Development of an artificial neural network for identification of used button cell batteries

各种形状和尺寸的碱性电池用于许多应用，占加拿大市场每年销售的所有电池的78%。碱性（不可充电）和锂离子（可充电）电池含有锌和稀土金属，如锂，镉和镍。如果这些电池被负责任地回收，它们将为农业生产提供新的能源，节约能源和资源，保护环境，并将废物从垃圾填埋场转移。但是，考虑到废电池的数量和种类，自动化系统需要能够通过化学分类来确保适当的回收。分类可以更好地控制环境结果，因为分离的电池类型可以由最好的专业回收商使用专为回收特定电池化学物质而设计的技术进行处理。在这个可行性研究项目中，我们将研究不同的方法来采用新技术，以提高家用电池回收的效率和生产力。本研究的核心是利用机器视觉和人工神经网络（ANN）技术，开发一种可靠的方法来识别不同类型的废旧纽扣电池。

Alkaline batteries in all shapes and sizes are used in many applications and comprise 78% of all batteries soldinto the Canadian market every year. Alkaline (non-rechargeable) and Lithium-Ion (rechargeable) batteriescontain Zinc and rare Earth metals such as Lithium, Cadmium, and Nickel. If these batteries are recycledresponsibly, they will provide new sources for fertilizing agricultural fields, save energy and resources, protectthe environment, and divert waste from landfills.However, considering the volume and variety of waste batteries, automated systems require the ability to sortby chemistry in order to ensure proper recycling. Sorting results in better control over environmental outcomesas separated battery types can be processed by the best specialty recyclers using technology designed forrecycling of specific battery chemistries.In this feasibility study project, we will investigate different ways to employ new technologies for improvingthe efficiency and productivity of household batteries recycling. The core of our study will be to develop areliable method for identifying different types of used button cell batteries using machine vision and ArtificialNeural Networks (ANNs).