基于机器学习的潜在预测和合关系,揭示了从耗尽的自动排气催化剂中回收金组金属的潜在预测和合关系
1School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, People's Republic of China.
Journal of environmental management
|June 25, 2024
概括
中国中国中国中国.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 大规模生产废弃的自动排气催化剂 (SAC) 带来了环境挑战.
- SAC为回收金组金属 (PGM) 提供了一个宝贵的机会.
研究的目的:
- 用机器学习预测中国地区SAC的产生.
- 估计从SAC回收PGM的经济和碳排放减少潜力.
主要方法:
- 机器学习 (ML) 模型是使用人口-GDP-GDPII-GDPIII和随机森林开发的.
- 在五个共享社会经济路径 (SSP) 场景中应用了预测建模.
- 环境影响评估,以确定最佳的低碳回收解决方案.
主要成果:
- 预计到2050年,SACs的产量将达到2815万套 (SSP1).
- 据估计,到2050年,PGM回收利用的经济潜力为8.9亿美元 (SSP1).
- 一种被认为是最好的低碳解决方案的捕获丰富电沉积净化工艺.
- 通过铜捕获的综合回收可以在2050年之前在中国减少151万的碳排放 (SSP1).
结论:
- 机器学习为SACs生成和PGM回收潜力提供了准确的预测.
- 低碳回收过程对于最大限度地提高环境和经济效益至关重要.
- 这些发现为PGM回收,环境管理和SAC回收计划的选择提供了指导.
相关概念视频
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