Engine performance and emission optimization with waste cooking oil biodiesel/diesel blend using ANN and RSM techniques coupled with ACKTR-DE and HHO algorithms

In this experimental investigation, Artificial Neural Network (ANN) and Response Surface Methodology (RSM) model structures were constructed to predict and optimize the performance and exhaust emissions of a diesel engine operating on a blend of diesel fuel and waste oil biodiesel. The test engine was operated with 0%, 50%, and 100% biodiesel content under varying injection pressures and loads. The RSM model was used to derive regression equations from the experimental results. The correlation coefficient (R²) for all responses of the constructed model ranged from 0.9785 to 0.9997. By applying the developed model, the brake thermal efficiency (BTE) response was optimized to its maximum value, while all other responses were minimized. All responses were predicted using an ANN model with R > 0.99 and a maximum mean absolute error (MAAE) of 1.723%. RSM-based optimization analysis was applied to the design of experiments (DOE). At an injection pressure of 180 bar, an engine torque of 3.846 Nm, and a 100 percent biodiesel ratio, optimal diesel engine performance characteristics, the lowest exhaust emissions, and the lowest specific fuel consumption values were achieved. In addition, the RSM approach performed satisfactorily, with a desirability value of 0.750. The RSM regression equations were assessed using the Actor Critic with Kronecker-Factored Trust Region-Differential Evolution (ACKTR-DE) and Harris Hawks Optimization (HHO) algorithms. The outcomes derived from the ACKTR-DE and HHO algorithms corroborated the results obtained from the RSM. Furthermore, verification experiments were conducted to confirm the optimal results, thus demonstrating that the combined use of RSM, ANN, and advanced algorithms offers a robust and accurate framework for optimizing biodiesel engine performance.