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Atomic Emission Spectroscopy: Lab01:29

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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
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For AAS measurements, samples must be introduced as clear solutions, often requiring extensive preliminary treatment to dissolve materials like soils, animal tissues, and minerals. Common methods for sample preparation include treatment with hot mineral acids, wet ashing, combustion in closed containers, high-temperature ashing, or fusion with reagents.
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使用激光诱导分解光谱学与随机森林算法相结合,对煤炭组成进行快速定量分析.

Hongkun Du1, Shaoying Ke2, Wei Zhang1

  • 1Centre for Advanced Laser Manufacturing (CALM), School of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry
|June 5, 2024
PubMed
概括

本研究介绍了一种使用激光诱导分解光谱和随机森林算法的快速煤炭分析方法. 该技术准确地确定了煤灰含量和热量,提高了资源利用率.

关键词:
煤炭分析 煤炭分析激光诱导的分解光谱学随机森林算法 随机森林算法

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科学领域:

  • 分析化学 分析化学
  • 材料科学 材料科学 材料科学
  • 频谱学是一种光谱学.

背景情况:

  • 煤炭是中国的主要能源,需要先进的分析技术.
  • 煤炭质量的变化需要快速准确的检测方法.
  • 目前的方法在煤炭分析的速度和精度方面面临挑战.

研究的目的:

  • 开发一种用于煤炭质量的快速定量分析方法.
  • 将激光诱导分解光谱 (LIBS) 与用于煤炭分析的机器学习相结合.
  • 为了准确预测煤灰含量和热量.

主要方法:

  • 使用Q开关Nd:YAG激光器在煤样本中产生等离子体.
  • 用于光谱数据预处理的波幅变换.
  • 应用随机森林算法进行定量分析,并与SVM,ANN和PLS进行比较.

主要成果:

  • 优化波形变换参数 (Db4,3级) 以提高模型性能.
  • 实现了煤灰含量 (R2=0.9470) 和热量 (R2=0.9485) 的高预测精度.
  • 波形变形-随机森林模型的表现优于其他方法.

结论:

  • 激光诱导分解光谱与随机森林算法相结合,为快速准确的煤炭分析提供了一种有效的方法.
  • 开发的模型提供了精确的煤炭成分监测和分析.
  • 这种方法提高了煤炭资源的利用率,并支持减少排放的努力.