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. 1. Introduction Valerian essential oils or extracts of valerian root have since long been used as sedatives. Therefore, extensive studies have been performed on the extract of the valerian root in recent years [1, 2]. These studies have revealed antispasm and sedative properties of the valerian [3–5], and attributed medical properties of the valerian mainly to valerenic acid (VA) [5]. Due to these findings, VA is used in formulation of many drugs and cosmetic products. Different methods have been employed for the extraction of the valerian root extract [6, 7], which are divided into two major categories. Hydrodistillation as the first category is inexpensive and easy to implement. However, its main disadvantage lies in operating at the boiling point of water, leading to the loss of many water-sensitive or temperature-sensitive combinations. Supercritical fluid extraction (SFE) in the second category has been recently used for the extraction of the natural materials to a great extent [6–9]. Working at lower temperatures, fast speed of the process, use of clean inexpensive fluid, and easy separation of the products are the main suitable features of SFE. Although the SFE is more costly compared to other methods such as hydrodistillation, its distinctive properties have made
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