A Polymath Approach for the Prediction of Optimized Transesterification Process Variables of Polanga Biodiesel

作者：Dhingra Sunil^*; Dubey Kashyap Kumar; Bhushan Gian

来源：Journal of the American Oil Chemists' Society, 2014, 91(4): 641-653.

DOI：10.1007/s11746-013-2409-7

• Polanga biodiesel
• RSM
• ANN
• GA
• Optimization
• Fuel properties

An attempt has been made to employ an artificial neural network (ANN) combined with a genetic algorithm (GA) in MATLAB 7.0 for predicting the optimized reaction variables for maximum biodiesel production of polanga oil by the transesterification process. The developed ANN is a multilayer feed-forward back-propagation network (5-10-1) with five input, ten hidden and one output layers. The input variables are the molar ratio of ethanol to oil (X (1) in % v/v), the catalyst concentration (X (2) in % w/v), the reaction temperature (X (3) in A degrees C), the reaction time (X (4) in min), the agitation speed (X (5) in rpm) and the output parameter is biodiesel yield (% by weight) of polanga oil. The experimental data used in the developed ANN were obtained from response surface methodology (RSM) based on a central composite design. The trained ANN was tested using different training functions from the MATLAB to predict the best correlation coefficients of training, testing and validation. The data generated by trained ANN is used by GA with regards to the best response (for predicting biodiesel yield greater than predicted by RSM) for different combinations of variables (X (1), X (2), X (3), X (4), and X (5)) to attain optimization. The average biodiesel yield (by performing experiments under optimized conditions) of 92 % by weight was produced against the proposed value of 91.08 % by weight.

• 出版日期2014-4