Prediction of diesel engine NOx emission based on AM-CNN-LSTM model
In order to accurately control the urea injection of the selective catalytic reduction(SCR)system,the research proposes a convolutional neural network(CNN)-long short term memory(LSTM)model based on the attention mechanism(AM),and applies it to predict diesel engine NOx emissions.The relevant variables are selected based on the diesel engine NOx generation mechanism and the data collects from actual vehicle road tests.The AM-CNN model is used to extract features,and the LSTM model is used to perform the extraction on the extracted features.The results show that the hybrid model has higher prediction accuracy for NOx emissions,with less calculation time,an average absolute error of 5.307x 10-6,and a coefficient of determination of 0.932.Analyzing the key factors affecting NOx generation based on the weight of the input parameters in the prediction model can provide a reference for optimizing the diesel engine combustion process.
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