ABS树脂
聚碳酸酯
线性判别分析
聚丙烯
主成分分析
材料科学
分类
偏最小二乘回归
化学计量学
聚苯乙烯
近红外光谱
废物管理
计算机科学
人工智能
复合材料
聚合物
工程类
机器学习
量子力学
物理
程序设计语言
作者
Xiaoyu Wu,Jia Li,Linpeng Yao,Zhenming Xu
标识
DOI:10.1016/j.jclepro.2019.118732
摘要
The recycling of plastics from Waste electrical and electronic equipment (WEEE) was constrained by the mix of types. Near-infrared (NIR) spectroscopy is suitable for polymer detection, and it is a rapid, non-destructive analysis method that can be applied to automatic on-line sorting system. The NIR spectra of four commonly recovered WEEE plastics, which are polypropylene (PP), polystyrene (PS), acrylonitrile butadiene styrene (ABS), and acrylonitrile butadiene styrene/polycarbonate (ABS/PC) blend, was collected. The flame-retardant ABS showed difference from ABS in NIR spectra. Three classification methods, which are spectral angle mapper (SAM), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis combined with principal component analysis (PCA-LDA), was tested. And the classification models trained on the virgin plastics have been compared with the models trained on the WEEE plastic to evaluate how these methods perform under limited training data. PLS-DA is one of the most widely used classification method in spectral data analysis, but it had unsuccessful prediction when the training set only included virgin plastics. But the overall prediction accuracy over 99% could be achieved by the other two whether the training set was the spectra of virgin plastics or WEEE plastics. In general, NIR spectroscopy has the competency of separating WEEE plastics. Finally, an automatic on-line sorting system was designed specifically for the large plastic segments from household appliances and electronics.
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