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多能光谱融合和有针对性的特征工程,用于准确的煤炭碳量化

Wenhan Gao^1,2, Boyuan Han^1,2,3, Zhuoyi Sun^1,2

¹State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Nanjing University of Information Science & Technology, Nanjing 210044, China.

Analytical chemistry

|March 5, 2026

概括

精确量化煤炭中的碳对于控制排放至关重要. 这项研究引入了使用激光诱导分解光谱 (LIBS) 和激光诱导等离子体声学 (LIPA) 信号的三模聚变系统,并采用机器学习来改进碳分析.

科学领域:

分析化学分析化学
环境科学环境科学
频谱学是一种光谱学.

背景情况:

煤炭燃烧是二氧化碳 (CO2) 排放的重要来源,需要精确的碳量化,以有效评估和减排战略.
对煤炭中碳含量的准确分析对于环境监测和质量控制至关重要.

研究的目的:

开发和评估三模融合预测系统,以精确估计煤炭中的碳.
将多能激光诱导分解光谱 (LIBS) 和激光诱导等离子体声学 (LIPA) 信号与机器学习算法集成.

主要方法:

使用五个标准煤样本,使用外部和内部标准方法建立了定量模型.
使用各种机器学习算法评估LIBS和LIPA信号的低级数据融合,随机森林显示最佳性能.
开发和应用了新的特征提取方法,包括有针对性的区域保护PCA和混合时间频率调整PCA.

主要成果:

使用LIBS光谱特征的基线模型缺乏化学解释性,不包括与碳相关的特征.
结合LIBS,LIPA和多能信息的三模数据融合显著提高了模型的准确性,可靠性和概括性.
开发的系统通过特征重要性排名和先进的特征提取技术展示了改进的预测能力.

结论:

拟议的三模融合系统为准确的二氧化碳排放监测和煤炭质量评估提供了一个有希望的方法.
与传统方法相比,集成多能LIBS,LIPA信号和先进的机器学习技术提供了优越的碳分析.

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