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

Autor: Tengteng Li, Xiaojun Jing, Fengbin Wang, Xiaowei Wang, Dongzhi Gao, Xianyang Cai, Bin Tang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Energies, Vol 17, Iss 14, p 3373 (2024)
Druh dokumentu: article
ISSN: 17143373
1996-1073
DOI: 10.3390/en17143373
Popis: 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.
Databáze: Directory of Open Access Journals
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