Improving The Use of Biogas/Biohydrogen in Dual Fuel Engines Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN).

Growing energy demand and the need to reduce the emission of greenhouse gases have created greater interest in alternative fuels such as diesel substitutes, with biodiesel, biogas, and bio-hydrogen being rated as the viable alternatives. Biodiesel improves combustion and reduces CO and HC emissions, biogas is economically viable utilization but its efficiency is impacted by the loss resulting from the presence of CO?, and bio-hydrogen supports the development of flame, thermal efficiency, and reduces carbon-based emissions. However, issues with abnormal combustion, reduced efficiency, and high levels of NOx with high levels of substitution necessitate optimization of the parameters. In this study, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to evaluated and optimize the effect of load, compression ratio, ignition pressure, and gas flow rates on engine performance and emission. RSM outputs reported load, ignition pressure, and bio-hydrogen to have strong effects on BTE, BSFC, CO, and NOx with a maximum of 40.55% BTE, 303.48 g/kWh BSFC, 2.35 g/kWh CO, and 869.78 ppm NOx. ANN models reported a good predictive capability with R² > 0.99 and were better at predicting emission trends compared to RSM. The integration of RSM and ANN offers a highly effective tool for optimizing dual-fuel diesel engines to attain improved efficiency, improved fuel utilization, and reduced emissions for green energy use.