Optimization of microfluidic gallotannic acid extraction using artificial neural network and genetic algorithm

Abstract

Abstract: The current study presents the outcomes of modeling and optimizing extraction of gallotannic acid from Quercus leaves using a microfluidic system. In this study, the effects of various experimental parameters were investigated using the method of design expert. Number of experiments suggested is 31 by central composite design of Design Expert. The experimental results of design expert were analyzed by artificial neural network (ANN). Based on the results of ANN, independent variables experiment: temperature (T), flow rate ratio (FR) and pH have shown a negative effect on extraction yield (dependent variable), while the residence time (RT) has shown a positive effect. In trained network, ${R^2} = 0.9805$ and RMSE = 0.0166 shows good agreement between the predicted values of ANN and experimental results. Optimum extraction conditions, to reach maximum yield by genetic algorithms (GA), were FR = 0.53, RT = 26.4, pH = 2.06 and T = 21.44 ${R^2} = 0.9805$ . The extraction yield under the optimum predicated conditions was 96.4 %, which was well matched with the experimental value 95.01 % $\pm 0.63$ . Based on the obtained results, it was found that the ANN model could be employed successfully in estimating the gallotannic acid extraction efficiency using microfluidic extraction method.