Experimental thermodynamic investigation and hybrid RSM-ANN prediction of hydrogen-enriched algal biodiesel combustion

The growing demand for low-carbon and high-efficiency combustion systems has accelerated research on renewable fuels compatible with existing compression ignition (CI) engines. This study experimentally investigated the combustion, performance and emission characteristics of a single-cylinder diesel engine fuelled with hydrogen-enriched Algal Oil Methyl Ester (AOME) using a hybrid Response Surface Methodology (RSM) and Artificial Neural Network (ANN) approach. Engine load (0 - 100%), injection pressure (200 - 240 bar), and hydrogen flow rate (3 - 9 LPM) were selected as the primary input parameters. A total of 45 experimental runs were conducted to evaluate peak cylinder pressure, heat release rate (HRR), brake thermal efficiency (BTE), brake specific fuel consumption (BSFC) and exhaust emissions. Results indicated that hydrogen enrichment significantly enhanced combustion characteristics, with peak cylinder pressure increasing from 34.75 to 79 bar and HRR rising from 29.72 to 157.62 J/ °CA at full load conditions. Maximum BTE of 44.01% and minimum BSFC of 0.141 kg/kWh were achieved under optimized conditions. Hydrogen addition also reduced CO, HC and smoke emissions by 91%, 90% and 99%, correspondingly, the NOₓ emissions increased at higher loads due to elevated combustion temperatures. The ANN model outperformed RSM, achieving prediction accuracy with R² values exceeding 0.98. Multi-objective optimization produced a desirability value of 0.958. The results prove the potential of hydrogen-enriched AOME dual-fuel operation for sustainable automotive and long-duration CI engine applications aligned with global net-zero and SDG goals.