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.