Cetane index prediction of ABE-diesel blends using empirical and artificial neural network models

Recent developments in internal combustion engines have heightened the need for alternative biofuel. In the last five years, acetone-butanol-ethanol (ABE) has been extensively studied as a promising biofuel. However, the detailed investigation of its fuel properties has not been performed. One of the vital fuel properties is the cetane index. It is used to define the ignition quality of fuel, but its determination is painstaking and expensive. No previous study has utilized both empirical mathematical and ANN models to predict the cetane index of ABE-diesel blends. This study aims to predict ABE's cetane index by comparing five empirical mathematical models with seven artificial neural networks (ANN) training algorithms. To the best of our knowledge, this is the first study to examine the cetane index of ABE-diesel blends using both empirical and ANN models. Results revealed that the feed-forward backpropagation network with 4 input, 10 hidden, and 1 output neurons that was trained with Levenberg-Marquardt algorithm (ANN-LM) showed the best performance with the highest values of R (0.9992) and R-2(0.9984). It also has the lowest values of MAD, MSE, RMSE and MAPE at 0.2572, 0.4456, 0.6675, and 0.5304, respectively. As compared to the best empirical mathematical model (the 3rd order polynomial), the ANN-LM had slightly better performance accuracy. Therefore, the 4-10-1 ANN structure trained with Levenberg-Marquardt was found to be the best predictor for cetane index of ABE-diesel blends at various blending ratios.