Comparison of linear regression and artificial neural network technique for prediction of a soybean biodiesel yield

Atmospheric pollution is one of the biggest problems all over the world. For this reason, researchers try to find alternative fuels for diesel engine, and biodiesel is the most feasible alternate fuel for diesel engines. In this study, linear regression (LR) and artificial neural network (ANN) used to predict the biodiesel yield produced by transesterification of soybean oil at constant temperature is reported in present work describes. The ANN estimation was done using a Levenberg-Marquardt learning algorithm (trainlm) with log sigmoid (logsig) neural network algorithm with 4 neurons in the hidden layer (3:4:1 topology). The experimental and ANN values were compared for the biodiesel yield. The value R-2 = 0.9899 for ANN and R-2 = 0.4198 for LR. Root mean square errors (RMSE) for ANN and LR are 0.6331 and 3.052, respectively. Results were compared with LR modeling. As a result, ANN gave more accurate results than LR and can be suggested as good a prediction method. It was followed by Fourier transform infrared (FTIR) spectroscopy analysis.