机器学习有助于揭示影响轮胎磨损和颗粒物排放的关键因素
Zhenyu Jia1, Jiawei Yin1, Tiange Fang1
1Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, 1st Floor, Nankai University Press, No.94 weijin Road, Nankai District, Tianjin 300071, China.
Environment international
|December 25, 2024
概括
轮胎磨损颗粒 (TWPs) 显著导致空气污染,PM2.5占PM10的65%. 机器学习确定了影响TWP排放的关键因素,提供了减少粒子生成的策略.
科学领域:
- 环境科学 环境科学
- 机械工程 机械工程
- 材料科学 是一种材料科学.
背景情况:
- 轮胎磨损颗粒 (TWPs) 是城市颗粒物的重要来源.
- 在现实驾驶条件下量化TWP的产生和确定影响因素仍然具有挑战性.
研究的目的:
- 在模拟的真实驾驶条件下开发TWPs数据集.
- 通过机器学习识别影响TWP排放的关键因素.
- 为减少TWP发电提供见解.
主要方法:
- 利用轮胎磨损模拟器和定制测试条件来生成TWP数据.
- 采用机器学习,特别是随机森林模型,分析排放响应关系.
- 应用模型可解释性技术 (相似性网络图,部分依赖图,以中心为中心的个体条件期望图) 来解释ML发现.
主要成果:
- 在TWP PM2.5中,PM10排放量约占TWP总排放量的65%.
- 随机森林模型实现了高预测准确性 (R2=0.84对于PM2.5,R2=0.78对于PM2.5-10).
- 确定了PM2.5和PM2.5-10之间的明显的排放响应关系,具有各种影响因素.
结论:
- 驾驶行为 (例如,力,扭矩),轮胎特性和跑道温度显著影响TWP排放.
- 减少劳累的驾驶 (力<400N),保持较低的跑道温度 (<45°C),并优化跑道图案,可以减轻TWP的产生.
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