To improve the pollutant removal efficiency by non-thermal plasma (NTP), the effect of H 2 O vapor and O 2 on removal efficiency of NO and SO 2 as well as the reduction of CO was investigated in H 2 O/O 2 /SO 2 /NO/C 3 H 6 /CO 2 /N 2 system. To analyse the reaction mechanism, the effects of H 2 O vapor and O 2 on the emission intensity of Oð3p 5 P → 3s 5 S 0 2 Þ and OHðA 2 ∑ þ → X 2 ∏Þ were investigated. The experimental results show that the increase of H 2 O vapour (0%-9.8%) promotes the generation of OH radicals, increases the removal efficiency of NO from 18.9% to 57.3%, decreases the energy per NO removed from 449.0 to 148.1 eV/NO, increases SO 2 removal efficiency from 4.8% to 35.3%, decreases the energy per SO 2 removed decreases from 2784.0 to 582.9 eV/SO 2 , and reduces the generation of CO from 460 to 229 ppm. In the range of 0%-15%, the increase of O 2 content promotes the formation of O radicals, increases the removal efficiency of NO from 8.5% to 54.2%, and decreases the energy per NO removed from 994.3 to 156.4 eV/NO. In the range of 0%-5%, increasing O 2 content promotes SO 2 removal efficiency from 8.7% to 24.8%, and decreases the energy per SO 2 removed from 2485.7 to 876.6 eV/SO 2 . However, CO generation increases from 200 to 350 ppm with the increase of O 2 in 0%-5% due to the incomplete oxidation of C 3 H 6 . In the range of 5%-15%, the increase of O 2 produces more O radicals, decreases the removal efficiency of SO 2 from 24.8% to 23.5% and the generates of CO from 350 to 311 ppm. This study is helpful for improving the efficiency of NTP for desulfurization and denitrification, while reducing the by-product CO.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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To improve NO oxidation and energy efficiency, the effect of dielectric barrier discharge reactor structure on NO oxidation was studied experimentally in simulated diesel exhaust at atmospheric pressure. The mixture of 15% O2/N2 (balance)/860 ppm NOX (92% NO + 8% NO2) was used as simulated diesel exhaust. The results show that DBD reactor with 100-mm electrode length has the highest oxidation degree of NOX and energy efficiency. NO oxidation efficiency is promoted and the generation of NO is inhibited significantly by increasing the inner electrode diameter. Increasing the inner electrode diameter not only improve the E/N, but also makes the distribution of E/N more concentrated in the gas gap. The secondary electron emission coefficient (γ) of electrode material is closely related to electron energy and cannot be considered as a constant, which causes the different performance of electrode material for NO oxidation under different gas gap conditions. Compared with the rod electrode, the screw electrode has a higher electric field strength near the top of the screw, which promotes the generation of N radicals and inhibits the generation of O radicals. Rod electrode has a higher NO oxidation and energy efficiency than screw electrode under oxygen-enriched condition.

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%.

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.