使用机器学习辅助的激光诱导分解光谱技术对电子废物进行分类
Zahid Ali1,2, Yasir Jamil1,2, Hafeez Anwar2
1Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, Pakistan.
概括
人工智能 (AI) 与激光诱导分解光谱学 (LIBS) 结合,有效地将电子废物中的合金分类. 这种无接触方法提高了回收效率,并减少了废物管理中的手工劳动.
科学领域:
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 可持续的废物管理对于经济增长和资源保护至关重要.
- 电子废物 (电子废物) 的管理需要高效的金属分类.
- 人工智能 (AI) 提供了快速,无接触的电子垃圾分类潜力.
研究的目的:
- 评估基于人工智能的激光诱导分解光谱 (LIBS) 系统,用于在电子废物中分类合金.
- 为了比较不同机器学习 (ML) 模型对此分类任务的有效性.
主要方法:
- 利用激光诱导分解光谱学 (LIBS) 来获得五种类型的合金的光谱数据.
- 采用机器学习 (ML) 分类器,包括主要组件分析 (PCA) 和K-近邻 (kNN) 变体.
- 训练有素的监督ML模型,对80%的光谱数据进行了10倍的交叉验证,并对20%进行了测试.
主要成果:
- 主要成分分析 (PCA) 在区分合金光谱方面无效.
- 标准的K-近邻 (kNN) 模型的准确性不到30%.
- 将kNN与随机子空间方法组合起来,显著提高了分类准确度,达到98%.
结论:
- 一个基于人工智能的LIBS系统提供了有效的,非接触的电子废料合金的分类.
- 这项技术可以与机器人系统集成,以尽量减少回收过程中的手工劳动.
- 这项研究表明了加强电子废物管理和资源回收的可行方法.
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