Multi-Objective Optimization of Fuel Consumption and Emissions in a Marine Methanol-Diesel Dual-Fuel Engine Using an Enhanced Sparrow Search Algorithm

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.