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针对优先污染物的基于深度学习的选方法:关于退役电池回收的案例研究

Xuchao Liu¹, Jing Zhao², Guohua Zhang¹

¹School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China.

Environmental pollution (Barking, Essex : 1987)

|July 19, 2025

概括

这项研究引入了一个深度学习框架,以优先考虑退役电池回收的污染物. 新的McA模型准确地识别和排名环境风险,指导更安全的回收实践.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
计算机科学计算机科学

背景情况:

越来越多的退役电池在回收过程中会给环境带来风险.
迫切需要有效的方法来识别和评估这些与回收相关的环境危害.

研究的目的:

开发和验证一种新的选框架,用于在退役电池回收中优先考虑污染物.
应用深度学习和层次聚类来进行准确的风险评估.

主要方法:

使用五种基于绩效权重的深度学习方法构建一个综合污染物选模型 (McA).
在污染物分类中应用层次聚类分析.
使用夏普利添加式扩展 (SHAP) 进行风险因素可视化.

主要成果:

与传统机器学习相比,McA模型的准确性和可靠性显著提高 (R2 = 0.9999).
确定了13种来自退役电池回收的污染物,分为四个优先级 (I-IV).
SHAP分析揭示了关键影响因素:急性毒性,刺激/腐蚀性和内分泌干扰.

结论:

开发的框架为退役动力电池回收提供了有效的污染物优先级.
深度学习方法显示了提高电池回收中的污染物选和风险管理的巨大潜力.
结果为开发更安全,更可持续的电池回收过程提供了洞察力.

关键词:

深度学习是一种深度学习.一个层次的集群.机器学习是机器学习.优先进行查.退役的电源电池已经退役.

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