Spectroscopic Quantification of Target Species in a Complex Mixture Using Blind Source Separation and Partial Least-Squares Regression: A Case Study on Hanford Waste

Abstract

One of the challenges associated with multicomponent mixture analysis using chemometrics models is collecting calibration data. Depending upon the number of constituents, the size of the calibration set can be quite large. In some cases, the mixtures may contain numerous species, but only a small subset is central to the process for which quantification is being undertaken. For example, nuclear waste at the Hanford site contains a large number of radioactive and non-radioactive species, which complicates remediation efforts. However, only the concentrations of a few target species may need to be quantified in real time to facilitate operation of the cleanup process. In this paper, we introduce a preprocessing procedure that reduces the need for extensive model calibration. The preprocessing framework uses blind source separation (BSS) to identify the independent components in the mixture, which is followed by a correlation to classify them as either target species (part of the critical quality attributes that need to be measured during waste processing) or non-target species. The classification is used to preprocess the original mixture data: the signals of the target components are retained, while those of the non-target components are removed. Since the preprocessed spectra only contain the target components, the spectra-to-concentration regression model can be trained with a smaller calibration set. The approach is tested for Raman and infrared spectroscopy using simulated and experimental data sets based on simulant mixtures of nuclear waste.

An Analysis of the Historical Process of Cultural Confidence in Ideological and Political Education Based on Deep Learning

“…A typical feature of historical text data is that the dimension is high, and the higher the dimension is, the more di cult the classi cation will be in the later period. At present, there are ve methods to reduce the dimension of historical text data: canonical correlation analysis [13], partial least squares [14], hyperspectral learning [15], linear discriminant analysis [16], and shared subspace learning [17]. However, in the process of application, these methods will destroy the integrity of text to some extent.…”

Quantifying Dense Multicomponent Slurries with In-Line ATR-FTIR and Raman Spectroscopies: A Hanford Case Study

“…ATR-FTIR was used to quantify the concentration of dissolved molecular species in slurries comprising glass-forming chemicals in nuclear waste simulants. Based on the system studied, the most abundant (and therefore process-relevant) soluble species were quantified with ATR-FTIR: , NO 3 – , NO 2 – , CO 3 2– , and SO 4 3– . In addition, borate (B­(OH) 4 – ) was chosen to quantify in the solution phase based on boric acid having high solubility and the distinguishable FTIR peak intensity shown in Section .…”

“…Partial least squares regression (PLSR) , was chosen as the spectra-to-composition model in this work. PLSR has been used for the analysis of both nuclear waste solutions ,− and pharmaceutical slurries. ,, The scikit-learn package (version 1.0.2) implementation of PLSR was used for all quantifications in this work. An additional description of PLSR utilized in this work is in Section S5.…”

“…The system is based on the 5.6 M Na + low-activity waste (LAW) pretreatment system simulant (sodium salts) 27 combined with simulated glass-forming chemical (GFC) recipes composed predominantly of metal oxides and silicates. 10 Figure S1b shows a slurry obtained after the addition of GFCs with the simulants (900 g GFCs/kg water).…”