Enhanced production of methane enriched biogas through intensified co-digestion process and its effective utilization in a biodiesel/biohydrogen fueled engine with duel injection strategies: ML-RSM based an efficient optimization approach

Due to stringent emission regulations, vehicle manufacturers must control diesel engine emissions. To address these regulations, this study introduces a novel approach for generating methane -enriched biogas through ultrasonic pretreatment and using it in dual fuel -mode diesel engines with a Hiptage biodiesel blend and biohydrogen. The study employs an RSM-based central composite design (CCD) approach to develop the L 54 orthogonal array for five factors and five levels of parameters. Additionally, seven different machine learning algorithms (DTR, RFR, ETR, GBR, LR, CBR, and XGBR) are used to develop a prognostic model based on the acquired experimental data. These models are used to estimate key performance indicators such as brake thermal efficiency (BTE), brake -specific fuel consumption (BSFC), carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxides (NO x ). Based on the evaluation of performance indicators, including R 2 values (0.9021 - 0.97344), RMSE (0.00203 - 26.5539), MAE (0.00114 - 23.5944), and MAPE (0.0215 - 0.04438), the GBR model establishes the most accurate predictions. Furthermore, the RSM-based desirability approach is used to evaluate the trade-off between performance and emissions, thereby identifying the optimal engine operating condition. This condition includes an engine load of 83%, a compression ratio of 18:1, a fuel injection pressure (FIP) of 243 bar, a fuel injection timing (FIT) of 26 degrees crank angle advance, and a biogas flow rate of 1.2 kg/h. Experimental investigation validates the RSM analysis findings, revealing an error margin of 7%. Overall, the introduction of methaneenriched biogas into a dual -fuel diesel engine improves combustion, resulting in increased engine performance and reduced emissions.