<p>To accurately estimate the potential recovery of metals from electronic devices, various tools such as mass flow analysis, dynamic models, and forecasting models have been employed. However, the reliability of the model-generated outputs hinges on the accuracy of the input data. To ensure accurate data collection, it is imperative to examine and compare different methodologies. Although surveys have conventionally been used in information and telecommunications technologies to gather consumer information, their validity is seldom contrasted with alternative methods due to the lack thereof. In response, a new mixed methodology has been developed to obtain primary consumer data through tangible information, offering new avenues for data acquisition. The methodology involves quantitative and qualitative approaches taking direct physical measurements (dimensions, weight, and quantity) of devices and including a ten question semi-structured interview to discussed consumer devices use, stock and transfer patterns, composition changes of the electronic devices, and disposal behavior. As a result, it was found new methodology measured 79% of the devices directly at laboratories and 21% of the remaining devices were self-reported by the participant. The devices on consumer stage frequently have the same type and number of components as fabric and a positive difference of about 17.99 g compared with the mass reported by fabricants or literature. The sequential steps undertaken by participants in the new methodology to acquire consumer-stage data offer distinct advantages over surveys, particularly in capturing a more comprehensive inventory of devices in storage.</p><p>Additional results indicate that the proposed methodology can provide valuable insights into the stock of electronic devices. Nevertheless, further research is required to understand the implications of surveys versus direct measurements in accurately representing mass flows during the user stage. Additionally, the relevance of external power supply or charging systems on storage will be explored as part of the supplementary findings. By improving the accuracy of metal recovery estimation and exploring more effective data collection methods, we can optimize closed-loop projects and contribute to sustainable resource management.</p>

<p>  </p> <p>The proper classification and disposal of waste are crucial in reducing environmental impacts and promoting sustainability. Several solid waste classification systems have been developed over the years, ranging from manual sorting to mechanical and automated sorting. Manual sorting is the oldest and most commonly used method, but it is time-consuming and labor-intensive. Mechanical sorting is a more efficient and cost-effective method, but it is not always accurate, and it requires constant maintenance. Automated sorting systems use different types of sensors and algorithms to classify waste, making them more accurate and efficient than manual and mechanical sorting systems. In this thesis, we propose the development of an intelligent solid waste detection, classification and tracking system using artificial deep learning techniques. To address the limited samples in the TrashNetV2 dataset and enhance model performance, a data augmentation process was implemented. This process aimed to prevent overfitting and mitigate data scarcity issues while improving the model's robustness. Various augmentation techniques were employed, including random rotation within a range of -20° to 20° to account for different orientations of the recycled materials. A random blur effect of up to 1.5 pixels was used to simulate slight variations in image quality that can arise during image acquisition. Horizontal and vertical flipping of images were applied randomly to accommodate potential variations in the appearance of recycled materials based on their orientation within the image. Additionally, the images were randomly scaled to 416 by 416 pixels, maintaining a consistent image size while increasing the dataset's overall size. Further variability was introduced through random cropping, with a minimum zoom level of 0% and a maximum zoom level of 25%. Lastly, hue variations within a range of -20° to 20° were randomly introduced to replicate lighting condition variations that may occur during image acquisition. These augmentation techniques collectively aimed to improve the dataset's diversity and the model's performance. In this study, YOLOv8, EfficientNet-B0 and VGG16 architectures were evaluated, and stochastic gradient descent (SGD) and Adam were used as the optimizer. Although, SGD provided better test accuracies compared to Adam. </p> <p>Among the three models, YOLOv8 showed the best performance, with the highest average precision mAP of 96.5%. YOLOv8 emerges as the top performer, with ROC values varying from 92.70% (Metal) to 98.40% (Cardboard). Therefore, the YOLOv8 model outperforms both VGG16 and EfficientNet in terms of ROC values and mAP. The findings demonstrate that our novel classifier tracker system made of YOLOv8, and supervision algorithms surpass conventional deep learning methods in terms of precision, resilience, and generalization ability. Our contribution to waste management is in the development and implementation of an intelligent solid waste detection, classification, and tracking system using computer vision and deep learning techniques. By utilizing computer vision and deep learning algorithms, our system can accurately detect, classify, and localize various types of solid waste on a moving conveyor, including cardboard, glass, metal, paper, and plastic. This can significantly improve the efficiency and accuracy of waste sorting processes.</p> <p>This research provides a promising solution for detection, classification, localization, and tracking of solid waste materials in real time system, which can be further integrated into existing waste management systems. Through comprehensive experimentation and analysis, we demonstrate the superiority of our approach over traditional methods, with higher accuracy and faster processing times. Our findings provide a compelling case for the implementation of intelligent solid waste sorting.</p>