基于激光诱导分解光谱和深度学习的煤炭和甘分类
Mengyuan Xu1, Yachun Mao1, Zelin Yan2
1School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China.
ACS omega
|December 25, 2023
概括
一种新方法使用激光诱导分解光谱 (LIBS) 和深度学习来分类煤炭和. 这种方法精确地将煤炭与废物分开,提高了煤炭利用效率.
科学领域:
- 地质科学和材料科学 材料科学
- 光谱学和分析化学 分析化学
- 人工智能和机器学习
背景情况:
- 煤炭加工产生了大量的 (废石),占产量的15-20%,碳含量低,灰含量高.
- 有效地分离煤炭和沟对于减少废物和提高煤炭利用效率至关重要.
- 目前的分离方法可能缺乏最佳资源管理所需的精度.
研究的目的:
- 开发和验证一种新型的分类方法,用于区分煤炭和煤.
- 利用先进的光谱技术和深度学习来实现自动化和准确的材料分类.
- 提高煤炭加工业务的效率和经济可行性.
主要方法:
- 使用激光诱导分解光谱 (LIBS) 来从煤炭和样中获得光谱数据.
- 使用格拉米安角总和场 (GASF) 将1D光谱数据转换为2D时间序列表示.
- 开发并应用了一个新的深度学习模型,GASF-CNN,结合SimAM注意力和剩余连接来进行分类.
主要成果:
- 在关键评估指标上,GASF-CNN模型实现了高性能.
- 获得了98.33%的分类准确率,97.06%的回忆率,100%的精度,以及98.51%的F1得分.
- 与其他传统的机器学习和深度学习模型相比,表现出卓越的性能.
结论:
- 拟议的GASF-CNN方法提供了一个准确和有效的方法,用于煤炭和的分类.
- 这种技术在优化采矿行业的煤炭加工和废物管理方面具有重大潜力.
- 整合LIBS,GASF和深度学习为材料的表征和分类提供了一个强大的工具.
相关概念视频
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A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated.
A mixture is composed of two or more types of...
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Compounds are pure substances composed of two or more elements in fixed, definite proportions. Compounds are classified as ionic or molecular (covalent) based on the bonds...
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