Multi-objective optimization of CRDI engine parameters fueled with blends of diesel and sterculia foetida biodiesel: A comparative study of RSM composite desirability and meta-heuristic algorithm

• Experimentally Investigated CRDI engine fuelled with sterculia foetida biodiesel and diesel blends as fuel by varying fuel injection pressure (400 - 1000 bar, fuel injection timing (0 – 30 o BTDC), exhaust gas recirculation (0 -60%) and engine load (20-100%). • Design expert used for preparing design of experiment, totally 50 engine trials were obtained through CCD as design of experiment. Accordingly experiment also conducted and found optimum engine influencing parameters based on RSM-desirability approach. • CRDI engine operations using sterculia foetida biodiesel blends as fuel showed that the best input combinations were B53 of sterculia foetida biodiesel blends, 98% of EL, 100 MPa of FIP, FIT of 6 o bTDC, and 0% of EGR, based on a maximum RSM desirability of 0.973. • This study establishes meta-heuristic algorithm as viable alternatives for complex engineering optimization problems, offering enhanced flexibility while maintaining result accuracy comparable to established RSM methodologies. All three meta-heuristic algorithms (GADF, DE-DF, PSO-DF) successfully replicated RSM composite desirability results with 88-98% similarity. This study presents a comprehensive multi objective optimization of common rail direct injection (CRDI) engine parameters using response surface methodology (RSM) with composite desirability approach and three meta-heuristic algorithms: genetic algorithm with desirability function (GADF), differential evolution with desirability (DE-DF), and particle swarm optimization with desirability (PSO-DF). A central composite design (CCD) with 50 experimental runs was conducted to evaluate the effects of fuel blend percentage, engine load, injection pressure, injection timing, and EGR rate on engine performance and emissions. The study aimed to maximise torque, brake power, BMEP, brake thermal efficiency, mechanical efficiency, and volumetric efficiency while minimising specific fuel consumption, CO, HC, and NOx emissions. RSM composite desirability optimisation yielded optimal conditions at 53% fuel blend, 98% engine load, 999.97 bar injection pressure, 6.0 o BTDC timing, and 0% EGR. Mata-heuristic validation showed DE-DF achieving 95.3%similarity to RSM results, PSO-DF demonstrating 97.9% similarity, and GADF providing 88.9% agreement. The study validates the effectiveness of meta-heuristic algorithms as robust alternatives to traditional RSM approaches for complex engine optimization problems.