Sorting of Smartphone Components for Recycling Through Convolutional Neural Networks

In a report released by the United Nations University (UNU) in 2020, the global generation of waste electrical and electronic equipment (WEEE) was estimated at 53.6 million tons annually, or 7.3 kg per capita, with WEEE being the fastest-growing solid waste stream in recent years (from 9.2 million tons in 2014 to a projected 74.7 million tons annually by 2030) [1]. The context of WEEE generation also includes a high degree of informality in end-of-life management, with only 17.4% being properly documented and disposed of through formal means, primarily due to technological challenges in collection and recycling faced by the actors involved in this process [1]. From this scenario, the report emphasizes that recycling is a fundamental strategy for minimizing the environmental and societal impacts of the WEEE generation, as it is an essential component of the 2030 Agenda for Sustainable Development under the following United Nations Sustainable Development Goals: Goal 3 (Good Health and Well-being), Goal 6 (Clean Water and Sanitation), Goal 8 (Decent Work and Economic Growth), Goal 11 (Sustainable Cities and Communities), Goal 12 (Responsible Consumption and Production), and Goal 14 (Life Below Water). Over the past decade, there has been a concentration of scientific efforts to find recycling solutions for WEEE. Typically, methods established in the metallurgical industry are adapted for WEEE processing. It is the case of the company Umicore, considered a global benchmark in the field, which has its processes based on copper and lead metallurgy, adding only 15% of WEEE to the primary ores and recovering only the most precious metals, such as gold and silver [2, 3].