拉曼光谱与活性超光谱传感相结合,用于分类含有制阻燃剂的废塑料:传感器融合方法
Tuomas Sormunen1, Ilkka Rytöluoto2, Anna Tenhunen-Lunkka3
1VTT Technical Research Centre of Finland Ltd., Oulu, Finland.
概括
综合拉曼和近红外 (NIR) 光谱的传感器融合有效地根据制阻燃剂 (BFR) 含量对塑料废物进行分类. 这种方法通过准确识别塑料中的BFR水平来提高回收的质量和安全性.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 环境科学 环境科学
背景情况:
- 含有制阻燃剂 (BFR) 的塑料废物的准确分类对于安全和高质量的回收过程至关重要.
- 目前的方法可能缺乏根据BFR度区分塑料垃圾所需的精度.
研究的目的:
- 开发和评估一种传感器融合方法,根据BFR度对塑料废物进行分类.
- 用组合光谱技术提高废塑料中BFR检测的准确性.
主要方法:
- 利用拉曼和近红外 (NIR) 光谱来获取光谱数据.
- 使用主要组件分析 (PCA) 来进行数据维度缩小.
- 训练有素的非常随机的树木分类器使用融合和单个光谱数据,用X射线光光谱作为参考.
主要成果:
- 在含有BFR的塑料中实现了超过80%的平衡分类准确度.
- 通过拉曼和NIR光谱的数据融合,显著提高了准确性.
- 根据元素的度成功分类高和低塑料.
结论:
- 拉曼和NIR光谱的传感器融合提供了一个强大的方法来分类带有BFR的塑料废物.
- 开发的方法通过允许精确的BFR歧视来提高塑料回收的质量和安全性.
- 这种技术有望改善电子废物流的管理.
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