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

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.

Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture: Optimization and characterization

“…For instance (Rajendra et al, 2009), used ANN coupled with genetic algorithm (GA) to optimize the methanol-to-oil ratio, catalyst concentration, and reaction time of the pretreatment process in order to minimize the high free fatty content (indicated by the initial acid value) of various plant-based oils. Likewise, Dhingra et al (2014) used ANN with GA to optimize the ethanol-to-oil molar ratio, catalyst concentration, reaction temperature, reaction time, and agitation speed of the transesterification process to maximize the biodiesel yield from Calophyllum inophyllum oil. used RSM to optimize the process variables of H 2 SO 4 -catalyzed esterification and then compared the use of RSM and ANN to optimize the process variables (methanol-to-oil ratio, reaction time, and amount of calcinated plantain peels used as catalyst) of the transesterification process in order to maximize the Thevetia peruviana (yellow oleander) biodiesel yield.…”

A Review on Machine Learning Application in Biodiesel Production Studies

“…e results determined the priority of ANFIS prediction capability over the RSM model. Dhingra et al [102] applied ANN and GA combination in polanga oil-based biodiesel production to predict and optimize reaction variables to maximize the transesterification process.…”

Mesoporous Mn-Doped Fe Nanoparticle-Modified Reduced Graphene Oxide for Ethyl Violet Elimination: Modeling and Optimization Using Artificial Intelligence

“…A suitable combination of variables was selected to check the aptness of the model. Experiments were carried out with the optimal four variables to verify the prediction models as proposed by ANN-GA and ANN-PSO [55]. GA is a stochastic optimization technology developed based on genetic mechanisms and Darwin's theory of evolution [56,57].…”