1. Proceedings
  2. I3M
  3. 2022
  4. MAS
  5. 10.46354/i3m.2022.mas.023

Recovering Critical Raw Materials from WEEE using Artificial Intelligence 

  • a Alberto Cabri ,
  • b Francesco Masulli,
  • c Stefano Rovetta, 
  • d Muhammad Mohsin 
  • a,b,c,d  DIBRIS University of Genoa, Via Dodecaneso 35, Genoa, 16146, Italy 
  • a.b.c Vega Research Laboratories, Via Ippolito d’Aste 7/5, Genoa, 16121, Italy 
Cite as
Cabri A., Masulli F., Rovetta S., Mohsin M. (2022).,Recovering Critical Raw Materials from WEEE using Artificial Intelligence. Proceedings of the 21st International Conference on Modelling and Applied Simulation MAS 2022). , 023 . DOI: https://doi.org/10.46354/i3m.2022.mas.023

Abstract

In the last few years, Artificial Intelligence (AI) has assumed a key role in the Circular Economy (CE), and particularly in the waste management process by supporting fast and efficient sorting of materials with computer vision and object recognition. The system presented in this paper demonstrates that AI could be a valuable asset in waste of electrical and electronic equipment (WEEE) recycling. In fact, the obtained accuracy of classification equal to 80% corresponds to a significant improvement compared to current situation in the recovery of critical raw materials (CRM) from the WEEE in which the whole board is shredded and only a maximum of 10-15 chemical components are recycled, while the majority of the CRM are lost.

References

  1. Akinode, J. L. and Oloruntoba, A. (2020). Artificial intelligence in the transition to circular economy. American Journal of Engineering Research, 9:185–190. 
  2. Ankile, L. L., Heggland, M. F., and Krange, K. (2020). Deep Convolutional Neural Networks: A survey of the foundations, selected improvements, and some current applications. arXiv:2011.12960. 
  3. Berg, H., Blévennec, K. L., Kristoffersen, E., Strée, B., Witomski, A., Stein, N., Bastein, T., Ramesohl, S., and Vrancken, K. (2020). Digital circular economy as a cornerstone of a sustainable European indus try transformation. White Paper - ECERA European Circular Economy Research Alliance. 
  4. Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 120:122–125. 
  5. Calaiaro, J. (2022). AI Takes a Dumpster Dive: Computer-vision systems sort your recyclables atsuperhuman speed. IEEE Spectrum, 59(7):22–27.
  6. Chum, O., Pajdla, T., and Sturm, P. (2005). The geo metric error for homographies. Computer Vision and Image Understanding, 97(1):86–102. 
  7. Ghoreishi, Malahat and Happonen, Ari (2020). New promises ai brings into circular economy accelerated product design: a review on supporting literature. E3S Web Conf., 158:06002. 
  8. Jocher, G. et al. (2022). ultralytics/yolov5: v6.1 - Ten sorRT, TensorFlow Edge TPU and OpenVINO Export and Inference. 
  9. Magnus, G. et al. (2018). Report on critical raw materi als and the circular economy. European Commission, Directorate General for Internal Market, Industry, Entrepreneurship and SMEs. 
  10. Merkel, D. (2014). Docker: lightweight Linux con tainers for consistent development and deployment. Linux journal, 2014(239):2. 
  11. Tzutalin (2015). Labelimg. git code. https://github.com /tzutalin/labelImg. 
  12. Van Rossum, G. and Drake Jr, F. L. (1995). Python refer ence manual. Centrum voor Wiskunde en Informatica Amsterdam. 
  13. V.V.A.A. (2020). Critical raw materials re silience: Charting a path towards greater security and sustainability. https://eur lex.europa.eu/legal-content/EN/TXT/PDF/ 
    ?uri=CELEX:52020DC0474&from=EN [Accessed: 2022-04-11]. 
  14. V.V.A.A. (2022a). Circular economy action plan. https://ec.europa.eu/environment/strategy/circular economy-action-plan_it [Accessed: 2022-04-11]. 
  15. V.V.A.A. (2022b). Sustainable development goals. United Nations, https://sdgs.un.org/goals [Accessed: 2022-04-11]. 
  16. Weiss, K., Khoshgoftaar, T. M., and Wang, D. (2016). A survey of transfer learning. Journal of Big Data, 3(1):9. Yamashita, R., Nishio, M., Do, R. K. G., and Togashi, K. 
    (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging, 9(4):611–629.