Optimization of supercritical carbon dioxide extraction of Passiflora seed oil

This study investigates extraction of Passiflora seed oil by using supercritical carbon dioxide. Artificial neural network (ANN) and response surface methodology (RSM) were applied for modeling and the prediction of the oil extraction yield. Moreover, process optimization were carried out by using both methods to predict the best operating conditions, which resulted in the maximum extraction yield of the Passiflora seed oil. The maximum extraction yield of Passiflora seed oil was estimated by ANN to be 26.55% under the operational conditions of temperature 56.5 °C, pressure 23.3 MPa, and the extraction time 3.72 h; whereas the optimum oil extraction yield was 25.76% applying the operational circumstances of temperature 55.9 °C, pressure 25.8 MPa, and the extraction time 3.95 h by RSM method. In addition, mean-squared-error (MSE) and relative error methods were utilized to compare the predicted values of the oil extraction yield obtained from both models with the experimental data. The results of the comparison reveal the superiority of ANN model compared to RSM model.

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► Neural networks are more accurate than statistical models in SFE.
► Optimization of SFE is more reliable with neural network than statistical methods.
► In presence of few experimental data neural models cannot be used.