex radioactive samples deriving from spent nuclear fuel reprocessing. Traditional multivariate calibration for these multisensor systems is performed with partial least squares regression—an intrinsically linear regression method that can provide suboptimal results when handling potentiometric signals from very complex multi-component samples. In this work, a thorough investigation was performed on the performance of a multisensor system in combination with non-linear multivariate regression models for the quantification of analytes in the PUREX (Plutonium–URanium EXtraction) process. The multisensor system was composed of 17 cross-sensitive potentiometric sensors with plasticized polymeric membranes containing different lipophilic ligands capable of heavy metals, lanthanides, and actinides binding. Regression algorithms such as support vector machines (SVM), random forest (RF), and kernel-regularized least squares (KRLS) were tested and compared to the traditional partial least squares (PLS) method in the simultaneous quantification of the following elements in aqueous phase samples of the PUREX process: U, La, Ce, Sm, Zr, Mo, Zn, Ru, Fe, Ca, Am, and Cm. It was shown that non-linear methods outperformed PLS for most of the analytes.
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9
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Using commercial calcium ionophores to make lanthanide sensors. J Radioanal Nucl Chem 2022. [DOI: 10.1007/s10967-022-08220-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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10
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Plutonium(IV) quantification in acidic process solutions using partial least-squares regression applied to UV–Vis spectrophotometry. J Radioanal Nucl Chem 2022. [DOI: 10.1007/s10967-022-08205-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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11
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Sadergaski LR, Andrews HB. Simultaneous quantification of uranium( vi), samarium, nitric acid, and temperature with combined ensemble learning, laser fluorescence, and Raman scattering for real-time monitoring. Analyst 2022; 147:4014-4025. [DOI: 10.1039/d2an00998f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Laser-induced fluorescence spectroscopy, Raman spectroscopy, and a stacked regression was developed for rapid quantification of uranium(vi) (1–100 μg mL−1), samarium (0–200 μg mL−1) and nitric acid (0.1–4 M) with varying temperature (20 °C–45 °C).
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Affiliation(s)
- Luke R. Sadergaski
- Radioisotope Science and Technology Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37830, USA
| | - Hunter B. Andrews
- Radioisotope Science and Technology Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37830, USA
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12
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Development of a remote aliquoting system and a remote titration method for analysis of fast reactor fuel reprocessing plant samples inside a hot cell. J Radioanal Nucl Chem 2021. [DOI: 10.1007/s10967-021-08029-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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13
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Sadergaski LR, Toney GK, Delmau LH, Myhre KG. Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis. APPLIED SPECTROSCOPY 2021; 75:1155-1167. [PMID: 33393348 DOI: 10.1177/0003702820987281] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Implementing remote, real-time spectroscopic monitoring of radiochemical processing streams in hot cell environments requires efficiency and simplicity. The success of optical spectroscopy for the quantification of species in chemical systems highly depends on representative training sets and suitable validation sets. Selecting a training set (i.e., calibration standards) to build multivariate regression models is both time- and resource-consuming using standard one-factor-at-a-time approaches. This study describes the use of experimental design to generate spectral training sets and a validation set for the quantification of sodium nitrate (0-1 M) and nitric acid (0.1-10 M) using the near-infrared water band centered at 1440 nm. Partial least squares regression models were built from training sets generated by both D- and I-optimal experimental designs and a one-factor-at-a-time approach. The prediction performance of each model was evaluated by comparing the bias and standard error of prediction for statistical significance. D- and I-optimal designs reduced the number of samples required to build regression models compared with one-factor-at-a-time while also improving performance. Models must be confirmed against a validation sample set when minimizing the number of samples in the training set. The D-optimal design performed the best when considering both performance and efficiency by improving predictive capability and reducing number of samples in the training set by 64% compared with the one-factor-at-a-time approach. The experimental design approach objectively selects calibration and validation spectral data sets based on statistical criterion to optimize performance and minimize resources.
