Application of Artificial Neural Network in Simulation of Supercritical Extraction of Valerenic Acid from Valeriana officinalis L.

Abstract

Application of artificial neural network (ANN) has been studied for simulation of the extraction process by supercritical CO2. Supercritical extraction of valerenic acid from Valeriana officianalis L. has been studied and simulated according to the significant operational parameters such as pressure, temperature, and dynamic extraction time. ANN, using multilayer perceptron (MLP) model, is employed to predict the amount of extracted VA versus the studied variables. Three tests, validation, and training data sets in three various scenarios are selected to predict the amount of extracted VA at dynamic time of extraction, working pressure, and temperature values. Levenberg-Marquardt algorithm has been employed to train the MLP network. The model in first scenario has three neurons in one hidden layer, and the models associated with the second and the third scenarios have four neurons in one hidden layer. The determination coefficients are calculated as 0.971, 0.940, and 0.964 for the first, second, and the third scenarios, respectively, demonstrating the effectiveness of the MLP model in simulating this process using any of the scenarios, and accurate prediction of extraction yield has been revealed in different working conditions of pressure, temperature, and dynamic time of extraction.