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

Abstract

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.