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    • Prediction Model for Transient NOx Emission of Diesel Engine Based on GA-Long Short Term Memory（LSTM） Neural Network
    DOI：10.13949/j.cnki.nrjgc.2022.01.002
    Key Words:diesel engine  transient  NOx prediction  long short term memory（LSTM） neural network  genetic algorithm（GA）
    Author NameAffiliationE-mail
    YANG Rong* Guangxi Key Laboratory of Manufacturing System ＆ Advanced Manufacturing Technology， School of Mechanical Engineering， Guangxi University， Nanning 530004， China yangrong0907@163.com 
    YANG Lin Guangxi Key Laboratory of Manufacturing System ＆ Advanced Manufacturing Technology， School of Mechanical Engineering， Guangxi University， Nanning 530004， China  
    TAN Shenglan Guangxi Key Laboratory of Manufacturing System ＆ Advanced Manufacturing Technology， School of Mechanical Engineering， Guangxi University， Nanning 530004， China  
    ZHANG Song Guangxi Yuchai Machinery Company Limited， Yulin 537005， China  
    HUANG Wei Guangxi Key Laboratory of Manufacturing System ＆ Advanced Manufacturing Technology， School of Mechanical Engineering， Guangxi University， Nanning 530004， China  
    HUANG Junming* Guangxi Key Laboratory of Manufacturing System ＆ Advanced Manufacturing Technology， School of Mechanical Engineering， Guangxi University， Nanning 530004， China jmhuangmail@163.com 
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    Abstract:In order to realize the accurate control of the urea injection volume by selective catalytic reduction（SCR）， a set of NOx emission prediction model for diesel engine in transient mode was constructed and evaluated. The model mainly adopted the long short term memory（LSTM） neural network algorithm， and was optimized by genetic algorithm（GA）. According to the transient operation characteristics of diesel engine， the correlation analysis was carried out by selecting the main influencing factors of NOx emission， and the input variables of the model were determined. Then， in order to avoid the adverse effect of artificial selection parameters on the prediction performance of the neural network， the structural parameters of the LSTM neural network were optimized by genetic algorithm， and the GA-LSTM model for forecasting transient NOx emission of diesel engine was established. Finally， the performance of the model was tested. The results show that the model has good predictive ability.
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