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J Geier¹, C Barretta², M Messiha²

¹Polymer Compentence Center Leoben GmbH (PCCL), Sauraugasse 1, 8700 Leoben, Austria; Chair of Material Science and Testing of Polymers, Montanuniversitaet Leoben, Otto Gloeckel-Strasse 2, 8700 Leoben, Austria.

Waste management (New York, N.Y.)

|June 20, 2025

概括

近红外高光谱成像可以有效地识别严重降解的聚乙烯 (PE) 塑料. 这项技术有助于分类老化PE,改善回收品质并扩大塑料回收应用.

科学领域:

材料科学材料科学材料科学
聚合物化学聚合物化学
分析化学分析化学

背景情况:

回收塑料的质量对于更广泛的应用至关重要,但降解会降低回收材料的价值.
识别和从回收流中去除老化塑料对于改善整体质量至关重要.

研究的目的:

研究近红外 (NIR) 超光谱成像的使用,以将降解聚乙烯 (PE) 从降解程度较低的材料中分离出来.
评估NIR光谱学与机器学习相结合的有效性,用于塑料回收的质量控制.

主要方法:

通过紫外线和二氧化暴露,对聚乙烯 (PE) 样品进行人工老化.
使用福里埃变换红外光谱 (FTIR) 和拉伸测试对老化样本进行表征.
使用实验室NIR分类器从老化PE样本中获取NIR光谱,然后使用机器学习算法进行分析.

主要成果:

尼尔射线超光谱成像显示了区分严重降解的PE与未老化或降解程度较低的样本的显著潜力.
在根据NIR光谱数据区分降解和降解程度较低的PE时,获得了超过90%的分类准确度和F1分数.
该研究证实了机械性能,光谱特征和降解状态之间的强烈相关性.

结论:

尼尔射线超光谱成像是一种有前途的非破坏性技术,用于在回收过程中实时分类退化PE.
这项技术可以提高再生PE的质量,使其能够在更苛刻的应用中使用.
实施NIR分类可以显著提高塑料回收的效率和经济可行性.

关键词:

超光谱成像技术的使用.机器学习机器学习多变量数据分析多变量数据分析.在 NIR 进行分类.聚乙烯老化的聚乙烯.回收回收是回收的过程.

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