通过近红外高光谱成像来评估消费后聚烯的融化流速
Nikolai Kuhn1, Moritz Mager2,3, Gerald Koinig1
1Chair of Waste Processing Technology and Waste Management, Department of Environmental and Energy Process Engineering, Technical University of Leoben, 8700 Leoben, Austria.
Polymers
|February 27, 2026
概括
近红外高光谱成像 (NIR-HSI) 有效地预测消费后聚烯 (PP) 包装中的融化流速 (MFR). 这项技术有助于按等级分类PP回收材料,提高回收材料的质量和可用性.
科学领域:
- 材料科学 材料科学 材料科学
- 聚合物回收利用 聚合物回收利用
- 频谱学是一种光谱学.
背景情况:
- 聚烯 (PP) 的机械回收受到原料特性不一致的阻碍,特别是化流速 (MFR).
- 消费后PP包装等级之间MFR的变化限制了回收材料的质量和适用性.
- 准确的MFR预测对于有效的PP回收物分类和升级至关重要.
研究的目的:
- 评估近红外高光谱成像 (NIR-HSI) 在消费后 PP 包装中预测 MFR 的有效性.
- 使用光谱数据,比较各种回归和分类模型的MFR预测性能.
- 评估样品颜色 (白色与透明) 对预测准确性的影响.
主要方法:
- 从材料回收设施获取82个刚性PP样本,MFR范围为2至108g 10min-1 .
- 应用十三种线性和非线性回归模型,使用中位数和像素智能的光谱数据.
- 在各种MFR值下对二进制分类模型的评估.
- 分析频谱数据以确定PP特征的信息区域.
主要成果:
- 基于树的回归模型表现出强的性能,白色PP获得R2 = 0.85,透明PP获得R2 = 0.61.
- 综合数据集为MFR预测提供了R2 = 0.66.
- 二元分类模型实现了0.82到0.92.9之间的平衡准确度.
- 频谱表示的中位数始终优于像素智能聚合.
结论:
- NIR-HSI是一种可行的技术,用于支持消费后PP包装的分类,特别是不透明的白色样品.
- 该方法在提高PP回收材料的价值和一致性方面显示出希望.
- 由于高MFR值和固有的数据异常值的异构复杂性,仍然存在挑战.
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