通过机器学习对固体样品火焰发射光谱进行矿物学分析

IF 6.7 1区化学 Q1 CHEMISTRY, ANALYTICAL Analytical Chemistry Pub Date : 2024-11-22 DOI:10.1021/acs.analchem.4c03107

Adam R. Bernicky, Boyd Davis, Milen Kadiyski, Hans-Peter Loock

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引用次数: 0

摘要

使用专门的火焰光学发射光谱 (OES) 系统，通过火焰发射光谱分析火法冶炼铜厂使用的固体预浓缩矿石样品。使用人工神经网络 (ANN) 对 8500 多条复杂光谱进行分类，优化后的人工神经网络有 10 个隐藏层，每层有 40 个节点。人工神经网络能够对所有样品的元素含量进行量化，误差不超过 1.5 质量%，并能识别普遍存在的矿物，误差不超过 2.5 质量%。火焰温度的不确定性为 σ < 3 K，粒度的不确定性为 2 μm。结果表明，该模型优于非线性部分最小二乘拟合模型，后者相当于没有隐藏层的 ANN。

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Mineralogical Analysis of Solid-Sample Flame Emission Spectra by Machine Learning

Solid preconcentrated ore samples used in pyrometallurgical copper smelters are analyzed by flame emission spectroscopy using a specialized flame optical emission spectroscopy (OES), system. Over 8500 complex spectra are categorized using an artificial neural network (ANN) that was optimized to have 10 hidden layers with 40 nodes per layer. The ANN was able to quantify the elemental content of all samples to within better than 1.5 mass% and was able to identify the prevalent minerals to within better than 2.5 mass%. The flame temperature was obtained with an uncertainty of σ < 3 K and the particle size to within 2 μm. The results are found to be superior to those obtained to a nonlinear partial least-squares fit model, which is equivalent to an ANN having no hidden layers.

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来源期刊

Analytical Chemistry 化学-分析化学

CiteScore

12.10

自引率

12.20%

发文量

1949

审稿时长

1.4 months

期刊介绍： Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.