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.

Recovering Critical Raw Materials from WEEE using Artificial Intelligence / A. Cabri, F. Masulli, S. Rovetta, M. Mohsin - In: Proceedings of the 21st International Conference on Modelling and Applied Simulation MAS / [a cura di] A.G. Bruzzone, F. Longo, F. De Felice, M. Massei, A. Solis. - [s.l] : [s.l.], 2022 Sep. - ISBN 978-88-85741-76-8. - pp. 1-5 (( convegno 21th International Conference on Modelling and Applied Simulation (MAS), 19th International Multidisciplinary Modeling & Simulation Multiconference tenutosi a Roma nel 2022 [10.46354/i3m.2022.mas.023].

Recovering Critical Raw Materials from WEEE using Artificial Intelligence

A. Cabri
Primo
;
2022

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.
Urban Mines; WEEE recycling; Circular Economy; Computer Vision; Machine Learning
Settore ING-IND/09 - Sistemi per l'Energia e L'Ambiente
set-2022
MITIM-DIME, University of Genoa, Italy
https://www.cal-tek.eu/proceedings/i3m/2022/mas/023/
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/967646
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