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Assessing the resource potential of paper and board in lightweight packaging waste sorting plants through manual analysis and sensor-based material flow monitoring

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WASTE MANAGEMENT 2024年 189卷 196-210页

作者： Spies, Alena Maria Kroell, Nils Ludes, Annika Kueppers, Bastian Raulf, Karoline Greiff, Kathrin Rhein Westfal TH Aachen Dept Anthropogen Mat Cycles Wuellnerstr 2 D-52062 Aachen Germany STADLER Anlagenbau GmbH Max Planck Str 2 D-88361 Altshausen Germany

The recycling of paper and board (PB) yields economic and environmental advantages compared to primary paper production. However, PB from lightweight packaging (LWP) waste is currently not comprehensively reintegrated into the paper value stream. To develop an adapted recycling process for PB from LWP, PB quantities, qualities, and fluctuations ranges in LWP are required. Currently, no sufficient database is available. Therefore, we developed a methodical approach and conducted a case study to access the PB potential in LWP sorting plants using manual analysis and sensor-based material flow monitoring. Differences resulting from seasonal variations, materials from different settlement structures, and fluctuation ranges in LWP composition over two weeks have been investigated. PB contents in the input of 6.5 wt% (ww) and 5.9 wt% (ww) were determined for winter and summer sampling campaigns, respectively. The PB product stream amounted to 5.7 wt% (ww, winter) and 4.8 wt% (ww, summer). Around 45 wt% (ww) of PB from the PB product stream was classified as misplaced by the consumer and should have been discarded in separate paper collections. based on the determined PB quantities and qualities, a potential of usable and in the PB product stream available PB in LWP was determined. The technically available and usable PB potential in German LWP waste amounts to 89,000 to 100,000 tons per year (average PB yield of around 65 wt% (ww). The methodical approach can be adapted for sorting plant balances. The results can contribute to developing an adapted recycling process for PB from LWP.

关键词： Sampling Waste analyses Near-infrared spectroscopy sensor-based material flow characterization Plastic waste Processing plant

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Towards digital twins of waste sorting plants: Developing data-driven process models of industrial-scale sensor-based sorting units by combining machine learning with near-infrared-based process monitoring

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RESOURCES CONSERVATION AND RECYCLING 2024年 200卷

作者： Kroell, Nils Maghmoumi, Abtin Dietl, Tobias Chen, Xiaozheng Kueppers, Bastian Scherling, Tabea Feil, Alexander Greiff, Kathrin Rhein Westfal TH Aachen Dept Anthropogen Mat Cycles Wuellnerstr 2 D-52062 Aachen Germany STADLER Anlagenbau GmbH Max Planck Str 2 D-88361 Altshausen Germany

Sorting plants are crucial for effective recycling, but their optimization can be challenging due to the heterogeneity of waste streams. We introduce a novel approach to holistically optimize sorting plants using digital twins containing data-driven process models. To demonstrate their technical feasibility, we developed a data -driven process model for industrial-scale sensor-based sorting (SBS) units by combining near-infrared process monitoring with machine learning. Our results indicate a sorting performance change (F1-score) in the SBS unit by 0.22 a% for +1% occupation density and +0.19 a% for +1 wt% target material share. An artificial neural network predicted the SBS behavior with a 3.0% mean absolute error. Our case study demonstrates the potential of data-driven process models for digital twins by clarifying the influence of throughput fluctuations on SBS performance and simulating different SBS cascade designs, thus paving the way towards improved design and operation of sorting plants and a more circular future.

关键词： sensor-based material flow characterization Circular economy Mechanical post-consumer plastic recycling Data-driven process simulation Artificial intelligence Lightweight packaging waste

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Deep learning-based instance segmentation on 3D laser triangulation data for inline monitoring of particle size distributions in construction and demolition waste recycling

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RESOURCES CONSERVATION AND RECYCLING 2024年 205卷

作者： Wu, Xiaoye Kroell, Nils Greiff, Kathrin Rhein Westfal TH Aachen Dept Anthropogen Mat Cycles Wuellnerstr 2 D-52062 Aachen Germany

Overlapping material flow presentations in construction and demolition waste (CDW) recycling make an inline particle size distribution (PSD) monitoring challenging. Here, we aim to build a deep-learning-based segmentation model for overlapping particles in 3D -laser -triangulation images of CDW. Our model was trained on three specially designed datasets with two transfer learning processes. U -net was employed as the backbone and MultiStar algorithm was used to describe particle shapes. The final model demonstrated an impressive performance on test set, with a mean average precision (mAP) of 92.8% at IoU = 0.5. Comparing with the traditional segmentation algorithm based on image processing methods, the mAP can only reach to 27.4% on the same images. The shown model performance paves the way toward novel sensor technology applications for real -time PSD monitoring in CDW recycling.

关键词： sensor-based material flow characterization Digital image processing Deep learning Artificial intelligence Instance segmentation Circular economy

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Near-infrared-based quality control of plastic pre-concentrates in lightweight-packaging waste sorting plants

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RESOURCES CONSERVATION AND RECYCLING 2024年 201卷

作者： Kroell, Nils Chen, Xiaozheng Kueppers, Bastian Schlogl, Sabine Feil, Alexander Greiff, Kathrin Rhein Westfal TH Aachen Dept Anthropogen Mat Cycles Wuellnerstr 2 D-52062 Aachen Germany STADLER Anlagenbau GmbH Max Planck Str 2 D-88361 Altshausen Germany Univ Leoben Chair Waste Proc Technol & Waste Management A-8700 Leoben Austria

Today's post-consumer plastic recycling is limited by labor-intensive manual quality control (MQC) procedures, resulting in largely unknown pre-concentrate purities. sensor-based quality control (SBQC) could enable an automated inline quality monitoring and thus contribute to a more transparent and enhanced plastic recycling. Therefore, we investigated the technical feasibility of near-infrared-based SBQC for plastic pre-concentrates in a lightweight packaging waste sorting plant. The developed SBQC method outperformed MQC methods by reducing measurement uncertainties from between +/- 0.8 wt% and +/- 6.7 wt% (MQC) to +/- 0.31 wt% (SBQC) for bale-specific purities at monolayered material flow presentations. In addition, we show that SBQC may even be possible at multilayered material flow presentations, although further research is needed to address identified segregation effects. The demonstrated technical feasibility of SBQC at plant scale represents a major break-through as it opens new opportunities in plastic recycling, such as adaptive pricing models and intelligent process control in sorting plants.

