NIR-MFCO dataset: Near-infrared-based false-color images of post-consumer plastics at different material flow compositions and material flow presentations
Author:
Publisher
Elsevier BV
Subject
Multidisciplinary
Reference20 articles.
1. Near-infrared-based determination of mass-based material flow compositions in mechanical recycling of post-consumer plastics: technical feasibility enables novel applications;Kroell;Resour. Conserv. Recycl.,2023
2. Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: a systematic literature review;Kroell;Waste Manag.,2022
3. Circular Economy Action Plan: For A Cleaner and More Competitive,2020
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5. Relevance and challenges of plant control in the pre-processing stage for enhanced sorting performance;Küppers,2022
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1. Near-infrared-based quality control of plastic pre-concentrates in lightweight-packaging waste sorting plants;Resources, Conservation and Recycling;2024-02
2. 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;Resources, Conservation and Recycling;2024-01
3. NIR-MFCO dataset: Near-infrared-based false-color images of post-consumer plastics at different material flow compositions and material flow presentations;Data in Brief;2023-06
4. Near-infrared-based determination of mass-based material flow compositions in mechanical recycling of post-consumer plastics: Technical feasibility enables novel applications;Resources, Conservation and Recycling;2023-04
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