Machine learning-guided rare earth recovery from NdFeB magnet waste: Model development, parameter influence analysis
Boyang Xu1, Shanshan E2, Jia Liu3
1Key Laboratory of Farmland Ecological Environment of Hebei Province, College of Resources and Environmental Science, Hebei Agricultural University, Hebei, Baoding, 071000, People's Republic of China.
Abstract:
The rapid expansion of the electric vehicle industry has resulted in substantial production of NdFeB magnet wastes from discarded electromotors. These magnets, weighing up to 2 kg in each electromotor, contain 25-35 wt% of strategic rare earth elements (REEs) such as Nd, Pr, and Dy, and their efficient recycling is crucial for sustainable development and environmental protection. Traditional methods for REEs recovery from NdFeB waste, involving oxidizing calcination and acid leaching, require extensive optimization due to waste variability and technological complexities, leading to high costs and environmental risks. Meanwhile, the influence rules of multi-parameters on REEs leaching are complex to comprehensively revealed by the traditional methods. To address these bottlenecks, this study employs machine learning for intelligent REEs recovery from NdFeB waste, bypassing numerous optimization experiments and reveal the complex influencing mechanisms of multi-parameters on REEs leaching. Based on a dataset of 9650 records, the developed model incorporates 24 input features related to waste properties and technological parameters, with 5 outputs corresponding to Nd, Pr, Dy, Co, and Fe leaching efficiencies. Four algorithms were used to develop 20 models to compare their performance. The XGBoost algorithm exhibited the highest prediction accuracy, with R2 values of 0.80-0.99 in the training, test, validation, and 5-fold cross-validation sets. Furthermore, the intricate influencing mechanisms of waste properties, calcination, and acid-leaching parameters on REEs leaching rates was comprehensively elucidated. Finally, a graphical user interface was developed to guide efficient REEs leaching from NdFeB waste and some experiments were conducted to verify its reliability. This study can skip numerous optimization experiments and improve the optimization efficiency, which achieves efficient and intelligent REEs recycling.
