Department of Chemical Engineering Materials & Environment, Sapienza-Università di Roma, Via Eudossiana 18, 00184 Rome, Italy.
Waste Manag. 2011 Nov;31(11):2217-27. doi: 10.1016/j.wasman.2011.06.007. Epub 2011 Jul 13.
In this paper new analytical inspection strategies, based on hyperspectral imaging (HSI) in the VIS-NIR and NIR wavelength ranges (400-1000 and 1000-1700 nm, respectively), have been investigated and set up in order to define quality control logics that could be applied at industrial plant level for polyolefins recycling. The research was developed inside the European FP7 Project W2Plastics "Magnetic Sorting and Ultrasound Sensor Technologies for Production of High Purity Secondary Polyolefins from Waste". The main aim of the project is the separation of pure polyethylene and polypropylene adopting an innovative process, the magnetic density separation (MDS). Spectra of plastic particles and contaminants resulting from post-consumer complex wastes and of virgin polyolefins have been acquired by HSI and by Raman spectroscopy. The classification results obtained applying principal component analysis (PCA) on HSI data have been compared with those obtained by Raman spectroscopy, in order to validate the proposed innovative methodology. Results showed that HSI sensing techniques allow to identify both polyolefins and contaminants. Results also demonstrated that HSI has a great potentiality as a tool for quality control of feed (identification of contaminants in the plastic waste) and of the two different pure polypropylene and polyethylene flow streams resulting from the MDS-based recycling process.
本文研究并建立了基于可见-近红外(VIS-NIR)和近红外-中红外(NIR)波长范围(分别为 400-1000nm 和 1000-1700nm)的高光谱成像(HSI)新技术分析检测策略,以定义可应用于聚烯烃回收工业现场的质量控制逻辑。该研究是在欧洲第七框架计划(FP7)项目 W2Plastics“用于从废塑料生产高纯度二次聚烯烃的磁性分选和超声传感器技术”内开展的。该项目的主要目标是采用创新的磁性密度分离(MDS)工艺分离纯聚乙烯和聚丙烯。通过高光谱成像(HSI)和拉曼光谱技术采集了来自消费后复杂废物和原始聚烯烃的塑料颗粒和污染物的光谱。通过主成分分析(PCA)对 HSI 数据进行分类的结果与拉曼光谱的结果进行了比较,以验证所提出的创新方法。结果表明,HSI 传感技术可用于识别聚烯烃和污染物。结果还表明,HSI 作为进料质量控制(识别塑料废物中的污染物)以及基于 MDS 回收工艺得到的两种不同的纯聚丙烯和聚乙烯流的工具具有很大的潜力。