  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.
