Combined Effect of Operating Parameters and Nanoparticles on the Performance of a Diesel Engine: Response Surface Methodology-Coupled Genetic Algorithm Approach

Abstract

This paper’s goal is to ascertain the optimum input parameters and nanoparticle concentrations for least emission and better performance by utilizing the genetic algorithm (GA) and response surface methodology (RSM) in a single-cylinder diesel engine running with 20% blend of biodiesel derived from Manilkara zapota seeds. Experiments to be conducted on the engine were designed with a central composite design (CCD) with input parameters of loads (20–100%), nanoparticle concentrations (NPCs, 0–80 ppm), compression ratios (CRs, 16.5–18.1), injection pressures (IPs, 190–230 bar), and injection timings [ITs, 17–29° bTDC (before top dead center)], and the engine response was recorded. The comparative analysis of optimization tools RSM and GA was employed for finding the ideal setting of engine input parameters and nanoparticle concentrations based on the maximization of performance [brake thermal efficiency (BTE) and brake-specific fuel consumption (BSFC)] and minimization of emissions [(hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxides (NOx)]. The best result was obtained by the RSM method. The optimized input parameters were recorded at a load of 59.36%, an NPC of 80 ppm, a CR of 18.1, an IP of 192.02 bar, and an IT of 18.62° bTDC. At these optimized settings, the performance and emissions were 32.4767% BTE, 0.1905 kg/kW h BSFC, 26.8436 ppm HC, 0.0272% CO, and 83.854 ppm NOx emissions from the engine. The developed model was validated through a confirmatory experiment, and the prediction error was within 8%. Thus, the applied model is appropriate for improving the engine’s emission and performance attributes.