Prediction of the NOx emissions from experimental heavy-duty engine using optimized Sparrow search algorithm combined with back propagation neural network - Pan, Xiubin; Guan, Wei; Gu, Jinkai; Sang, Hailang; Wang, Hui; Zhou, Shengkai; Liu, Xing (Journal of environmental chemical engineering, 2025)
It is crucial to control Nitrogen oxide (NOx) emissions from diesel engines as its seriously affects on environment and human health. However, measuring NOx emissions through experiments is not only consumes a huge amount of human, financial and time costs, but also prolongs engine development cycle. Therefore, this paper combines experimental data and predictive modeling to study NOx emissions. Firstly, a large amount of engine operating and emissions data was collected through testing on a heavy-duty diesel engine. Secondly, Pearson and Spearman correlation analysis methods are used to select seven most important variables as inputs to reduce the dimension of data. Whereafter, the back propagation neural network (BPNN) model optimized by Sparrow Search Algorithm (SSA) is used to predict the NOx emissions. Results show that the coefficient of determination (R2) of the optimized model for the training and testing sets were 0.99025 and 0.98812, respectively, which verified the good robustness of prediction model. Finally, comparing to six other machine learning models: BPNN, TCN, ELM, GRU, RF, and XGBOOST in predicting NOx emissions, the SSA-BPNN not only exhibited excellent generalization ability but also achieved lower prediction errors. This indicates that SSA-BPNN is a promising method for accurate NOx emissions prediction, and could be an effective means to alleviate the tedious and expensive experimental testing work. •Engine parameters and NOx correlation was analyzed using PCC and SCC.•The performance of the proposed prediction model was evaluated systematically.•Comparison of the prediction performance of seven ML methods for NOx emissions.•The SSA- BPNN model plays a crucial role in NOx emission prediction.