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基于近红外高光谱成像系统的废旧塑料分拣判别模型

A discrimination model in waste plastics sorting using NIR hyperspectral imaging system.

机构信息

Key Laboratory for Green Chemical Technology of State Education Ministry, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, PR China; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300350, PR China.

Key Laboratory for Green Chemical Technology of State Education Ministry, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, PR China; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300350, PR China.

出版信息

Waste Manag. 2018 Feb;72:87-98. doi: 10.1016/j.wasman.2017.10.015. Epub 2017 Nov 10.

Abstract

Classification of plastics is important in the recycling industry. A plastic identification model in the near infrared spectroscopy wavelength range 1000-2500 nm is proposed for the characterization and sorting of waste plastics using acrylonitrile butadiene styrene (ABS), polystyrene (PS), polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). The model is built by the feature wavelengths of standard samples applying the principle component analysis (PCA), and the accuracy, property and cross-validation of the model were analyzed. The model just contains a simple equation, center of mass coordinates, and radial distance, with which it is easy to develop classification and sorting software. A hyperspectral imaging system (HIS) with the identification model verified its practical application by using the unknown plastics. Results showed that the identification accuracy of unknown samples is 100%. All results suggested that the discrimination model was potential to an on-line characterization and sorting platform of waste plastics based on HIS.

摘要

塑料的分类在回收行业中非常重要。本研究提出了一种基于近红外光谱技术(波长范围 1000-2500nm)的塑料鉴别模型,用于鉴定和分拣丙烯腈-丁二烯-苯乙烯共聚物(ABS)、聚苯乙烯(PS)、聚丙烯(PP)、聚乙烯(PE)、聚对苯二甲酸乙二醇酯(PET)和聚氯乙烯(PVC)等废旧塑料。该模型通过标准样品的特征波长应用主成分分析(PCA)构建,分析了模型的准确性、性能和交叉验证。该模型仅包含一个简单的方程、质心坐标和径向距离,便于开发分类和分拣软件。经过验证,高光谱成像系统(HIS)能够通过未知塑料样本进行实际应用。结果表明,未知样品的识别准确率达到 100%。所有结果均表明,该鉴别模型有望应用于基于 HIS 的在线废旧塑料特征化和分拣平台。

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