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Frontiers of Chemical Science and Engineering >> 2013, Volume 7, Issue 3 doi: 10.1007/s11705-013-1336-3

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-based fuzzy inference system

Department of chemical engineering, Ferdowsi university of Mashhad, Mashhad 9177948944, Iran

Available online: 2013-09-05

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Abstract

Various simulation tools were used to develop an effective intelligent system to predict the effects of temperature and pressure on an oil extraction yield. Pomegranate oil was extracted using a supercritical CO (SC-CO ) process. Several simulation systems including a back-propagation neural network (BPNN), a radial basis function neural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested and their results were compared to determine the best predictive model. The performance of these networks was evaluated using the coefficient of determination ( ) and the mean square error (MSE). The best correlation between the predicted and the experimental data was achieved using the BPNN method with an of 0.9948.

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