Kroell Nils, Chen Xiaozheng, Maghmoumi Abtin, Lorenzo Julius, Schlaak Matthias, Nordmann Christian, Küppers Bastian, Thor Eric, Greiff Kathrin
Department of Anthropogenic Material Cycles, RWTH Aachen University, Wuellnerstr. 2, Aachen D-52062, Germany.
STADLER Anlagenbau GmbH, Max-Planck-Str. 2, Altshausen D-88361, Germany.
Data Brief. 2023 Mar 14;48:109054. doi: 10.1016/j.dib.2023.109054. eCollection 2023 Jun.
Determining mass-based material flow compositions (MFCOs) is crucial for assessing and optimizing the recycling of post-consumer plastics. Currently, MFCOs in plastic recycling are primarily determined through manual sorting analysis, but the use of inline near-infrared (NIR) sensors holds potential to automate the characterization process, paving the way for novel sensor-based material flow characterization (SBMC) applications. This data article aims to expedite SBMC research by providing NIR-based false-color images of plastic material flows with their corresponding MFCOs. The false-color images were created through the pixel-based classification of binary material mixtures using a hyperspectral imaging camera (EVK HELIOS NIR G2-320; 990 nm-1678 nm wavelength range) and the on-chip classification algorithm (CLASS 32). The resulting NIR-MFCO dataset includes = 880 false-color images from three test series: (T1) high-density polyethylene (HDPE) and polyethylene terephthalate (PET) flakes, (T2a) post-consumer HDPE packaging and PET bottles, and (T2b) post-consumer HDPE packaging and beverage cartons for = 11 different HDPE shares (0% - 50%) at four different material flow presentations (singled, monolayer, bulk height H1, bulk height H2). The dataset can be used, e.g., to train machine learning algorithms, evaluate the accuracy of inline SBMC applications, and deepen the understanding of segregation effects of anthropogenic material flows, thus further advancing SBMC research and enhancing post-consumer plastic recycling.
确定基于质量的物料流组成（MFCO）对于评估和优化消费后塑料的回收利用至关重要。目前，塑料回收中的MFCO主要通过人工分拣分析来确定，但在线近红外（NIR）传感器的使用有望实现表征过程的自动化，为新型基于传感器的物料流表征（SBMC）应用铺平道路。本文旨在通过提供具有相应MFCO的塑料物料流的基于近红外的伪彩色图像来加速SBMC研究。伪彩色图像是通过使用高光谱成像相机（EVK HELIOS NIR G2 - 320；波长范围990 nm - 1678 nm）和片上分类算法（CLASS 32）对二元物料混合物进行基于像素的分类而创建的。所得的NIR - MFCO数据集包括来自三个测试系列的880张伪彩色图像：（T1）高密度聚乙烯（HDPE）和聚对苯二甲酸乙二酯（PET）薄片，（T2a）消费后的HDPE包装和PET瓶，以及（T2b）消费后的HDPE包装和饮料纸盒，针对四种不同的物料流呈现方式（单张、单层、堆积高度H1、堆积高度H2）下的11种不同HDPE份额（0% - 50%）。该数据集可用于例如训练机器学习算法、评估在线SBMC应用的准确性以及加深对人为物料流分离效应的理解，从而进一步推动SBMC研究并加强消费后塑料的回收利用。
