Predicting the supercritical carbon dioxide extraction of oregano bract essential oil

Abstract

The extraction of essential oils using compressed carbon dioxide is a modern technique offering significant advantagesover more conventional methods, especially in particular applications. The prediction of extraction efficiency is a powerful toolfor 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 backpropagationlearning algorithm, incorporating different training methods. The required data were collected; pre-treating wasused for ANN training. The accuracy and trend stability of the trained networks were verified according to their ability to predictunseen data. The Levenberg-Marquardt algorithm has been found to be the most suitable algorithm, with the appropriatenumber of neurons (i.e., ten neurons) in the hidden layer and a minimum average absolute relative error (i.e., 0.019164). Inaddition, some excellent predictions with maximum error of 0.039313 were observed. The results demonstrated the ANN’scapability 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.