Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus

Meta-heuristic optimization algorithms are widely applied across various fields due to their intelligent behavior and fast convergence, but their use in optimizing engine behavior remains limited. This study addresses this gap by integrating the Design of Experiments-based Response Surface Methodology (RSM) with meta-heuristic optimization techniques to enhance engine performance and emissions characteristics using Tectona Grandi's biodiesel with Elaeocarpus Ganitrus as an additive. Advanced Machine Learning (ML) models, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Random Trees (RT), were employed for predictive analysis, with ANN outperforming RSM in accuracy. The study identified the Teak biodiesel blend (TB20) with a 5 ml Elaeocarpus Ganitrus additive (TB20 + R5) as the optimal formulation, achieving the highest Brake Thermal Efficiency and reduced Brake-Specific Fuel Consumption. Desirability analysis further confirmed the blend's superior performance and emissions characteristics, with a desirability rating of 0.9282. This work highlights the potential of hybrid optimization approaches for improving biodiesel performance and emissions without engine modifications, contributing to the advancement of sustainable energy practices in internal combustion engines.