Transient Emissions Forecasting of Off-Road Construction Machinery Based on Long Short-Term Memory Network

Off-road machinery is one of the significant contributors to air pollution due to its large quantity. In this study, a deep learning model was developed to predict the transient engine emissions of CO, NO, NO2, and NOx, which are the main pollutants emitted by off-road machinery. A portable emission...

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Bibliographic Details
Published in:Energies (Basel) 2024-07, Vol.17 (14), p.3373
Main Authors: Li, Tengteng, Jing, Xiaojun, Wang, Fengbin, Wang, Xiaowei, Gao, Dongzhi, Cai, Xianyang, Tang, Bin
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
Air pollution
Air quality
Automotive emissions
Computational linguistics
Construction equipment
Deep learning
Emissions
emissions forecasting
Global positioning systems
GPS
Highway construction
Infrastructure (Economics)
Language processing
long short-term memory network
Natural language interfaces
Neural networks
Nitrogen
off-road machinery
Optimization
PEMS test data
Pollutants
Remote sensing systems
Road construction
Road construction industry
Roads & highways
Time series
Vehicles
Wavelet transforms
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Summary:Off-road machinery is one of the significant contributors to air pollution due to its large quantity. In this study, a deep learning model was developed to predict the transient engine emissions of CO, NO, NO2, and NOx, which are the main pollutants emitted by off-road machinery. A portable emission measurement system (PEMS) was used to measure the exhaust emission features of four types of construction machinery. The raw PEMS data were preprocessed using data compensation, local linear regression, and normalization to ensure that the data could handle transient conditions. The proposed model utilizes the preprocessing PEMS data to estimate the CO, NO, NO2, and NOx emissions from off-road machinery using a recurrent neural network (RNN) based on a long short-term memory (LSTM) model. The experimental results show that the proposed method can effectively predict the emissions from off-road construction machinery under transient conditions and can be applied to controlling the emissions from off-road construction machinery.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17143373