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Modeling of milk lactose removal by column adsorption using artificial neural networks: MLP and RBF

Manuela Leite, Matheus Santos, Eulina Costa, Acenini Balieiro, Álvaro Lima, Odelsia Sanchez, Cleide Soares
2019 Chemical Industry and Chemical Engineering Quarterly  
Article Highlights • Mathematical models are proposed for the removal of lactose from milk • Multilayer perceptron and radial basis function neural networks are applied • Neural models (MLP and RBF) were developed to predict breakthrough curves • The operational parameters were evaluated for lactose adsorption capacity • The models effectively predicted the breakthrough curves under various conditions Abstract Artificial neural network (ANN) techniques are effective in modeling nonlinear
more » ... ng nonlinear processes, are simple to implement and require low computational time. In this work, the lactose adsorption process for continuous flow in a fixed-bed column with a molecularly imprinted polymer (MIP) adsorbent was modeled using an ANN technique. The neural models allowed predicting the relative lactose concentration (C/C 0 ) from the interactions between the variables of contact time (min), temperature (°C), granulometry (mesh), bed height (cm) and flow rate (mL min -1 ). The ANN models were developed in MATLAB using multilayer perceptrons (MLP) and a radial basis function network (RBF). The MLP model was developed using a three-layer feed forward backpropagation network with 5, 8 and 4 neurons in the first, second and third layer, respectively. The function (RBF) network is also proposed and its performance is compared to a traditional network type. The best architecture configuration RBF model was developed using 5, 14 and 1 neurons in the first, second and third layer, respectively. The proposal of development of mathematical models applied to multi-component adsorption system for milk using these approaches is innovative. The resulting breakthrough curve models for lactose adsorption were in good agreement with the experimental results. Performance indices, such as R², MSE, RMSE, SSE, MAE and RME were used to evaluate the reliabilities and accuracies of the models. A comparison between the ANN models shows the ability to predict the breakthrough curves of lactose removal in the milk adsorption process. Though, the MLP network model shows more accurately a higher correlation coefficient (R 2 = 0.9751) and lower values for the obtained error indices. The accuracy of the model is confirmed by the comparison between the predicted and experimental data. The results showed that both neural models efficiently described the non-linear process of lactose adsorption in a fixed-bed column.
doi:10.2298/ciceq180606015l fatcat:4k5jfcct7jbthcbvl5lvd47xhi