Bischoff Peter, Kaas Alexandra, Schuster Christiane, Härtling Thomas, Peuker Urs
Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Maria-Reiche-Str. 2, 01109 Dresden, Germany.
Institute of Solid State Electronics, Technische Universität Dresden, 01069 Dresden, Germany.
J Imaging. 2023 Jul 5;9(7):135. doi: 10.3390/jimaging9070135.
With the increasing number of electrical devices, especially electric vehicles, the need for efficient recycling processes of electric components is on the rise. Mechanical recycling of lithium-ion batteries includes the comminution of the electrodes and sorting the particle mixtures to achieve the highest possible purities of the individual material components (e.g., copper and aluminum). An important part of recycling is the quantitative determination of the yield and recovery rate, which is required to adapt the processes to different feed materials. Since this is usually done by sorting individual particles manually before determining the mass of each material, we developed a novel method for automating this evaluation process. The method is based on detecting the different material particles in images based on simple thresholding techniques and analyzing the correlation of the area of each material in the field of view to the mass in the previously prepared samples. This can then be applied to further samples to determine their mass composition. Using this automated method, the process is accelerated, the accuracy is improved compared to a human operator, and the cost of the evaluation process is reduced.
随着电气设备数量的增加,尤其是电动汽车的增多,对电子元件高效回收工艺的需求也在上升。锂离子电池的机械回收包括电极的粉碎和颗粒混合物的分选,以实现各材料组分(如铜和铝)尽可能高的纯度。回收的一个重要部分是产量和回收率的定量测定,这对于使工艺适应不同的进料是必需的。由于这通常是在确定每种材料的质量之前通过手动分选单个颗粒来完成的,我们开发了一种用于自动化此评估过程的新方法。该方法基于基于简单阈值技术检测图像中的不同材料颗粒,并分析视野中每种材料的面积与先前制备样品中的质量之间的相关性。然后可以将其应用于其他样品以确定其质量组成。使用这种自动化方法,该过程得到加速,与人工操作相比精度提高,并且评估过程的成本降低。