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Digital Twin Concept for Manual Waste Sorting Management

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In recent years, increasing attention has been given to waste management. It is mainly related to the promotion of the Circular Economy as a key policy objective in the EU. The aim is to shift from a linear approach to a circular one, which is based on reuse and recycling. To enable recycling, it is necessary to separate waste mixtures into different fractions. Therefore, it is crucial to consider and optimize different waste sorting techniques. Despite advances in technology that have emerged as part of Industry 4.0, manual sorting is still widely used as support for automatic/mechanical sorting. Current manual sorting research is mainly focused on human health and ergonomics. There is a definite lack of studies dedicated to the management of this process which is critical for the transformation to a circular economy. Therefore the objective of the paper is to present the digital twin concept for manual waste sorting management. Four stages of research work have been introduced. Within these stages, the motion capture gloves for data collection from physical objects (workers) were proposed. Additionally simulation model for virtual representation was considered and a data exchange system for connection between physical objects and their virtual representations.
Rocznik
Strony
65--74
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • Wrocław University of Science and Technology, Faculty of Mechanical Engineering, Department of Operation and Maintenance of Technical Systems, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
  • Wrocław University of Science and Technology, Faculty of Mechanical Engineering, Department of Operation and Maintenance of Technical Systems, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
Bibliografia
  • [1] REJEB A., SUHAIZA Z., REJEB K., SEURING S., TREIBLMAIER H., The Internet of Things and the circular economy: A systematic literature review and research agenda, J. Clean. Prod., 350, 1–19. DOI:10.1016/j.jclepro.2022.131439
  • [2] ROZENSTEIN O., PUCKRIN E., ADAMOWSKI J., Development of a new approach based on mid-wave infrared spectroscopy for post-consumer black plastic waste sorting in the recycling industry, Waste Manage., 2017, 68, 38–44. DOI:10.1016/j.wasman.2017.07.023.
  • [3] WILTS H., GARCIA B.R., GARLITO R.G., GÓMEZ L.S., PRIETO E.G., Artificial intelligence in the sorting of municipal waste as an enabler of the circular economy, Resources, 2021, 10 (4), 1–9. DOI:10.3390/resources10040028.
  • [4] BOURTSALAS A.C., THEMELIS N.J., Materials and energy recovery at six European MBT plants, Waste Manage., 2022, 141, 79–91. DOI: 10.1016/j.wasman.2022.01.024.
  • [5] LUBONGO C., ALEXANDRIDIS P., Assessment of performance and challenges in use of commercial automated sorting technology for plastic waste, Recycling, 2022, 7 (2), 1–26. DOI: 10.3390/recycling7020011.
  • [6] CIMPAN C., MAUL A., JANSEN M., PRETZ T., WENZEL H., Central sorting and recovery of MSW recyclable materials. A review of technological state-of-the-art, cases, practice and implications for materials recycling, J. Environ. Manage., 2015, 156, 181–199. DOI: 10.1016/j.jenvman.2015.03.025.
  • [7] WERBIŃSKA-WOJCIECHOWSKA S., WINIARSKA K., Maintenance performance in the Age of Indus-try 4.0. A bibliometric performance analysis and a systematic literature review, Sensors, 2023, 23 (3). DOI: 10.3390/s23031409.
  • [8] LENORT R., WICHER P., Methodology for investment decision-making in the area of automated waste sorting systems, Prz. Elektrotech., 2012, 88 (10B).
  • [9] HENRIKSEN M.L., KARLSEN C.B., KLARSKOV P., HINGE M., Plastic classification via in-line hyperspectral camera analysis and unsupervised machine learning, Vib. Spectr., 2022, 118. DOI: 10.1016/j. vibspec.2021.103329.
  • [10] KÜPPERS B., SEIDLER I., KOINIG G., POMBERGER R., VOLLPRECHT D., Influence of throughput rate and input composition on sensor-based sorting efficiency, Detritus, 2020, 9, 59–67. DOI: 10.31025/2611-4135/2020.13906.
  • [11] CUNHA J., CARNEIRO P., COLIM A., Ergonomic assessment in waste sorting jobs with different methods, Stud. Syst. Decis. Control, Springer, Cham 2020, 461–469.
