Enhancing efficiency and environmental impacts of a nano-enhanced oleander biodiesel-biohydrogen dual fuel engine equipped with EGR, through operational parameter optimization using RSM-MOGA technique

This study investigates the enhancement of combustion efficiency in a dual-fuel diesel engine powered with oxyhydrogen as the primary fuel and a Graphene oxide nanoparticle-dispersed biodiesel/diesel blend as the pilot fuel. A Box-Behnken design (BBD) is employed to develop an L 17 orthogonal array for three factors and three levels of input parameters: engine load, HHO flow rate, and EGR ratio. An RSM-based 3D surface plot is utilized to analyze the interaction between input parameters and responses. Multi-objective optimization through RSMbased desirability and genetic algorithms (MOGA) is conducted to obtain the optimal engine operating parameters. The MOGA approach demonstrates superior performance compared to the RSM-based desirability approach. The optimal engine operating parameters identified by the MOGA approach are an engine load of 9.89 kg (approximately 3.43 kW), an HHO flow rate of 5.78 L/min, and an exhaust gas recirculation (EGR) ratio of 10.18%. At this optimized operating condition, the performance and combustion output are 31.85% BTE, 0.3268 kg/kWh of BSFC, and 60.16 bar of peak cylinder pressure (PCP). In terms of emissions, carbon monoxide (CO) is 0.04152 vol %, hydrocarbon (HC) is 25.45 ppm, and nitrogen oxide (NO x ) is 746.12 ppm. Experimental validation confirms that all results are within an acceptable limit with a maximum error of 6%. Oxyhydrogen significantly enhances fuel efficiency and consumption while simultaneously reducing carbon-based emission levels, except for nitrogen oxide emissions. The adverse effects of an increase in NO x are mitigated by the addition of EGR.