Black Carbon Emission Prediction of Diesel Engine Using Stacked Generalization

With the continuous growth of international maritime trade, black carbon (BC) emissions from ships have caused great harm to the natural environment and human health. Controlling the BC emissions from ships is of positive significance for Earth's environmental governance. In order to accelerate the development process of ship BC emission control technologies, this paper proposes a BC emission prediction model based on stacked generalization (SG). The meta learner of the prediction model is Ridge Regression (RR), and the base learner combines four models: Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), Random Forest (RF), and Support Vector Regression (SVR). We used mutual information (MI) to measure the correlation between combustion characteristic parameters (CCPs) and BC emission concentration, and selected them as the features of the prediction model. The results show that the CCPs have a strong correlation with the BC emission concentration of the diesel engine under different working conditions, which can be used to describe the influence of the changes to the combustion process in the cylinder on the BC generation. The introduction of the stacked generalization method reconciles the inherent bias of various models. Compared with traditional models, the fusion model has achieved higher prediction accuracy on the same datasets. The research results of this paper can provide a reference for the research and development of ship black carbon emission control technologies and the formulation of relevant regulations.