Numerical investigation of the high pressure selective catalytic reduction system impact on marine two-stroke diesel engines

After-treatment systems using the selective catalytic reduction (SCR) technology have demonstrated a potential to reduce the nitrogen oxides (NOx) emissions from marine engines by more than 90% with its most typical configurations being the high pressure system (SCR-HP) and the low pressure system (SCR-LP). This study aims to investigate the impact of the SCR-HP system on a large marine two-stroke engine performance parameters by employing thermodynamic modelling. A coupled model of the zero-dimensional type is extended to incorporate the modelling of the SCR-HP system components and the control bypass valve (CBV) block. The CBV control system is modelled based on the exhaust gas minimum temperature set point, which is considered a function of the sulphur content and the exhaust receiver pressure. This model is initially validated against experimental data and subsequently employed to simulate several scenarios representing the engine operation at both healthy and degraded conditions considering the compressor fouling and the SCR reactor clogging. The derived results are analysed to quantify the impact of the SCR-HP system on the investigated engine performance. The SCR system pressure drop and the cylinder bypass valve flow cause an increase of the engine specific fuel oil consumption (SFOC) in the range 0.3 to 2.77 g/kWh. The thermal inertia of the SCR-HP system is mainly attributed to the SCR reactor, which causes a delayed turbocharger response. These effects are more pronounced at low engine loads. This study supports the better understanding of the operating characteristics of marine two-stroke diesel engines equipped with the SCR-HP system and quantification of the impact of the components degradation on the engine performance. Furthermore, it provides insights for the effective shipboard operation of these engines and the SCR-HP system

Parametric investigation of pre-injection on the combustion, knocking and emissions behaviour of a large marine four-stroke dual-fuel engine

This study aims at the parametric investigation of a large marine four-stroke dual-fuel engine in order to identify the pre-injection effects on the engine combustion, knocking and emissions parameters. A model was employed that was developed by integrating a 1-D engine model in AVL-BOOST and a 3-D CFD model in CONVERGE. The MAN 51/60DF marine engine is modelled and the simulation results were validated against experimental data. Subsequently, parametric runs for various pre-injection timings and mass ratios are performed and the simulation results are analysed and discussed. The derived in cylinder pressure oscillations at determined points are employed to calculate the knock index (KI), which was used as an evaluation indicator for the knocking intensity. A number of pre-injection strategies with varying timing and fuel mass ratios are studied. This study results reveal that a lower knock trend and NOX emissions can be achieved by early pre-injection timing and increasing pre-injection fuel mass ratio. In addition, the medium pre-injection interval increases the engine IMEP while reducing the NOX and total hydrocarbon emissions. Larger pre-injection mass ratio reduce the KI and NOX emissions, but reduces IMEP and causes the wetted-wall phenomenon. Besides, the excessive pre-injection intervals and pre-injection mass ratio result in a change in combustion mode from the conventional diesel compression ignition mode to a two-stage auto-ignition mode. This study provides a better understanding of the underlying interactions of involved parameters and proposes pre-injection solutions to improve the engine performance, emissions and knocking behaviour

Modeling and Optimization of the Flue Gas Heat Recovery of a Marine Dual-Fuel Engine Based on RSM and GA

Implementation of flue gas waste heat recovery is an effective way to improve the energy utilization of marine engines. This paper aims to model and optimize a marine four-stroke dual-fuel (DF) engine coupled with a flue gas waste heat recovery system. Firstly, the DF engine and waste heat recovery system were respectively modeled in GT-Power and Simulink environments and verified with experimental data. Then, a regression model was built using the response surface method, with the intake temperature, compression ratio, and pilot fuel injection timing as input parameters and parametric analysis was performed. Finally, multi-objective optimization of the waste heat recovery system was performed using a genetic algorithm. The result showed that the optimal solution is obtained when the intake temperature is 306.18 K, the geometric compression ratio is 14.4, and the pilot fuel injection timing is −16.68 °CA after the top dead center. The corresponding brake-specific fuel consumption was 155.18 g/kWh, reduced by 3.24%, and the power was 8025.62 kW, increased by 0.32%. At the same time, 280.98 kW of flue gas waste heat generation was obtained

