Enhancing Circular Economy Models for Battery Material Recovery Using IoT Data and AdaBoost Algorithm

This research investigates the combination of Internet of Things (IoT) data with the AdaBoost algorithm to improve circular economy frameworks for battery material recovery. With the rising demand for lithium-ion batteries (LIBs), efficient recycling methods are crucial for resource preservation and environmental sustainability. The study emphasizes the significance of IoT in delivering real-time information on battery use, deterioration, and end-of-life condition, hence enabling informed decision-making in recovery processes. The AdaBoost algorithm, known for its ensemble learning capabilities, is used to analyze IoTgenerated data, enhancing forecast accuracy in evaluating battery status. A comparative analysis of AdaBoost against other machine learning algorithms demonstrates its superiority in performance measures, including accuracy, precision, and F1-score. It highlights the potential of data-driven methodologies in enhancing battery material recovery operations and promoting sustainable practices within the circular economy paradigm. It provides significant insights for stakeholders in the recycling sector, informing initiatives to improve efficiency and mitigate environmental effect.