Prediction of Pollutant Emissions from a Low-Speed Marine Engine Based on Harris Hawks Optimization and Lightgbm

With the rapid development of data science, machine learning has been widely applied to research on pollutant emission prediction in internal combustion engines due to its excellent responsiveness and generalization ability. This article introduces Lightgbm (LGB), which belongs to ensemble learning, to predict the pollutant emissions from a low-speed two-stroke marine engine. The dataset used to train LGB was derived from a one-dimensional performance simulation model of the engine, which was rigorously verified for its reliability by experimental data. To further improve the forecast performance of the LGB model, we used Harris Hawks Optimization (HHO) to automatically optimize the hyperparameters of the model, and finally, we analyzed the importance of the model features. The results show that changes in engine control parameters have significant influences on NOx and soot emissions from the engine, which can serve as the basis for the selection of the LGB model features; the LGB model was able to accurately predict pollutant concentrations from the engine with much higher accuracy than a single decision tree (DT) model; combining with HHO, the predictive ability of the LGB model was significantly improved, such as for the validation set prediction results, the mean absolute error (MAE) was reduced by about 20%, the mean squared error (MSE) was reduced by about 30%, and the coefficient of determination (R 2 ) was increased by about 0.005; and the importance analysis of the model features indicated that the combustion condition of the fuel was highly correlated with the generation of the pollutants, and the fuel injection phases can be adjusted in practice to achieve highly efficient and low-emission processes of combustion. The results of this study can provide references for the development of a new generation of highly efficient and low-pollution marine engines.