在机器学习下准确预测NCM电池回收过程:机制分析和工业应用
Yanyan Hu1, Yuting Wang1, Xiaohong Zheng2
1Tianjin Key Laboratory of Clean Energy and Pollution Control, School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China.
Waste management (New York, N.Y.)
|November 27, 2025
概括
这项研究使用机器学习来预测和优化使用过的离子电池 (NCM) 的回收. 机器学习准确地预测金属炼效率,改善回收和可持续性.
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.
- 数据科学数据科学数据科学
背景情况:
- 使用过的离子电池 (NCM) 的可持续回收对行业至关重要.
- 优化NCM电池回收是具有挑战性的,因为复杂的因素和可变的原材料.
研究的目的:
- 开发一种机器学习 (ML) 策略,准确预测使用过的NCM电池回收过程.
- 为了确定影响金属炼效率的关键因素,以优化工艺.
主要方法:
- 考虑了28个输入特性,包括原材料特性,浸出试剂和操作条件.
- 分析了使用四种ML模型的Li,Ni,Co和Mn漏效率,极端梯度增强显示出最佳性能.
- 研究了酸类型,固体-液体比,速度,温度和pH对浸出效率的影响.
主要成果:
- ML策略准确地预测了在使用过的NCM电池回收中金属漏效率.
- 极端梯度增强模型在预测漏效率方面表现优于其他模型.
- 有效出水的最佳条件包括酸 (2.27mol/L),S/L比率 (48.25g/L),速度 (528rpm),温度 (55°C) 和pH (2.15).
结论:
- 基于ML的预测为优化NCM电池回收过程提供了一个强大的工具.
- 这种方法促进了大数据下电池回收的机制分析和工业应用.
- 精确预测回收过程可以提高金属回收,并促进电池行业的可持续性.
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