Prediction of transient NOX emission from a non-road diesel engine using a model combining Bayesian search and Population-based training

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成果类型：

期刊论文

作者：

Zeng, Wen;Fu, Jianqin;Zhou, Feng;Yu, Juan;Liu, Jingping;...

通讯作者：

Zhou, F

作者机构：

[Zhou, Feng; Zhou, F; Yu, Juan; Zeng, Wen] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Peoples R China.

[Fu, Jianqin; Zhou, Feng; Liu, Jingping] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China.

[Yuan, Kainan] China Machinery Int Engn Design & Res Inst Co Ltd, Changsha 410000, Hunan, Peoples R China.

通讯机构：

[Zhou, F ] C

Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Peoples R China.

语种：

英文

关键词：

Deep neural networks;Hyperparameter optimization;NO X prediction;Transient cycle;Diesel engine;Population based training

期刊：

Atmospheric Environment

ISSN：

1352-2310

年：

2024

卷：

321

页码：

120350

DOI：

10.1016/j.atmosenv.2024.120350

基金类别：

CRediT authorship contribution statement Wen Zeng: Conceptualization, Software, Validation, Writing – original draft, Investigation. Jianqin Fu: Data curation, Writing – review & editing. Feng Zhou: Conceptualization, acquisition, Project administration. Juan Yu: Formal analysis. Jingping Liu: Methodology. Kainan Yuan: Writing – review & editing.

机构署名：

本校为第一且通讯机构

院系归属：

机电工程学院

摘要：

Due to the rising environmental concerns, particularly air quality, the emission regulations for non -road mobile machinery are becoming increasingly strict. Real-time emission prediction from diesel engines is significant for emission control and regional pollution estimation. This study aims to develop a machine learning model and optimize its hyperparameters by using a hyperparameter optimization method to NOX emission. Firstly, we collected NOX emission data from test under the non -road transient test cycle (NRTC) and built a significant dataset to choose a best model. Then, the model was...

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Prediction of transient NOX emission from a non-road diesel engine using a model combining Bayesian search and Population-based training

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