Research on Fault Early Warning of Marine Diesel Engine Based on CNN-BiGRU

The normal operation of the marine diesel engine is of great significance to ensure the normal navigation of the ship. Predicting its operation state and judging whether the diesel engine is in the abnormal state in advance can guarantee the safe navigation of the vessel. In this paper, combining the feature extraction ability of the convolutional neural network (CNN) and the time series data prediction ability of the bidirectional gated recurrent unit (BiGRU), a marine diesel engine exhaust temperature prediction model is constructed. The results show that the mean square error (MSE) of the prediction model is 0.1156, the average absolute error (MAE) is 0.2501, and the average absolute percentage error (MAPE) is 0.0005336. Then, according to the residual distribution between the predicted value and the actual value of the model output and the standard deviation of the residual calculated by using the sliding window, we set the alarm threshold, where the upper limit of residual error is 1 and the lower limit is 1. The upper limit of the standard deviation is 0.604. Finally, we used the data set under abnormal conditions for experimental verification. The results show that the method can accurately determine the fault early warning of the marine diesel engine and provides a new reference for the health management of intelligent marine equipment

Emission and Performance Optimization of Marine Four-Stroke Dual-Fuel Engine Based on Response Surface Methodology

As the emissions regulations have become more stringent, reducing NOX emissions is of great importance to the shipping industry. Due to the price and emissions advantages of natural gas, the diesel-natural gas engines have become an attractive solution for engine manufacturers. Firstly, in this paper, the NOX emissions prediction model of a large marine four-stroke dual-fuel engine is built by using AVL-BOOST. In addition, the model is further calibrated to calculate the performance and emissions of the engine. Then, the influences of boost pressure, compression ratio, and the timing of intake valve closing on engine performance and emissions are analyzed. Finally, the response surface methodology is used to optimize the emissions and performance to obtain the optimal setting parameters of the engine. The results indicate that the response surface method is a highly desirable optimization method, which can save a lot of repeated research. Compared with the results from manufactured data, the power is increased by 0.55% and the BSFC, the NOX emissions, and the peak combustion pressure are decreased by 0.60%, 13.21%, and 1.51%, respectively, at low load

Performance Optimization and Knock Investigation of Marine Two-Stroke Pre-Mixed Dual-Fuel Engine Based on RSM and MOPSO

The two-stroke pre-mixed dual-fuel marine engine is prone to knocking at full load in gas mode, which affects the overall dynamic and economic performance of the engine. In this paper, the 7X82DF engine produced by Winterthur Gas & Diesel Ltd. (WinGD) was selected as the research object, aiming to investigate the effect of different parameters on engine power and knocking. Multi-objective optimizations were carried out. First, we used the one-dimensional simulation software AVL-BOOST to build the gas mode model of 7X82DF. Second, the pilot fuel start of combustion timing (SOC), the gas injection pressure, and the mass of diesel were taken as independent variables. The response surface methodology analysis of the independent variables was completed using the Design-Expert software and corresponding prediction model equations were generated. Finally, we took ringing intensity (RI) as the knock intensity evaluation index, combined with multi-objective particle swarm optimization (MOPSO) to optimize multiple-parameters to improve the overall performance and reduce combustion roughness of the engine. The optimization results showed that when the SOC was −8.36 °CA ATDC, the gas injection pressure was 20.00 bar, the mass of diesel was 14.96 g, the corresponding power was 22,668 kW, which increased by 0.68%, the brake-specific fuel consumption was 156.256 g/kWh, which was reduced by 3.58%, the RI was 4.4326 MW/m2, and the knock intensity decreased by 6.49%

Multiobjective Optimization of the Performance and Emissions of a Large Low-Speed Dual-Fuel Marine Engine Based on MNLR-MOPSO

With increasingly strict emission regulations and growing environmental concerns, it is urgent to improve engine performance and reduce emissions. In this paper, multivariate nonlinear regression (MNLR) combined with multiobjective particle swarm optimization (MOPSO) was implemented to optimize the performance and emissions of a large low-speed two-stroke dual-fuel marine engine. First, a simulation model of a dual-fuel engine was established using AVL-BOOST software. Next, a single-factor scanning value method was applied to control a range of variables, including intake pressure, intake temperature, and natural gas mass fraction. Then, a nonlinear regression model was established using the statistical multivariate nonlinear regression equation. Finally, the multiobjective optimization algorithm implementing MOPSO was used to solve the trade-off between performance and emissions. It was found that when the intake pressure was 3.607 bar, the intake temperature was 297.15 K and the natural gas mass fraction was 0.962. The engine power increased by 0.34%, the brake specific fuel consumption (BSFC) reduced by 0.21%, and the NOx emissions reduced by 39.56%. The results show that the combination of multiple nonlinear regression and intelligent optimization algorithm is an effective method to optimize engine parameter settings