Determining the composition of post-consumer flexible multilayer plastic packaging with near-infrared spectroscopy. (15th March 2021) - Chen, Xiaozheng; Kroell, Nils; Wickel, Jan; Feil, Alexander
Highlights: First study focusing on near-infrared-based multilayer packaging classification. Feasibility of sorting for post-consumer multilayer packaging is shown. We offer detailed classification results to different sorting strategies. Influences of different factors on sorting possibility are studied. Suggestions to recycling-friendly multilayer plastic packaging design are given. 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%.