Modeling of microwave‐ and ultrasound‐ohmic‐assisted hydro‐distillation extraction of the <i>Pimpinella anisum</i> essential oil
Roya Jafari, Mohsen Zandi, Ali Ganjloo
Abstract
Abstract First‐order, second‐order, sigmoid, and absorption models were used to predict the extraction yield (EY) of anise essential oil (AEO). Sigmoid model was able to predict the EY with R 2 0.9852 and 0.9869 respectively for ohmic‐assisted hydro‐distillation methods with ultrasound and microwave pretreatments. It was found that among the different prediction models, adaptive neuro‐fuzzy inference system (ANFIS) has the highest accuracy for EY estimating. So that a network with the help of Mamdani's maximum‐minimum method and triangular membership function was able to predict the EY in both pretreatments with R 2 above 0.99. The results of artificial neural network (ANN) modeling showed that the multilayer perceptron network with feed‐forward back propagation structure, Levenberg–Marquardt training algorithm, and with configurations 3‐16‐7‐1 and 3‐14‐5‐1 was able to have the best performance in EY predicting of ultrasound and microwave pretreatments, respectively. The results emphasize the successful use of models, especially dynamic models, in predicting the kinetics of essential oil extraction. Practical applications Modeling helps to understand the basic process and is suitable for better control of the process and increasing its efficiency. Mathematical models, as a relatively simple method, lead to a better understanding of the mechanism and factors affecting extraction processes. Although mathematical models have many advantages in identifying and predicting the behavior of parameters in different conditions, generally, they are not very accurate in predicting complex behaviors. Dynamic modeling including artificial neural networks (ANNs) and fuzzy logic has recently received much attention due to its ability to learn complex processes and predict nonlinear relationships. The results emphasize the successful use of models, especially dynamic models, in predicting the kinetics of essential oil extraction.