Abstract
Ba-based ion interference with Eu in coal and coal combustion products during quadrupole-based inductively coupled plasma mass spectrometry procedures is problematic. Thus, this paper proposes machine-learning-based prediction models for determination of the threshold value of Ba interference with Eu, which can be used to predict such interference in coal. The models are trained for Eu, Ba, Ba/Eu, and Ba interference with Eu. Under different user-defined parameters, different prediction models based on the corresponding model tree can be applied to Ba interference with Eu. We experimentally show the effectiveness of these different prediction models and find that, when the Ba/Eu value is less than 2950, the Ba-Eu interference prediction model is y=–0.18419411+0.00050737×x, 0<x<2950. Further, when the Ba/Eu value is between 2950 and 189,523, the Ba-Eu interference prediction model of y = 0.293982186 + 0.00000181729975 × x, 2950 < x < 189,523 yields the best result. Based on the optimal model, a threshold value of 363 is proposed; i.e., when the Ba/Eu value is less than 363, Ba interference with Eu can be neglected during Eu data interpretation. Comparison of this threshold value with a value proposed in earlier works reveals that the proposed prediction model better determines the threshold value for Ba interference with Eu.
Highlights
Rare earth elements and yttrium (REY, or REE if Y is excluded) in coal and coal combustion products (CCPs), e.g., fly and bottom ash, have attracted much attention in recent years, because of the high international demand for these technologically important elements, and because of the restrictions on export from China [1,2]
We propose a threshold value at which Ba interference with Eu has a meaningful effect on ICP-Q-MS results, which is determined via prediction models created using machine learning algorithms
Unlike other ICP-Q-MS data which were obtained based on non-separation between Ba and Eu in solutions digested from solid samples, the data by Yan et al [27] provided a good opportunity for determining the threshold value using machine learning algorithms for Ba interference with Eu in coal and coal combustion products by ICP-Q-MS
Summary
Rare earth elements and yttrium (REY, or REE if Y is excluded) in coal and coal combustion products (CCPs), e.g., fly and bottom ash, have attracted much attention in recent years, because of the high international demand for these technologically important elements, and because of the restrictions on export from China [1,2]. Unlike other ICP-Q-MS data which were obtained based on non-separation between Ba and Eu in solutions digested from solid samples (e.g., coal samples in the U.S Geological Survey’s WoCQI database, Palmer et al [37], and in other numerous published papers, for example but not limited to references [38,39,40,41,42,43,44,45,46]), the data by Yan et al [27] provided a good opportunity for determining the threshold value using machine learning algorithms for Ba interference with Eu in coal and coal combustion products by ICP-Q-MS. In the case of the regression tree, classification and regression tree (CART) algorithms [59] are applied
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