Artificial neural network based determination of the performance and emissions of a Diesel engine using ethanol-diesel fuel blends

Abstract

In this study, the performance and exhaust emission values in a four-stroke, four-cylinder turbocharged Diesel engine fueled with ethanol-diesel fuel blends (10% and 15% in volume) were investigated by using Artificial Neural Network (ANN) modeling. The actual data derived from engine test measurements was applied in model training, cross-validation, and testing. To train the network, fuel injection pressures, throttle positions, engine speed, and ethanol fuel blend ratios were used as input layer in the network. The outputs are the engine performance values (engine torque, power, brake mean effective pressure, and specific fuel consumption) and exhaust emissions (SO2, CO2, NOx, and smoke level (N%)) which were measured in the experiments.The back-propagation learning algorithm with three different variants, a single layer, and logistic sigmoid transfer function (log-sig) was used in the network. By using the weights of the network, formulations were given for each output. The network for test data yielded the R2 values of 0.999 and the mean % errors for test data are smaller than 3.5% for the performance and 8% for the emissions.