Near-infrared-based determination of mass-based material flow compositions in mechanical recycling of post-consumer plastics: Technical feasibility enables novel applications

Abstract

Mass-based material flow compositions (MFCOs) are crucial to assess and optimize mechanical plastic recycling processes. While MFCOs are determined by manual sorting analysis today, in the future MFCOs could be determined inline through near-infrared-based material flow characterization. This study aims to quantify the accuracy of near-infrared-based MFCO determinations to assess its technical feasibility. Binary mixtures of plastic flakes and post-consumer packaging were pixel-based classified at different material flow presentations, and mass-based MFCOs were predicted from the resulting false-color data using different data processing techniques. The results show high correlations between near-infrared-based false-color data and mass-based MFCOs. Through regression models and data aggregation, it was possible to predict mass-based MFCOs with mean absolute errors of 0.5% and 1.0% and R2-scores of 99.9% and 99.4% for plastic flakes and packaging, respectively, across all material flow presentations. The demonstrated technical feasibility thus paves the way for new sensor technology applications in plastic recycling.