Optimization of Oleuropein Extraction from Olive Leaves using Artificial Neural Network

In this work, the artificial neural networks (ANN) technology was applied to the simulation of oleuropein extraction process. For this technology, a 3-layer network structure is applied, and the operation factors such as amount of flow intensity ratio, temperature, residence time, and pH are used as input variables of the network, whereas the extraction yield is considered as response value. Performance indicators RMSE, SSE, R2adj, R2 have been used to determine the number of optimal midway neurons. Neural network trained with an error back-propagation algorithm, was used to evaluate the effects of parameters on extraction yield.The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with 4 input neurons, 1 hidden layer with 6 neurons and one output layer including single neuron.The trained network gave the minimum value in the RMSE of 1.6423 and the maximum value in the = 0.9641, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network.Functional structure of modeling, related to education,validation and test were obtained 0.99229,0.95591and 0.99224 respectively. The overall agreement between the experimental data and ANN predictions was satisfactory showing a determination coefficient of 0.9838.The neural network tools implemented in MATLAB software were used to predict the change process.