M Khajenoori - Department of Chemical, Gas and Petroleum Engineering, Semnan University, Semnan, Iran
A Haghighi Asl - Department of Chemical, Gas and Petroleum Engineering, Semnan University, Semnan, Iran

In this study, the extraction of trans-anethole (t-anethole) using subcritical water solvent was employed as a case-study. A feed-forward multilayer back propagation artificial neural network (ANN) with various train algorithms and number of neurons was considered for the prediction of t-anethole extraction yield (mg/g dry sample). The input variables were temperature (100-175oC), flow rate (0.5-4ml/min), mean particle size (0.25-1mm) and output was t-anethole extraction yield. The optimized structure of neural network is manufactured based on minimum mean square error (MSE) of training and testing data. The optimal ANN model consisted of one hidden layer andfive neurons. The Prediction of t-anethole extraction yield using the ANN model was proven to be an accurate, appropriate, and simple method

trans-anethole, extraction, subcritical water, ANN model.