Fast analysis of metal components in coal dust using spark emission spectroscopy

IF 3.1 2区 化学 Q2 CHEMISTRY, ANALYTICAL Journal of Analytical Atomic Spectrometry Pub Date : 2024-10-22 DOI:10.1039/D4JA00322E
Jialin Li, Jing Huang, Lina Zheng, Shakila Naz and Xutong Liu
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Abstract

In this study, we utilized spark emission spectroscopy to develop a fast analysis technique for identifying the concentration of metal components in airborne coal dust. Quantitative measurements of Al, Si, Fe, Ca, and Ti elements in lignite, bituminous coal, and anthracite were conducted. Calibration models were built under both variable concentration and fixed concentration conditions. The R2 values of all calibration curves were approximately 0.90. Except for Si in lignite, the limits of detection (LODs) of other measured elements were within 4 μg m−3, with a sampling time of 10 minutes. Comparative analysis with the reference method revealed normalized root mean square error (NRMSE) concentrations of 9.1%, 9.8%, 10.7%, 24%, and 8.2% for various elements in lignite. Additionally, principal component analysis (PCA) was employed to categorize coal samples, confirming the method's capability to distinguish coal dust types. This shows the effectiveness of spark emission spectroscopy in coal dust composition analysis, demonstrating high sensitivity and efficiency.

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利用火花发射光谱快速分析煤尘中的金属成分
在这项研究中,我们利用火花发射光谱开发了一种快速分析技术,用于确定空气中煤尘中金属成分的浓度。我们对褐煤、烟煤和无烟煤中的铝、硅、铁、钙和钛元素进行了定量测量。在可变浓度和固定浓度条件下建立了校准模型。所有校准曲线的 R2 值均约为 0.90。在采样时间为 10 分钟的情况下,除褐煤中的 Si 外,其他测量元素的检出限均在 4 μg m-3 以内。与参考方法的比较分析表明,褐煤中各种元素的归一化均方根误差(NRMSE)浓度分别为 9.1%、9.8%、10.7%、24% 和 8.2%。此外,采用主成分分析法(PCA)对煤炭样本进行分类,证实了该方法区分煤尘类型的能力。这显示了火花发射光谱在煤尘成分分析中的有效性,具有高灵敏度和高效率。
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来源期刊
CiteScore
6.20
自引率
26.50%
发文量
228
审稿时长
1.7 months
期刊介绍: Innovative research on the fundamental theory and application of spectrometric techniques.
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