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

Tengteng Li, Xiaojun Jing, Fengbin Wang, Xiaowei Wang, Dongzhi Gao, Xianyang Cai and Bin Tang ()
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Tengteng Li: CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin 300300, China
Xiaojun Jing: CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin 300300, China
Fengbin Wang: CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin 300300, China
Xiaowei Wang: CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin 300300, China
Dongzhi Gao: CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin 300300, China
Xianyang Cai: School of Energy and Power Engineering, Dalian University of Technology, Dalian 116023, China
Bin Tang: School of Energy and Power Engineering, Dalian University of Technology, Dalian 116023, China

Energies, 2024, vol. 17, issue 14, 1-16

Abstract: 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, NO 2 , and NO x , 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, NO 2 , and NO x 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.

Keywords: emissions forecasting; off-road machinery; long short-term memory network; PEMS test data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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