LIBS-ConSort: Development of a sensor-based sorting method for construction and demolition waste

被引:0
|
作者
Klewe, Tim [1 ]
Völker, Tobias [1 ]
Landmann, Mirko [2 ]
Kruschwitz, Sabine [1 ,3 ]
机构
[1] Bundesanstalt für Materialfor schung und -prüfung, Berlin, Germany
[2] Institut für Angewandte Bauforschung Weimar gGmbH, Weimar, Germany
[3] Technische Universität Berlin, Berlin, Germany
关键词
Concretes - Data fusion - Demolition - Impurities - Infrared devices - Multivariant analysis - Near infrared spectroscopy - Recycling - Sorting;
D O I
10.1002/cepa.2866
中图分类号
学科分类号
摘要
A joint project of partners from industry and research institutions approaches the challenge of construction and demolition waste (CDW) sorting by investigating and testing the combination of laser-induced breakdown spectroscopy (LIBS) with near-infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.). Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic / technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates. The objective of this paper is to present current progress and results of the test stand development combining LIBS with NIR spectroscopy and visual imaging. In the future, this laboratory prototype will serve as a fully automated measurement setup to allow real-time classification of CDW on a conveyor belt. © 2023 The Authors. Published by Ernst & Sohn GmbH.
引用
收藏
页码:973 / 976
相关论文
共 50 条
  • [21] Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions
    Zhao, Yue
    Li, Jia
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2022, 56 (24) : 17531 - 17544
  • [22] Explainable AI for sensor-based sorting systems
    Anneken, Mathias
    Veerappa, Manjunatha
    Huber, Marco F.
    Kuehnert, Christian
    Kronenwett, Felix
    Maier, Georg
    TM-TECHNISCHES MESSEN, 2023, 90 (03) : 154 - 165
  • [23] Processing of aluminium scrap with sensor-based sorting
    Harbeck, Hartmut
    Aufbereitungs-Technik/Mineral Processing, 2006, 47 (12): : 4 - 12
  • [24] State-of-the-art of Sensor-based Sorting
    Wotruba, H.
    BHM Berg- und Huttenmannische Monatshefte, 2008, 153 (06): : 221 - 224
  • [25] Development of sustainable construction material using construction and demolition waste
    Dakwale, V. A.
    Ralegamkar, R. V.
    INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES, 2014, 21 (04) : 451 - 457
  • [26] Deep learning of grasping detection for a robot used in sorting construction and demolition waste
    Yuedong Ku
    Jianhong Yang
    Huaiying Fang
    Wen Xiao
    Jiangteng Zhuang
    Journal of Material Cycles and Waste Management, 2021, 23 : 84 - 95
  • [27] Deep learning of grasping detection for a robot used in sorting construction and demolition waste
    Ku, Yuedong
    Yang, Jianhong
    Fang, Huaiying
    Xiao, Wen
    Zhuang, Jiangteng
    JOURNAL OF MATERIAL CYCLES AND WASTE MANAGEMENT, 2021, 23 (01) : 84 - 95
  • [28] Influence of plastic packaging design on the sensor-based sortability in lightweight packaging waste sorting plants
    Jakobs, Michelle
    Kroell, Nils
    RESOURCES CONSERVATION AND RECYCLING, 2024, 207
  • [29] Separation of kimberlite from waste rocks using sensor-based sorting at Cullinan Diamond Mine
    Mahlangu, T.
    Moemise, N.
    Ramakokovhu, M. M.
    Olubambi, P. A.
    Shongwe, M. B.
    JOURNAL OF THE SOUTHERN AFRICAN INSTITUTE OF MINING AND METALLURGY, 2016, 116 (04) : 343 - 347
  • [30] Tests on a f reef all sorter sensor-based sorting of mineral waste and raw materials
    Muller, Sebastian
    Muller, Anette
    Ddring, Ines
    Palzer, Ulrich
    Aufbereitungs-Technik/Mineral Processing, 2021, 62 (05): : 48 - 55