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机器学习用于有效预测和优化从汽车催化剂中使用的白金组金属回收

Shanshan E¹, Haoxuan Yin², Yilin Yuan²

¹College of Mechanical and Electrical Engineering, Hebei Agricultural University, Hebei, Baoding 07100, People's Republic of China.

Waste management (New York, N.Y.)

|September 14, 2025

概括

机器学习模型显著改善了从汽车催化剂 (SAC) 中使用的金组金属 (PGM) 的回收. 开发的图形用户界面 (GUI) 简化了优化,降低了实验成本和环境影响.

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.
环境科学环境科学

背景情况:

使用的汽车催化剂 (SAC) 富含有价值的白金组金属 (PGM).
传统的PGM回收方法包括烤和酸浸,但面临着废物成分变化和众多参数的挑战.
为过程优化进行广泛的实验会增加成本和环境风险.

研究的目的:

应用机器学习 (ML) 来提高PGM从SAC恢复效率.
利用废物特性和工艺参数,开发PGM漏效率的预测模型.
为快速PGM恢复预测和流程优化创建一个用户友好的工具.

主要方法:

从SACs治疗中收集了18877个数据点的数据集.
利用了34个输入特征,包括废物特性和技术参数.
与四个ML算法进行了比较,其中极端梯度提升显示了最佳性能.

主要成果:

极端梯度提升实现了对PGM漏效率的优异预测性能.
功能重要性分析确定了影响PGM漏的关键参数.
开发了一个图形用户界面 (GUI) 以基于颗粒大小和废物组成的快速预测.

结论:

ML提供了一种智能和高效的战略,用于从SAC中恢复PGM.
开发的GUI最大限度地减少了对广泛的实验优化的需求.
这种方法提高了PGM回收的经济可行性和环境可持续性.

关键词:

化学洗是一种化学的洗.智能回收利用的智能回收利用金组的金属金组的金属烤的预处理方法使用过的汽车催化剂.

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