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.

In-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; various uncertain factors will bring errors to test results, and the time of an engine test is so long that the test results cannot meet the real-time requirement. Therefore, it is necessary to develop technology with high accuracy and a fast response to predict the in-cylinder pressure of diesel engines. In this paper, the in-cylinder pressure values of a high-speed diesel engine under different conditions are used to train the extreme gradient boosting model, and the sparrow search algorithm—which belongs to the swarm intelligence optimization algorithm—is introduced to optimize the hyper parameters of the model. The research results show that the extreme gradient boosting model combined with the sparrow search algorithm can predict the in-cylinder pressure under each verification condition with high accuracy, and the proportion of the samples which prediction error is less than 10% in the validation set is 94%. In the process of model optimization, it is found that compared with the grid search method, the sparrow search algorithm has stronger hyper parameter optimization ability, which reduces the mean square error of the prediction model by 27.99%.

One viable technical approach for achieving hydrogen-blended combustion in marine ammonia-fueled engines is to utilize online ammonia decomposition to produce hydrogen, which is then introduced into the engine for combustion. This work carried out ammonia decomposition experiments using various catalysts, examining the effects of temperature and space velocity on Ru/Ce_0.33Zr_0.58La_0.03Nd_0.03Pr_0.03O_2.09 and Ni/Ce_0.36Zr_0.64O₂ catalysts. Based on the experimental data obtained, the kinetic parameters of ammonia decomposition were fitted using four different models: mass action law, first-order reaction, Langmuir, and Temkin–Pyzhev kinetics across two catalysts, with the subsequent mechanistic analysis of catalytic reaction processes within the reactor. The results revealed that the NH₃ conversion rate of the Ru/Ce_0.33Zr_0.58La_0.03Nd_0.03Pr_0.03O_2.09 catalyst was superior to that of the Ni/Ce_0.36Zr_0.64O₂ catalyst, with temperature activity windows of 250–450 °C and 400–600 °C, respectively. Within the range of 2000–32,000 mL·g⁻¹·h⁻¹), an increase in space velocity led to a decrease in NH₃ conversion rate by approximately half. All four models were able to predict NH₃ conversion rates for the different catalysts with reasonable accuracy. The activation energies for Ru/Ce_0.33Zr_0.58La_0.03Nd_0.03Pr_0.03O_2.09 and Ni/Ce_0.36Zr_0.64O₂ catalysts were found to be 37.7 kJ·mol⁻¹ and 66 kJ·mol⁻¹, respectively. Targeting hydrogen requirements of 10–40% vol for ammonia engines, the corresponding catalytic temperatures for Ru/Ce_0.33Zr_0.58La_0.03Nd_0.03Pr_0.03O_2.09 and Ni/Ce_0.36Zr_0.64O₂ were above 267 °C and 500 °C, respectively.

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 (R²) 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.