Integrating RSM and NSGA-III for Multiobjective Optimization of a Diesel/Methanol Dual-Fuel Engine Performance and...

Area of Science:

• Combustion Engines
• Sustainable Energy
• Environmental Science

Background:

• Diesel/methanol dual-fuel engines are vital for decarbonization but face efficiency and emission challenges, especially at low loads.
• Optimizing these engines requires understanding the interplay of fuel composition, injection strategy, and exhaust gas recirculation (EGR).

Purpose of the Study:

• To comprehensively investigate the combined effects of methanol substitution ratio (MSR), EGR, and injection strategy on engine emissions and fuel economy.
• To perform multiobjective optimization for improved performance and reduced emissions in a methanol-diesel dual-fuel engine at 25% load.

Main Methods:

• Utilized response surface methodology (RSM) and the Nondominated Sorting Genetic Algorithm-III (NSGA-III) for multiobjective optimization.
• Analyzed the impact of varying MSR, EGR rates, main injection timing (MIT), and fuel injection pressure (FIP).

Main Results:

• Increasing MSR reduced NOx and particle number (PN) but increased HC and CO. Higher EGR effectively reduced NOx but increased PN.
• Advancing MIT and increasing FIP lowered NOx and improved brake thermal efficiency (BTE), with complex effects on other emissions.
• Optimized parameters (MSR=5.23%, EGR=4.06%, MIT=-1.98° CA ATDC, FIP=90.49 MPa) achieved BTE over 33% and reduced NOx and PN emissions compared to pure diesel.

Conclusions:

• The interaction between MSR and EGR significantly influences engine performance and emissions.
• AI-driven optimization effectively enhances the performance and emission characteristics of diesel/methanol dual-fuel engines.
• Achieved simultaneous emission reduction and improved fuel efficiency, demonstrating the potential for sustainable transportation solutions.