Multi-response optimization of a microalgae-spirulina-fueled VCR diesel engine: a comprehensive RSM-GA approach

With the global proliferation of industrialization, it is increasingly imperative to mitigate detrimental pollution, particularly in the transportation sector. This study aims to address these concerns by comprehensively investigating the influence of engine input parameters on the performance and emissions of a variable compression ratio diesel engine operating on microalgae biodiesel–diesel blends. To achieve this objective, a meticulously designed four-level, three-factor L₁₆ orthogonal array was employed to construct a statistical model. Subsequently, the response surface methodology (RSM) desirability approach, along with a multi-objective optimization genetic algorithm (GA) approach, was employed to optimize the model and determine the optimal engine input parameters. The investigation revealed that the parametric combination obtained through the GA outperformed the RSM approach. The optimized engine responses were attained at the following input parameter settings: 99.70% engine load, 16.85 compression ratio, and a blend ratio of B20 (20% biodiesel and 80% diesel). At these optimal conditions, the engine exhibited remarkable performance and emissions characteristics. Specifically, the corresponding optimal engine responses for brake thermal efficiency, brake-specific fuel consumption, carbon dioxide, particulate matter, and nitrogen oxides were observed as 33.85%, 281.27 g/kWh, 870 g/kWh, 0.811 g/kWh, and 2074 ppm volume, respectively. Furthermore, experimental validation of the model demonstrated a close agreement between the predicted values and the actual experimental results. These findings substantiate that the utilization of biodiesel–diesel blends can yield substantial environmental benefits and offer a viable alternative to conventional diesel fuel.