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对废弃有机聚合物的激光诱导分解光谱和基于机器学习的表征方法进行机制研究

Rui Liang¹, Chao Chen¹, Junyu Tao²

¹School of Environmental Science and Engineering, Tianjin University, Tianjin, China.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA

|May 31, 2025

概括

机器学习与激光诱导分解光谱学 (LIBS) 结合,可以准确地描述废物有机聚合物 (WOP). 这项研究揭示了可靠的WOP燃料性质预测,增强能源回收和可持续废物管理的关键光谱特征.

科学领域:

分析化学分析化学
材料科学材料科学材料科学
环境科学环境科学

背景情况:

激光诱导分解光谱 (LIBS) 提供了废物有机聚合物 (WOP) 的快速表征.
在LIBS模型中缺乏机械解释性,引发了对WOP分析实际可靠性的担忧.
了解基本的化学相关性对于推动废物管理中的LIBS应用至关重要.

研究的目的:

系统地研究WOP燃料特性和LIBS光谱特征之间的化学相关性.
提高LIBS在WOP表征方面的可靠性和实际应用.
为基于LIBS的系统在能源回收和循环经济倡议中提供理论验证.

主要方法:

特性选择以确定与WOP组成相关的关键LIBS光谱峰值.
机器学习解释性分析以了解光谱特征影响.
使用选定的关键峰值与原始光谱或主要组件进行模型构建.

主要成果:

对碳 (97.74%), (91.22%),氧 (91.28%) 和较低的加热值 (LHV) (97.02%) 实现了高预测准确度.
使用10个选定的关键峰值的模型表现优于使用原始LIBS光谱或主要组件的模型.
C2天带对于碳,氧和LHV预测至关重要;H I线对于预测至关重要.

结论:

机械研究为基于LIBS的WOP表征提供了理论验证.
该方法支持能源回收和可持续废物管理的实际实施.
这项工作促进了循环经济的高效资源利用.

关键词:

有机聚合物废弃物有机聚合物废弃物标志性特征的描述.一个元素组成的元素组成.的加热价值.机器学习是机器学习.这是光谱学.

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