Transfer learning for transient NOx, PN and THC emission prediction of non-road diesel engines based on NRTC experiments

Q: What type of study is this?

This is a Quantitative Study study.

Wen Zeng; Haiyi Wang; Jianqin Fu; Feng Zhou; Tao Wen; Kainan Yuan; Xiongbo Duan

doi:10.1039/d5em00321k

January 1, 2025Environmental Science Processes & Impacts

Transfer learning for transient NOx, PN and THC emission prediction of non-road diesel engines based on NRTC experiments

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Key Points

The transfer learning framework significantly improves prediction accuracy of transient emissions in diesel engines, and eliminates the need for extensive retraining.
Emissions including NOx, PN, and THC were predicted with improved reliability using this innovative approach based on NRTC experiments.
This assessment utilized transfer learning methods to enhance model performance across various diesel engine scenarios.
The findings highlight that transfer learning can streamline emission prediction processes in non-road diesel engines, potentially reducing costs.

Implication

Novel transfer learning framework predicts NOx, PN, and THC emissions in diesel engines, suggesting reduced model re-optimization.

Abstract

This study introduces a novel task transfer learning framework for predicting transient emissions (NOx, PN, THC) in non-road diesel engines. Our key innovation lies in eliminating model re-optimization through a...

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Cite This Study

Zeng et al. (2025) studied this question.

synapsesocial.com/papers/68d44a4731b076d99fa53ed6 https://doi.org/10.1039/d5em00321k

View Full Paper

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Transfer learning for transient NOx, PN and THC emission prediction of non-road diesel engines based on NRTC experiments

Key Points

Implication

Abstract

Discussion

Cite This Study

Transfer learning for transient NOx, PN and THC emission prediction of non-road diesel engines based on NRTC experiments

Key Points

Implication

Abstract

Discussion

Cite This Study

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