# Sorting of Smartphone Components for Recycling Through Convolutional Neural Networks

Source: [Sorting of Smartphone Components for Recycling Through Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa71a11e209131cea5232c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:28:17.403Z

Authors: \\'Alvaro G. Becker, Marcelo P. Cenci, Thiago L. T. da Silveira, Hugo M. Veit

Published: 2023-12-27T16:16:15.000Z

## Abstract

The recycling of waste electrical and electronic equipment is an essential tool in allowing for a circular economy, presenting the potential for significant environmental and economic gain. However, traditional material separation techniques, based on physical and chemical processes, require substantial investment and do not apply to all cases. In this work, we investigate using an image classification neural network as a potential means to control an automated material separation process in treating smartphone waste, acting as a more efficient, less costly, and more widely applicable alternative to existing tools. We produced a dataset with 1,127 images of pyrolyzed smartphone components, which was then used to train and assess a VGG-16 image classification model. The model achieved 83.33% accuracy, lending credence to the viability of using such a neural network in material separation.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2312.16626>)

[PDF](<https://arxiv.org/pdf/2312.16626.pdf>)