  • [12] BRĄGOSZEWSKA E., BIEDROŃ I., HRYB W., Microbiological air quality and drug resistance in airborne bacteria isolated from a waste sorting plant located in Poland. A case study, Microorg., 2020, 8 (2), 1–11. DOI: 10.3390/microorganisms8020202.
  • [13] EMMATTY F.J., PANICKER V. V., BARADWAJ K.C., Ergonomic evaluation of work table for waste sorting tasks using digital human modelling, Int. J. Ind. Ergon., 2021, 84, 1–8. DOI: 10.1016/j.ergon.2021.103146.
  • [14] BARKHORDARI A., GUZMAN M., EBRAHIMZADEH G., SOROOSHIAN A., DELIKHOON M., JAMSHIDI RASTANI M., GOLBAZ S., FAZLZADEH M., NABIZADEH R., NOROUZIAN BAGHANI A., Characteristics and health effects of particulate matter emitted from a waste sorting plant, Waste Manage., 2022, 150, 244–256. DOI: 10.1016/j.wasman.2022.07.012.
  • [15] ERIKSEN E., AFANOU A.K., MADSEN A.M., STRAUMFORS A., GRAFF P., An assessment of occupational exposure to bioaerosols in automated versus manual waste sorting plants, Environ. Res., 2023, 218, 1–12. DOI: 10.1016/j.envres.2022.115040.
  • [16] GIEL R., MŁYŃCZAK M., PLEWA M., Logistic support model for the sorting process of selectively collected minicipal waste, Adv. Int. Syst. Comput., 2015, 365, 305–318. DOI: 10.1007/978-3-319-19216-1.
  • [17] IP K., TESTA M., RAYMOND A., GRAVES S.C., GUTOWSKI T., Performance evaluation of material separation in a material recovery facility using a network flow model, Res. Cons. Rec., 2018, 192–205. DOI: 10.1016/j.resconrec.2017.11.021.
  • [18] NERSTING L., MALMROS P., SIGSGAARD T., PETERSEN C., Biological health risk associated with re-source recovery, sorting of recycle waste and composting, Grana, 1991, 30 (2), 454–457. DOI: 10.1080/00173139109432008.
  • [19] TESTA M., Modeling and design of material recovery facilities. Genetic algorithm approach, Massachusetts Institute of Technology, Massachusets, 2018.
  • [20] LIU X., JIANG D., TAO B., XIANG F., JIANG G., SUN Y., KONG J., LI G., A systematic review of digital twin about physical entities, virtual models, twin data, and applications, Adv. Eng. Inform., 2022, 55, 1–18. DOI: 10.1016/j.aei.2023.101876.
  • [21] JONES D., SNIDER C., NASSEHI A., YON J., HICKS B., Characterising the Digital Twin. A systematic literature review, CIRP J. Manuf. Sci. Technol., 2020, 29, 36–52. DOI: 10.1016/j.cirpj.2020.02.002.
  • [22] SINGH M., FUENMAYOR E., HINCHY E.P., QIAO Y., MURRAY N., DEVINE D., Digital Twin. Origin to future, Appl. Syst. Innov., 2021, 4 (2), 1–20. DOI: 10.3390/asi4020036.
  • [23] ATTARAN M., CELIK B.G., Digital Twin. Benefits, use cases, challenges, and opportunities, Dec. Anal. J., 2023, 6, 1–10. DOI: 10.1016/j.dajour.2023.100165.
  • [24] LIU M., FANG S., DONG H., XU C., Review of digital twin about concepts, technologies, and industrial applications, J. Manuf. Syst., 2021, 58, 346–361. DOI: 10.1016/j.jmsy.2020.06.017.
  • [25] SHAROTRY A., JIMENEZ J.A., MEDIAVILLA F.A.M., WIERSCHEM D., KOLDENHOVEN R.M., VALLES D., Manufacturing operator ergonomics. A conceptual Digital Twin approach to detect biomechanical fatigue, IEEE Access, 2022, 10, 12774–12791. DOI: 10.1109/ACCESS.2022.3145984.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-716df02f-62e4-4438-9a6f-c4614dd13563
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