Experimental and numerical consideration of the effect of CeO2 nanoparticles on diesel engine performance and exhaust emission with the aid of artificial neural network

Three sets of input elements namely, BSFC, nanoparticle addition, and engine speed were considered whereas power, NOx, HC, and CO emissions are output parameters. The results, however, indicate that 12 neurons of hidden layer, together with application of Levenberg-Marquardt training rule led to the best network performance with the least MSE value of 0.000172. According to the current investigation, the network modeling succeeded in presentation of efficient interconnecting relation between nanoparticle impact in fuel with engine power and pollutant emissions.