可解释的人工智能用于预测从二次资源中泄漏的稀土元素
Quang Loc Nguyen1, Huy Nguyen Lai2, Hong T M Nguyen1
1Climate Change Cluster, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Journal of hazardous materials
|August 8, 2025
概括
这项研究使用可解释的人工智能 (AI) 来优化从二次资源中提取稀土元素 (REE). 人工智能系统预测了漏效率,并确定了度等关键因素,以改善回收.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 人工智能的人工智能
背景情况:
- 全球对稀土元素 (REEs) 的需求不断增长,需要可持续的开采方法.
- 电子废物和矿山尾矿等二次资源为初级采矿提供了可行的替代方案.
- 从这些复杂的矩阵中优化REE回收的浸出过程是具有挑战性的.
研究的目的:
- 开发一个可解释的AI系统来预测REE漏效率.
- 确定影响从二次资源中提取REE的关键因素.
- 为优化泄漏条件和提高回收率提供实时建议.
主要方法:
- 在来自Web of Science数据库的572个实验数据集上训练了一个可解释的AI模型.
- 该系统预测漏效率,并为关键影响参数提供解释.
- 人工智能建议调整条件以改善REE恢复.
主要成果:
- 度被确定为影响REE漏效率的最关键因素.
- REE分类 (轻型与重型) 是第二个最有影响力的参数.
- 酸强度 (pH),含量和温度对漏性能产生中等影响 (R2=0.81).
结论:
- 可解释的人工智能有效地弥合了经验数据和REE开采过程创新之间的差距.
- 开发的AI框架提高了复杂的提取系统的决策和流程效率.
- 这种方法在REEs以外也广泛适用于其他资源密集型行业.
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