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

Agus Haryanto, Tri Wahyu Saputra, Mareli Telaumbanua, Amiera Citra Gita


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.


Biodiesel; ANN model; Waste frying oil; Transesterification; Activation function; Yield

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