Original Article | 2011, Vol.33, No.5, pp. 531-538
Predicting the supercritical carbon dioxide extraction of oregano bract essential oil
Abdolreza Moghadassi, Sayed Mohsen Hosseini, Fahime Parvizian, Ibrahim Al-Hajri and Mehdi Talebbeigi
pp. 531 - 538
Abstract
The extraction of essential oils using compressed carbon dioxide is a modern technique offering significant advantages over more conventional methods, especially in particular applications. The prediction of extraction efficiency is a powerful tool for designing and optimizing the process. The current work proposed a new method based on the artificial neural network (ANN) for the estimation of the extraction efficiency of the essential oil oregano bract. In addition, the work used the backpropagation learning algorithm, incorporating different training methods. The required data were collected; pre-treating was used for ANN training. The accuracy and trend stability of the trained networks were verified according to their ability to predict unseen data. The Levenberg-Marquardt algorithm has been found to be the most suitable algorithm, with the appropriate number of neurons (i.e., ten neurons) in the hidden layer and a minimum average absolute relative error (i.e., 0.019164). In addition, some excellent predictions with maximum error of 0.039313 were observed. The results demonstrated the ANN’s capability to predict the measured data. The ANN model performance was also compared to a suitable mathematical model, thereby confirming the superiority of the ANN model.
