Modeling the performance of batch ultrafiltration of synthetic fruit juice and mosambi juice using artificial neural network Original Research Article

Neural network models have been used to describe the permeate flux and permeate concentration (total soluble solid) profiles during the ultrafiltration of synthetic fruit juice and mosambi juice dynamically. It aims to predict the permeate flux and total soluble solid of the permeate as a function of transmembrane pressure, sucrose, pectin concentration in the feed and the processing time. A multi-layer feed forward network structure with input, output and hidden layer(s) is used in this study. The back-propagation algorithm is utilized in training of ANN models. Two neural network models are constructed to predict the permeate flux and the total soluble solids in the permeate using the filtration data of the synthetic juice. The modeling results showed that there is an agreement between the experimental data and predicted values, with mean absolute errors less than 1% of the experimental data. Also the trained networks are able to capture accurately the non-linear dynamics of synthetic fruit juice and the actual mosambi juice even for a new condition that has not been used in the training process.