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.

通过使用图像分类神经网络作为处理智能手机废料的一种更高效、成本更低、更广泛适用的替代工具，我们调查了在循环经济中废弃电子设备的回收利用，以实现环境和经济的显著收益。我们使用了 1,127 个经过热解的智能手机组件图像构建数据集，并使用 VGG-16 图像分类模型进行训练和评估，模型达到了 83.33% 的准确性，为使用这样的神经网络在材料分离中的可行性提供了支持。