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Affiliation(s)
- Luke R Sadergaski
- Radioisotope Science and Technology Division, 6146Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Gretchen K Toney
- Radioisotope Science and Technology Division, 6146Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Laetitia H Delmau
- Radioisotope Science and Technology Division, 6146Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Kristian G Myhre
- Radioisotope Science and Technology Division, 6146Oak Ridge National Laboratory, Oak Ridge, TN, USA
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14
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Plutonium assay by spectrophotometry and estimation of uncertainties on routine glove box samples. J Radioanal Nucl Chem 2020. [DOI: 10.1007/s10967-020-07369-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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15
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Review of on-line and near real-time spectroscopic monitoring of processes relevant to nuclear material management. Anal Chim Acta 2020; 1107:1-13. [PMID: 32200882 DOI: 10.1016/j.aca.2020.02.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 02/01/2020] [Accepted: 02/03/2020] [Indexed: 11/21/2022]
Abstract
Spectroscopic chemometric based on-line monitoring of used nuclear fuel (UNF) reprocessing solutions and characterization of legacy nuclear waste (LNW) stored at Hanford is discussed in this manuscript. Utilizing on-line and near real-time monitoring, as opposed to traditional off-line monitoring, can significantly reduce the cost, risk and improve the efficiency of characterizing UNF and LNW processing streams. Specifically, this manuscript will highlight the benefits of spectroscopy-based monitoring approaches, which generally include the ability to collect data non-destructively. Furthermore, significant literature precedence supports the use of various real-time analysis methods, including chemometric analysis, that enable near-instantaneous conversion of spectroscopic data into information useable by process operators. This approach can accurately quantify and qualify nuclear material in near-real time enabling immediate condition characterization and potential diversion detection within UNF reprocessing streams and LNW. The ability to be applied in a real reprocessing plant and in an actual Hanford waste tank/transfer pipe has been demonstrated by applying this technique to accurately quantify analytes in real UNF streams and LNW samples. The future development of spectroscopy-based on-line monitoring is also discussed in this manuscript.
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16
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Quantification of thorium and uranium in real process streams of Mayak radiochemical plant using potentiometric multisensor array. J Radioanal Nucl Chem 2020. [DOI: 10.1007/s10967-019-06941-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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17
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Lines AM, Tse P, Felmy HM, Wilson JM, Shafer J, Denslow KM, Still AN, King C, Bryan SA. Online, Real-Time Analysis of Highly Complex Processing Streams: Quantification of Analytes in Hanford Tank Sample. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03636] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- A. M. Lines
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - P. Tse
- Department of Chemistry, Colorado School of Mines, Golden, Colorado 80401, United States
| | - H. M. Felmy
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - J. M. Wilson
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - J. Shafer
- Department of Chemistry, Colorado School of Mines, Golden, Colorado 80401, United States
| | - K. M. Denslow
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - A. N. Still
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - C. King
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - S. A. Bryan
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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18
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Akkuşlu Y, Sarıöz Ö. The Synthesis and Investigation of Liquid-Liquid Extraction Capability of N-Diphenylphosphino-N-ethylaniline and Its Chalcogenide Derivatives. RUSS J APPL CHEM+ 2019. [DOI: 10.1134/s1070427219060119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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19
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Rodríguez-Ruiz I, Lamadie F, Charton S. Uranium(VI) On-Chip Microliter Concentration Measurements in a Highly Extended UV-Visible Absorbance Linearity Range. Anal Chem 2018; 90:2456-2460. [PMID: 29327582 DOI: 10.1021/acs.analchem.7b05162] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The reduction of effluents deriving from analytical control is a serious concern in the nuclear industry, for both production and R&D units. In this work we report an alternative methodology for the standard UV-vis absorbance analyses for actinides concentration monitoring along the plutonium uranium refining extraction (PUREX) process. This methodology, based on photonic lab-on-a-chip (PhLoC) technology, enables drastic sampling reduction down to a few microliters and simultaneously allows to track concentrations over several orders of magnitude while maintaining a detection linearity range. A PhLoC microfluidic platform was specifically designed to allow online sample injection with zero dead volume connectivity and the on-chip spectrophotometric approach, based on a multiple optical path configuration, was tested for the determination of uranium(VI) concentrations from 0.1 to 200 g L-1, showing that linearity is maintained within high levels of confidence. These results provide the proof of concept for the transposition of current analytical methods for actinides, including plutonium, to microfluidic systems.
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Affiliation(s)
- Isaac Rodríguez-Ruiz
- CEA, DEN , Research Department on Mining and Fuel Recycling Processes, SA2I, 30207 Bagnols-sur-Cèze, France
| | - Fabrice Lamadie
- CEA, DEN , Research Department on Mining and Fuel Recycling Processes, SA2I, 30207 Bagnols-sur-Cèze, France
| | - Sophie Charton
- CEA, DEN , Research Department on Mining and Fuel Recycling Processes, SA2I, 30207 Bagnols-sur-Cèze, France
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