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

•The removal of cholesterol from milk by batch experiments is proposed.•Artificial neural networks are applied for prediction of cholesterol removal.•The ANN model is developed using a three layer feedforward backpropagation network.•The operational parameters were studied on cholesterol-removal efficiency from milk.•The model is able to predict the behaviour of process under different conditions.

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 (R2 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.