使用红外拉曼光谱与机器学习和深度学习作为废织品的自动织品分类技术.
Pei-Fen Tsai1, Shyan-Ming Yuan1
1Department of Computer Science, National Yang Ming Chiao Tung University, ChiaoTung Campus, Hsinchu 300093, Taiwan.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究引入了先进的拉曼光谱和人工智能用于织品分类,提高回收效率和纤维质量. 这项技术通过改善废物管理以实现可持续时尚来支持循环经济.
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:
- 快速时尚行业产生大量的消费前和消费后织废物,对环境可持续性产生负面影响.
- 线性经济模式加剧了织废物,突出了在循环经济框架内迫切需要有效的回收解决方案.
研究的目的:
- 开发一种高效的织品分类技术,以提高织品的回收利用率.
- 为应对废织品中多样化的纤维成分所带来的挑战.
- 通过精确的分类来提高回收纤维的质量.
主要方法:
- 开发基于拉曼光谱的织品分类系统,能够进行详细的分子组成分析.
- 整合人工智能 (AI) 算法,包括主要组件分析 (PCA),K-最近邻居 (KNN),支持矢量机器 (SVM),随机森林 (RF),人工神经网络 (ANN) 和卷积神经网络 (CNN).
- 根据纤维成分将织品分为六个不同的组.
主要成果:
- 开发的分类器实现了每秒一块的分类效率.
- 根据纤维组成分析,织品分组精度超过95%.
- 拉曼光谱学在为分类提供关键的分子组成数据方面被证明是有效的.
结论:
- 拉曼光谱和人工智能集成系统为织品分类提供了高度准确和高效的解决方案.
- 这项技术有助于提高回收纤维质量,促进可持续的织品回收利用.
- 跨学科的方法为实现织行业循环经济目标提供了可行的途径.
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