关于NdFeB废物的可持续回收利用的审查:方法,挑战和机器学习 (ML) 的整合
Rehan Ullah1, Jason Daza1, Asma Wederni1
1Department of Physics, Universitat de Girona, P2, EPS, Campus Montilivi s/n, 17003 Girona, Spain.
Materials (Basel, Switzerland)
|February 13, 2026
概括
回收新铁永久磁铁 (NdFeB-PMs) 对稀土元素 (REE) 供应至关重要. 机器学习优化了回收过程,以实现经济和环境的可持续性.
科学领域:
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 电子,能源和汽车行业对NdFeB-PMs的需求不断增长,造成了重大处置挑战.
- NdFeB-PMs富含稀土元素 (REEs),供应链中断和贸易政策影响全球可用性.
- 中国在REE出口中占主导地位,需要采用替代的,可持续的采购策略.
研究的目的:
- 系统地审查目前的NDFEB-PM回收技术,重点关注它们的工业,经济和环境影响.
- 探索机器学习 (ML) 在REE回收中的整合,特别是在漏过程中.
- 确定挑战,并为大规模,可持续的NdFeB回收提出解决方案.
主要方法:
- 对现有的循环回收方法进行全面审查:短环,水金属,金和混合工艺.
- 机器学习应用在优化漏效率和选择性REE回收方面的分析.
- 对评估工业能力,经济可行性和环境影响的不同方法的比较研究.
主要成果:
- 目前的回收方法面临着大规模实施的挑战,原因是废物流的碎片化和监管漏洞.
- ML集成显示了增强漏效率和选择性回收REEs的潜力,提高了经济可行性.
- 技术创新,如自动拆卸和立法措施 (例如,EPR) 对于克服实施障碍至关重要.
结论:
- 可持续的NdFeB回收需要一个多方面的方法,结合技术创新,环境考虑和支持性政策.
- 基于ML的回收程序为REE回收和资源可用性提供了优化,环保的解决方案.
- 集体努力至关重要,以建立有效和环保的 NdFeB 废物回收系统.
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