Analysis of Mean Permeate Flux For Ultrafiltration Process By Two Methods: Resistance-In-Series Model and Artificial Neural Network

Two resistance-in-series and artificial neural network (ann) methods have been studied to compute the amount of mean permeate flux of a hollow-fiber module for ultrafiltration of pvp (360) aqueous solution# the present resistance-in-series model includes two types of pressure distribution equations# in model 1, momentum balance with term of convection has been considered# in model 2, the pressure distribution has been obtained by hagen poiseuille equation# the total aad% and mse in model 1 are 7#47 and 0#0966# these numbers are a bit lower than model 2 with aad% 7#53 and mse equals to 0#0975# the calculated amounts of aad% and mse in the lowest velocity, 0#0723 (m/s), are 5#2 and 0#042 and in the highest velocity, 0#2195 (m/s), are 12#5 and 0#4# thus in both models, the errors and deviations has been arised by increasing feed velocities# in ann method, input data has been chosen feed velocity, feed concentration and transmembrane pressure and the output data has been selected mean permeate flux# back-propagation ann model has been developed with a tree-layer network and levenberg-marquardt (lm) as learning algorithm# the transfer function is tansig and six different structures are examined which the number of neurons in hidden layer are varying between 15 and 20# the network with 17 hidden neurons have showed good performance with least deviation and error (aad%=6#14 and mse=0#0004)# as the amounts of r2 are between 0#87 and 0#99, all structures have got the acceptable proportion of variances#