Efficient optimization of DF engine emissions and fuel consumption via XGBoost and MOEA/D

Abstract To explore and improve the economic efficiency and nitrogen oxide (NOx) emissions of marine natural gas-diesel dual-fuel engines, this study proposes a performance optimization method integrating machine learning and multi-objective optimization based on bench tests and simulation models. Firstly, a GT-Power simulation model was established and calibrated. Four machine learning models were developed and compared, with the XGBoost model demonstrating superior prediction accuracy and generalization ability, achieving average R 2 values of 0.9979 and 0.9874 for the training and testing datasets, respectively. Subsequently, the MOEA/D algorithm was employed for multi-objective optimization of dual-fuel engine performance. The results indicate that MOEA/D exhibited excellent optimization performance under all load conditions, especially at high loads, effectively balancing fuel consumption (BSFC) and nitrogen oxide emissions (BS-NOx). CV-TOPSIS decision-making resulted in 17.63%–28.58% BSFC reduction and 26.8%–69.14% BS-NOx abatement across load conditions. The proposed methodology improved computational efficiency by over 10× compared to conventional simulation, providing an effective solution for low-carbon operation of marine dual-fuel engines.