Application of Artificial Neural Network to Predict Biodiesel Yield from Waste Frying Oil Transesterification

Used frying oil (UFO) has a great potential as feedstock for biodiesel production. This study aims to develop an artificial neural network (ANN) model to predict biodiesel yield produced from base-catalyzed transesterification of UFO. The experiment was performed with 100 mL of UFO at three different molar ratios (oil:methanol) (namely 1:4, 1:5, and 1:6), conducted with reaction temperatures of 30 to 55oC (raised by 5oC), and reaction time of 0.25, 0.5, 1, 2, 3, 6, 8, and 10 minutes. Prediction model was based on ANN model consisting of three layers with 27 combinations of three activation functions (tansig, logsig, purelin). All activation function architectures were trained using Levenberg-Marquardt train type with 126 data set (87.5%) and learning rate of 0.001. Model validation used 18 data set (12.5%) measured at reaction time of 8 min. Results showed that two ANN models with activation function of logsig-purelin-logsig and purelin-logsig-tansig be the best with RRMSE of 2.41% and 2.44% with R2 of 0.9355 and 0.9391, respectively. Predictions of biodiesel yield using ANN models are significantly better than those of first-order kinetics.