Literature DB >> 33556715

Determining the composition of post-consumer flexible multilayer plastic packaging with near-infrared spectroscopy.

Xiaozheng Chen1, Nils Kroell2, Jan Wickel2, Alexander Feil2.   

Abstract

Flexible multilayer plastic packaging (MPP) has grown in popularity in the last years especially in food and medical sectors, and its share in the packaging industry is expected to increase further. Compared to traditional packaging with same functionalities, MPP is characterized by lower energy consumption in production and a reduced packaging weight. So far, the recycling of post-industrial MPP with specific material composition has been achieved by several companies. To our knowledge, all existing MPP recycling processes require a known material combination. In contrast to post-industrial MPP, post-consumer MPP still ends up in incinerators or as low-quality products, mainly because of the lacking ability to sort. This study investigates the detectability of post-consumer MPP with near-infrared spectroscopy, the state-of-the-art technology for sensor-based waste sorting. Firstly, MPP classification with near-infrared spectroscopy was analyzed with clean samples. Subsequently, the effect of waste collection and preprocessing in sorting plants on MPP classification was investigated. For this purpose, clean samples were covered with water and oil and mixed with lightweight packaging waste in a drum sieve. The results show it is possible to classify post-consumer MPP based on near-infrared spectra according to different sorting strategies. For the existing recycling processes which are suitable for post-consumer MPP, the corresponding object-based classification accuracy was found to exceed 96%.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Classification; Multilayer plastic packaging; Near-infrared spectroscopy; Plastic recycling; Post-consumer; Sorting

Year:  2021        PMID: 33556715     DOI: 10.1016/j.wasman.2021.01.015

Source DB:  PubMed          Journal:  Waste Manag        ISSN: 0956-053X            Impact factor:   7.145


  1 in total

1.  Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, Using an Advanced Deep Learning Approach.

Authors:  Jordi-Roger Riba; Rosa Cantero; Pol Riba-Mosoll; Rita Puig
Journal:  Polymers (Basel)       Date:  2022-06-17       Impact factor: 4.967

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.