Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network

In this study, the cyclic variability of a diesel engine using diesel fuel and butanol-diesel fuel blends is modeled using an artificial neural network (ANN) method. The engine was operated with ten different engine speeds and full load conditions using six different n-butanol-diesel fuel blends. The coefficient of variation (COV) of the indicated mean effective pressure (IMEP), which is a well-accepted evaluation method, was used to assess the cyclic variability for 100 sequential engine cycles. Results indicated that adding n-butanol to diesel fuel caused an increase. Moreover, the COVimep values exhibited a decreasing trend with an increase in the engine speed for each fuel. The experimental results were used to train the ANN. The ANN network was trained with Levenberg - Marquardt (LM) and Scaled Conjugate Gradient (SCG) algorithms. After training the ANN, it was found that the coefficient of determination (R2) values were in the range of between 0.737 and 0.9677, the mean-absolute-percentage error (MAPE) values were smaller than 8.7 and the mean-square error values (MSE) were smaller than 0.042. The predictions of the developed ANN model showed reasonable consistency with the experimental results.