Application of artificial neural networks for the prediction of performance and exhaust emissions in IC engine using biodiesel-diesel blends containing quantum dot based on carbon doped

Abstract

• Novel ANN based modeling of GQD on the engine performance/emissions characteristics established. • ANN model is capable for mapping engine performance-emission paradigms with high accuracy. • GQD nanoparticles as a latent ternary fuel-borne additives for diesel engine. The application of the fossil diesel in engines is detrimental to the humans as they pollute the environment. Currently, all-embracing research is in progression to discovery an alternative fuel and improve its quality. The nanoparticles are one of the current technologies that is beneficial for upgrading the fuel properties. Amongst the nanoparticles, GQD is outstanding and exhibits myriads of technical benefits for futuristic applications as an additive to curtail the emission profiles. Highlighted the suitability of newly evolved additives, which is GQD to improve the characteristics of IC engines powered with alternate fuels. The GQD is a novel development with nil dimensional illumination. Owing to its capacity to produce cheap and ecologically approachable materials. In the prevailing works, there is absence of orderly studies ANN created modelling of diesel engine’s effectiveness of IC engines powered with ternary blends of GQD, waste fish oil biodiesel (WOFB) and diesel fuel. In this study, GQD that is an innovative cohort of carbon nanotubes with the amount of 45 ppm and ethanol ranges from 2 to 8 with volume percent concentrations were joined with B10 fuel. Then, the performance and emission characteristics of a diesel engine at changed speeds of 1800, 2100 and 2400 rpm were measured also ANN was accosted to establish a prognostic model to evaluate the biodiesel yield. The output models formulated for the speeds, GQD+E and 10% Waste fish oil biodiesel +diesel (inputs) and for the performance and emission characteristics (output). Finally, the results indicated that adding GQD+E to 10%WOFB causes increase the brake power and engine torque, which B10 + E6 + GQD45 fuel had the highest brake power and engine torque. On average brake power and engine torque’s difference for the B10 + E6 + GQD45 fuel compared to the D100 is 13.84% and 16.95% respectively. Also adding GQD+E to 10%WOFB reduced BSFC, UHC, CO and NO emission, which B10 + E6 + GQD45 fuel had the lowest UHC, CO emissions. On average UHC, CO and CO 2 decreased compared to D100 21.70, 24.67 and 26.38% respectively. So can be concluded that the addition GQD+E to 10%WOFB improves performance and reduces engine emission. Also, the results for ANN showed that, both Pearson’s correlation coefficient (0.96, 0.71, 0.95, 0.92, 0.84 and 0.91 for CO 2 , NO, UHC, BSFC, brake power and engine torque, respectively) and RMSE (0.0295, 0.0227, 1.6545, 1.5046, 11.2515, 0.1557 and 0.3913 for CO 2 , NO, UHC, BSFC, brake power and engine torque respectively) for either parameter traits show the efficacy of ANN as observed from the predicted values being close to the experimental values.