关键词： sensor-based material flow characterization Automated quality control Inline quality monitoring Circular economy Mechanical post-consumer plastic recycling Machine learning

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Robust near-infrared-based plastic classification with relative spectral similarity pattern

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WASTE MANAGEMENT 2023年第1期166卷 315-324页

作者： Jeon, Youngjun Seol, Woojin Kim, Soohyun Kim, Kyung-Soo Korea Adv Inst Sci & Technol KAIST Dept Mech Engn Daejeon South Korea Korea Hydro & Nucl Power Gyeongju South Korea

sensor-based material flow characterization techniques, particularly hyperspectral imaging in the near-infrared (NIR) range, can recognize materials quickly, accurately, and economically. When identifying materials using NIR hyperspectral imaging, extracting influential features from high-dimensional wavelength information is essential for effective recognition. However, spectral noise from the rough and contaminated surfaces of objects (especially un-shredded waste) degrades the feature-extraction performance, which in turn deteriorates the material classification performance. In this study, we propose a real-time feature-extraction method, named relative spectral similarity pattern color mapping (RSSPCM), to robustly classify materials in noisy environments, such as plastic waste sorting facilities. RSSPCM compares relative intra- and inter-class spectral similarity patterns, instead of individual similarity, to class-representative spectra alone. Recognition targets have similar chemical makeups that are applied to feature extraction as an intra-class similarity ratio. The proposed model is robust owing to the remaining relative similarity trends found in a contaminated spectrum. We evaluated the effectiveness of the proposed method using noisy samples obtained from a waste-management facility. The results were compared with two spectral groups obtained at different noise levels. Both results showed high accuracy as there was an increased number of true positives for low-reflectance regions. The average F1-score values were 0.99 and 0.96 for low- and high-noise sets, respectively. Furthermore, the proposed method showed minimal F1-score variations between classes (standard deviation of 0.026 for the high-noise set).

关键词： Feature extraction Hyperspectral imaging material classification sensor-based material flow characterization Spectral noise Spectral similarity

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sensor-based particle mass prediction of lightweight packaging waste using machine learning algorithms

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WASTE MANAGEMENT 2021年 136卷 253-265页

作者： Kroell, Nils Chen, Xiaozheng Maghmoumi, Abtin Koenig, Morgane Feil, Alexander Greiff, Kathrin Rhein Westfal TH Aachen Dept Anthropogen Mat Cycles Wullnerstr 2 D-52062 Aachen Germany

sensor-based material flow characterization (SBMC) promises to improve the performance of future-generation sorting plants by enabling new applications like automatic quality monitoring or process control. Prerequisite for this is the derivation of mass-based material flow characteristics from pixel-based sensor data, which requires known individual particle masses. Since particle masses cannot be measured inline, the prediction of particle masses of lightweight packaging (LWP) waste using machine learning (ML) algorithms is investigated. Five LWP material classes were sampled, preprocessed, and scanned on a custom-made test rig, resulting in a dataset containing 3D laser triangulation (3DLT) images, RGB images, and corresponding masses of n = 3,830 particles. based on 66 extracted shape measurements, six ML models were trained for particle mass prediction (PMP). Their performance was compared with two state-of-the-art reference models using (i) material-specific mean particle masses and (ii) grammages. Obtained particle masses showed a high variation and significant differences between material classes and particle size classes. After feature selection, both reference models achieving R-2-scores of (i) 0.422 +/- 0.121 and (ii) 0.533 +/- 0.224 were outperformed by all investigated ML models. A random forest regressor with an R-2-score of 0.763 +/- 0.091 and a normalized mean absolute error of 0.243 +/- 0.050 achieved the most accurate PMP. In contrast to studies on primary raw materials, PMP of LWP waste is challenging due to influences of packaging design and post-consumer disposal behavior. ML algorithms are a promising approach for PMP that outperform state-of-the-art methods by 43% higher R-2-scores.

关键词： sensor-based material flow characterization Particle mass prediction Lightweight packaging waste 3D laser triangulation Machine learning Shape measurements

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NIR-MFCO dataset: Near-infrare d-based false-color images of post-consumer plastics at different material flow compositions and material flow presentations

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DATA IN BRIEF 2023年 48卷 109054页

作者： Kroell, Nils Chen, Xiaozheng Maghmoumi, Abtin Lorenzo, Julius Schlaak, Matthias Nordmann, Christian Kuppers, Bastian Thor, Eric Greiff, Kathrin Rhein Westfal TH Aachen Dept Anthropogen Mat Cycles Wuellnerstr 2 D-52062 Aachen Germany STADLER Anlagenbau GmbH Max Planck Str 2 D-88361 Altshausen Germany

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 n = 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 n = 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. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/)

关键词： sensor-based material flow characterization Lightweight packaging waste Mechanical plastic recycling Circular economy Machine learning Computer vision NIR spectroscopy Polymers

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