使用LIBS和集成机器学习技术进行煤炭质量的先进多参数预测
Qingsong Wang1, Donglian Zhang1, Youquan Dou1
1Nanjing Coal Quality Supervision and Inspection Co. Ltd, China Energy Corporation, Nanjing 210031, China.
ACS omega
|September 2, 2025
概括
激光诱导分解光谱 (LIBS) 与机器学习相结合,可以准确预测煤炭质量参数. 这种快速分析方法为优化发电厂效率和排放控制提供了可靠的替代方案.
科学领域:
- 分析化学
- 光谱学
- 机器学习
背景情况:
- 精确的煤炭质量评估对于发电厂的燃烧效率和减少排放至关重要.
- 传统的煤炭分析方法可能耗时且劳动密集.
- 开发快速可靠的煤炭分析方法是一个持续的挑战.
研究的目的:
- 开发基于激光诱导分解光谱 (LIBS) 的框架来预测关键的煤质参数.
- 整合先进的机器学习技术以提高预测准确度.
- 为常规煤炭质量分析提供快速有效的替代方案.
主要方法:
- 使用激光诱导分解光谱 (LIBS) 进行碳元素和分子分析.
- 应用光谱预处理技术,包括异常值的去除和基线校正.
- 使用机器学习算法开发预测模型,特别是最小平方支持向量机 (LS-SVM).
主要成果:
- 基于LIBS的框架成功预测了关键的煤炭质量参数:元素碳,灰含量,挥发性物质,总硫和热量.
- 最小方程支持向量机 (LS-SVM) 模型实现了高精度,元素碳预测的R2为0.9940.
- 提出的方法在快速煤炭质量分析中证明了可靠性和效率.
结论:
- 集成的LIBS和机器学习方法为实时煤炭质量监测提供了强大的解决方案.
- 这一框架有可能显著改善燃烧过程和燃煤发电厂的排放控制.
- 该研究强调了先进分析技术对智能工业监控系统的适用性.
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