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.

翻译：废弃电气电子设备的回收是实现循环经济的重要工具，具有显著的环境和经济效益潜力。然而，传统的基于物理和化学过程的材料分离技术需要大量投资，且并非适用于所有情况。本文研究利用图像分类神经网络作为控制智能手机废弃物自动化材料分离过程的潜在手段，作为比现有工具更高效、成本更低且适用范围更广的替代方案。我们构建了一个包含1127张热解智能手机组件图像的数据集，用于训练和评估VGG-16图像分类模型。该模型取得了83.33%的准确率，验证了使用此类神经网络进行材料分离的可行性。