Application of an artificial neural network model for the supercritical fluid extraction of seed oil from Argemone mexicana (L.) seeds

摘要

In this work, a three-layer artificial neural network (ANN) was investigated to predict the cumulative extraction yield (CEY) of seed oil during the supercritical fluid extraction (SFE) of Atgemone mexicana (L.) (A. Mexican) seeds. The effect of five extraction parameters (i.e. temperature (degrees C), pressure (bar), particle size (mm), flow rate-CO2 (g/min) and co-solvent % (% of flow rate-CO2) on the CEY of A. Mexicana seed oil was investigated and found that all five extraction parameters have significant effect in the order (co-solvent % & pressure & particle size & flow rate-CO2 & temperature) on it. The ANN model was generated, using experimental data and for this, a trainable, feed-forward-back-propagation (FFBP) network was used to predict the CEY of A. Mexicana seed oil with an acceptable level of accuracy. From the best performing ANN models, a single mathematical equation was developed that can be used in predicting the CEY. In this regard, by changing, the number of neurons in the hidden layer and the algorithms, different networks were formed and compared with the evaluation of networks accuracy in CEY. Finally, the six neurons in the hidden layer, using the Levenberg-Marquardt algorithm, found to be the most suitable network. The value of average-absolute-relative-deviation percentage (AARD%) (3.33%), mean-square-error (MSE) (0.0038) and the coefficient of determination (R-2 = 0.9838) showed that the ANN-FFBP [5-6-1] model is a better option for predicting the CEY. Furthermore, the fatty acids analysis was done by using gas chromatography (GC) which confirmed presence of leading fatty acids (C16:0, C16:1, C17:1, C18:0, C18:1n9c, C18:2n6c, and C20:5n3) in the extracted oil of A. Mexicana seeds.