Nonlinear Multivariate Regression Algorithms for Improving Precision of Multisensor Potentiometry in Analysis of Spent Nuclear Fuel Reprocessing Solutions

Nadan Kravić,Dmitry Kirsanov,Andrey Legin,Vasily Babain,Julia Savosina,Marina Agafonova-Moroz

doi:10.3390/chemosensors10030090

Nadan Kravić, Dmitry Kirsanov + Show 4 more

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https://doi.org/10.3390/chemosensors10030090

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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.

Highlights

Chemical monitoring of spent nuclear fuel reprocessing poses a challenging analytical task for a variety of reasons
The final products of interest are uranyl nitrate, uranium, and plutonium oxide, and these can be reused as nuclear fuel [3]
The response of cross-sensitive potentiometric sensors in multi-component samples can have a very complex character, and non-linear regression algorithms can be better suited for making quantitative predictive models

Summary

Introduction

Chemical monitoring of spent nuclear fuel reprocessing poses a challenging analytical task for a variety of reasons To this day, the PUREX (Plutonium–URanium EXtraction) process remains the most common industrial method of SNF (spent nuclear fuel) reprocessing [1,2]. Current analytical control of PUREX technological media is based on spectroscopic methods such as ICP-MS and ICP-AES (inductively coupled plasma mass spectrometry and atomic emission spectrometry) [4,5,6]. These methods have the necessary qualities for performing this type of analysis, where low detection limits, high accuracy, and good reproducibility are the most important. The realization of these methods is demanding work that requires special working conditions and difficult sampling, which

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