Sorting of Smartphone Components for Recycling Through Convolutional Neural Networks
This addresses the need for more efficient and cost-effective recycling methods in waste management, though it is incremental as it applies an existing model to a new dataset.
The paper tackled the problem of recycling smartphone waste by using a VGG-16 convolutional neural network for image classification to automate material separation, achieving 83.33% accuracy.
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.