Experimental and Modelling Study on the Performance of an SI Methanol Marine Engine Under Lean Conditions

This study presents the experimental and modelling investigation of the performance of an SI methanol marine engine operating under lean conditions. The effects of spark timing and excess air ratio on combustion characteristics, engine performance, and emissions are explored. Multiple machine learning models, including Support Vector Machines (SVM), Artificial Neural Network (ANN), LightGBM, and Random Forest (RF), are employed to predict the engine performance and emission characteristics. Experimental results show that as spark timing advances, the combustion phase advances, with the burn duration being extended. When the excess air ratio is less than 1.35, there exists an optimal spark timing, corresponding to a maximum brake thermal efficiency. The optimal spark timing exhibits an advancing tendency along with increasing excess air ratio. HC emission is primarily determined by the excess air ratio and shows no significant variation under the different spark timings. NOx emission is initially increased and then decreased with advancing spark timing. Compared with ANN, LightGBM, and RF, SVM demonstrates a superior predictive accuracy, with R2 values for engine performance exceeding 0.98 and R2 values for emissions above 0.92.