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

Yue Chen, Yulong Shen, Miaomiao Wen, Cunfeng Wei, Junjie Liang, Yuanqiang Li and Ying Sun ()
Additional contact information
Yue Chen: Guangxi Yuchai Marine and Genset Power Co., Ltd., Yulin 537005, China
Yulong Shen: Hainan Branch, China Classification Society, Haikou 570102, China
Miaomiao Wen: Shanghai Rules & Research Institute, China Classification Society, NO.1234, Pudong Avenue, Shanghai 200135, China
Cunfeng Wei: China Shipbuilding Power Engineering Institute Co., Ltd., Shanghai 201206, China
Junjie Liang: School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
Yuanqiang Li: Guangxi Yuchai Marine and Genset Power Co., Ltd., Yulin 537005, China
Ying Sun: School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China

Energies, 2024, vol. 17, issue 23, 1-23

Abstract: 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.

Keywords: low-speed marine engine; emission characteristics; performance simulation; machine learning; swarm intelligence (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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