RESEARCH ARTICLE


The Utilization of Hyperspectral Imaging for Impurities Detection in Secondary Plastics



S. Serranti*, A. Gargiulo, G. Bonifazi
Department of Chemical Engineering Materials & Environment, Sapienza University of Rome, Via Eudossiana 18, 00184, Rome, Italy


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© 2010 Serranti et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Sapienza – Università di Roma, DICMA, Via Eudossiana n. 18, 00184 Rome, Italy; Tel: +39 06 44585 360; Fax: +39 06 44585 618; E-mail: silvia.serranti@uniroma1.it


Abstract

The systematic identification of impurities inside secondary plastics flow streams can be considered as one of the key issues to certify and to classify waste plastics fed to recycling plants and to perform a full control of the resulting processed fractions, that have to comply with market demands on grade and purity of the recovered products when compared with virgin streams. HyperSpectral Imaging (HSI) can represent an optimal, reliable and low costs answer to reach the previous mentioned goals. HSI is based on the utilization of an integrated hardware and software architecture able to digitally capture and handle spectra, as an image sequence, as they results along a pre-defined alignment on a surface sample properly energized. According to the different wavelengths of the source and the different spectral sensitivity of the device, different physical-chemical superficial characteristics of the sample can be investigated and analyzed. Such an approach, if fully investigated and implemented, could allow to perform a big-step-forward inside quality-inspection-control-strategies/logics (QICSL) applied to the entire waste sector. The identification of contaminants in secondary plastics, adopting HSI, thus can represent a first attempt to introduce new QICSL in an innovative polyolefins separation process from end-of-life product. Such an approach well fit the goals that are at the base of a the application of a non-linear Cyclic Innovation Model (CIM) addressed to connect technical capabilities (contaminants identification) with societal market needs (quality of the recycled products).

Keywords: Recycling, secondary plastics, polyolefins, hyperspectral imaging, sorting.