使用磁感应光谱学对碎废金属进行分类
Kane C Williams1, Michael J Mallaburn1, Martin Gagola2
1Department of Electrical and Electroninc Engeering, The University of Manchester, Oxford Road, Manchester M13 9PL, UK.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
磁感应光谱学有效地从造废料中造的物品进行分类. 虽然实验室结果显示了高精度,但工业实施面临着传感器重叠,表面变化和算法选择的挑战,突出了未来改善回收的领域.
科学领域:
- 材料科学 材料科学 材料科学
- 金工程 金工程 金工程
- 循环经济技术 循环经济技术
背景情况:
- 回收对于循环经济至关重要,与初级提取相比,可显著降低能源消耗和温室气体排放.
- 一个"Twitch"废物流包括混合碎和造,需要分离以防止造杂质在回收过程中污染造材料.
研究的目的:
- 为了证明磁感应光谱 (MIS) 在混合废物流中的造的分类效果.
- 实施和评估第一个工业规模的MIS材料回收解决方案,用于分类"Twitch"废物.
主要方法:
- 使用磁感应光谱学 (MIS),测量振荡磁场的散射,以根据它们的导电率差异来表征材料.
- 进行了实验室规模的实验,随后将MIS传感器与工业试验的商业规模分离器系统集成.
主要成果:
- 实验室系统在炼的分类中实现了89.66%的回收和94.96%的纯度.
- 工业系统的性能低于实验室结果,发现了包括传感器重叠错误分类,表面依赖结果和算法影响在内的问题.
结论:
- 磁感应光谱显示出对自动化废物分类的希望,特别是在区分造和造合金方面.
- 优化传感器配置,考虑表面变化,并选择适当的机器学习算法 (如人工神经网络) 对于成功的工业部署至关重要.
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