A Polymath Approach for the Prediction of Optimized Transesterification Process Variables of Polanga Biodiesel. (7th January 2014)

• Record Type: Journal Article
• Authors: Dhingra, Sunil; Dubey, Kashyap Kumar; Bhushan, Gian
• Is Part Of: Journal of the American Oil Chemists' Society. Volume 91:Number 4(2014)
• Page Start: 641
• Page End: 653
• Publication Date: 2014-01-07
• Subjects: Polanga biodiesel -- RSM -- ANN -- GA -- Optimization -- Fuel properties; Oils and fats -- Periodicals; Soap -- Periodicals; Fats -- Periodicals; Food-Processing Industry -- Periodicals; Acides gras -- Périodiques; Aliments -- Industrie et commerce -- Périodiques; Huiles et graisses -- Périodiques; Savon -- Périodiques; Oliën; Chemie; Oils and fats; Soap; Periodicals; 547.77
• DOI: 10.1007/s11746-013-2409-7
• Languages: English
• ISSNs: 0003-021X

Abstract

Abstract: 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 °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.