Neural modelling in adsorption column of cholesterol-removal efficiency from milk

In this study, the process of cholesterol removal from milk in an adsorption column with a continuous flow was modelled with artificial neural networks (ANN) models. The input operational parameters used for training the neural network include the bed height (1-3 cm), contact time (0-6 h) and flow-rate (3 9 mL/min). The cholesterol-removal efficiency (%) was defined as the output of the neural network. The neural network structure has been optimised by testing various training algorithms, different number of neurons and activation functions in a hidden layer. A high correlation coefficient (R-2 average ANN = 0.98), a minimum mean-squared error (MSE) and the minimum root mean squared error (RMSE) showed that the neural model obtained was able to predict the cholesterol-removal efficiency in milk. Comparison of the model results and experimental data showed that the ANN model can estimate the behaviour of the cholesterol-removal process through adsorption under different conditions. (C) 2015 Elsevier Ltd. All rights reserved.