基于NRTC实验的非道路柴油发动机的暂时NOx,PN和THC排放预测转移学习
Wen Zeng1, Haiyi Wang1, Feng Zhou1
1College of Mechanical and Intelligent Manufacturing, Central South University of Forestry and Technology, Changsha, 410004, China.
Environmental science. Processes & impacts
|September 29, 2025
概括
本研究提出了一个新的转移学习框架,用于预测柴油发动机排放量 (NOx,PN和THC). 该方法显著降低了计算成本,同时保持了高预测准确度,超过了传统方法.
科学领域:
- 环境科学与工程环境科学与工程
- 机械工程 机械工程
- 数据科学和机器学习
背景情况:
- 准确预测非公路柴油发动机的短暂排放对于环境遵守和发动机优化至关重要.
- 传统方法通常需要对新的排放预测任务进行广泛的再优化,从而导致高计算成本.
- 现有的模型可能无法始终满足操作传感器和实验室级设备的准确性要求.
研究的目的:
- 引入一个新的任务转移学习框架,用于预测非公路柴油发动机中的氧化 (NOx),颗粒量 (PN) 和总碳化合物 (THC).
- 通过保留预训练过的超参数和仅微调特定任务的层来消除对模型重新优化的需求.
- 为了验证框架的准确性,效率和性能与传统方法和现有的传感器技术相比.
主要方法:
- 开发一个固定架构的转移学习框架.
- 通过微调特定任务层来保存预训练过的超参数.
- 在各种排放转移场景中使用非道路过渡周期 (NRTC) 数据进行验证.
主要成果:
- 实现了与完全预训练模型几乎相同的精度,R2差异≤0.0044.
- 达到了NOx的峰值R2值98.87%,PN的99.54%,THC的99.52%.
- 与传统方法相比,计算成本降低了72%,超过了运营车辆传感器的精度,并与实验室精度相匹配.
结论:
- 开发的转移学习框架对于非公路柴油发动机的短暂排放预测非常有效.
- 固定架构方法提供了显著的计算节省,而不会影响预测准确性.
- 这项研究为组织和利用预先训练的模型进行排放预测任务建立了一个有效的范式.
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