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.

Driven by the shipping industry’s pressing need to reduce its environmental impact, methanol has emerged as a promising marine fuel. Methanol-diesel dual-fuel (DF) engines present a viable solution, yet their optimization is challenging due to complex, nonlinear interactions among operational parameters. This study develops an integrated simulation and data-driven framework for multi-objective optimization of a large-bore two-stroke marine DF engine. We first establish a high-fidelity 1D model in GT-POWER, rigorously validated against experimental data with prediction errors within 10% for emissions (NOx, CO, CO₂) and 3% for performance indicators. To address computational constraints, we implement a Polynomial Regression (PR) surrogate model that accurately captures engine response characteristics. The innovative Triple-Adaptive Chaotic Sparrow Search Algorithm (TAC-SSA) serves as the core optimization tool, efficiently exploring the parameter space to generate Pareto-optimal solutions that simultaneously minimize fuel consumption and emissions. The Entropy-Weighted TOPSIS (E-TOPSIS) method then identifies the optimal compromise solution from the Pareto set. At 75% load, the framework determines an optimal configuration: methanol substitution ratio (MSR) = 93.4%; crank angle at the beginning of combustion (CAB) = 2.15 °CA; scavenge air pressure = 1.70 bar; scavenge air temperature = 26.9 °C, achieving concurrent reductions of 7.1% in NOx, 13.3% in CO, 6.1% in CO₂, and 4.1% in specific fuel oil consumption (SFOC) relative to baseline operation.