Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

Nee, K.; Bryan, S.; Levitskaia, T.; Kuo, J. W. -J.; Nilsson, M.

doi:10.1016/j.aca.2017.12.019

Title: Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

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Abstract

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of the spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO₃^-), total acid (H⁺), neodymium (Nd³⁺), sodium (Na⁺), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful butmore »« less

Authors:

Nee, K. ^[1]; Bryan, S. ^[2]; Levitskaia, T. ^[2]; Kuo, J. W. -J. ^[1];

^[3]

Univ. of California, Irvine, CA (United States). Dept. of Chemical Engineering and Materials Science
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Univ. of California, Irvine, CA (United States). Dept. of Chemical Engineering and Materials Science; Univ. of California, Irvine, CA (United States). Dept. of Chemistry

Publication Date:: Thu Dec 28 00:00:00 EST 2017

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1415776

Grant/Contract Number:: AC05-76RL01830; NA0000979

Resource Type:: Accepted Manuscript

Journal Name:: Analytica Chimica Acta

Additional Journal Information:: Journal Volume: 1006; Journal Issue: C; Journal ID: ISSN 0003-2670

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Near Infra-Red spectroscopy; Raman spectroscopy; Chemometric models; Partial Least Square (PLS) regression analysis; Principal Component Analysis (PCA)

Citation Formats


                    Nee, K., Bryan, S., Levitskaia, T., Kuo, J. W. -J., and Nilsson, M. Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models.  United States: N. p., 2017. 
Web.  doi:10.1016/j.aca.2017.12.019.

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                    Nee, K., Bryan, S., Levitskaia, T., Kuo, J. W. -J., & Nilsson, M. Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models.  United States.  https://doi.org/10.1016/j.aca.2017.12.019

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                    Nee, K., Bryan, S., Levitskaia, T., Kuo, J. W. -J., and Nilsson, M. Thu .  
"Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models".  United States.  https://doi.org/10.1016/j.aca.2017.12.019.  https://www.osti.gov/servlets/purl/1415776.

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@article{osti_1415776,

  title        = {Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models},

  author       = {Nee, K. and Bryan, S. and Levitskaia, T. and Kuo, J. W. -J. and Nilsson, M.},

  abstractNote = {The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of the spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO3-), total acid (H+), neodymium (Nd3+), sodium (Na+), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.},

  doi          = {10.1016/j.aca.2017.12.019},

  journal      = {Analytica Chimica Acta},

  number       = C,

  volume       = 1006,

  place        = {United States},

  year         = {Thu Dec 28 00:00:00 EST 2017},

  month        = {Thu Dec 28 00:00:00 EST 2017}

}

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