Predicting effect of interparticle interactions on permeate flux decline in CMF of colloidal suspensions: An overlapped type of local neural network

Abstract

An overlapped type of local neural network is proposed to improve accuracy of the flux decline prediction in crossflow membrane filtration (CMF) of colloidal suspensions. This network combines the advantages of the multilayer feedforward back-propagation neural network and the radial basis function network. The prediction performance and efficiency of the proposed network are examined using a published experimental dataset of CMF. The data includes the permeate flux decline under hydrodynamic and physicochemical operating conditions, such as transmembrane pressure, particle size, ionic strength and solution pH. Unlike the conventional network using the trial-and-error method, the proposed network offers a systematical framework for constructing and training network based on the data distribution of the flux decline in CMF, bringing the benefits of fast convergence and easy training. The results indicate that the proposed network can find out the proper locations of the neurons to improve the approximation without trial-and-error procedures. The effective contribution of the colloidal interactions, hydrodynamic forces and the coupling between these parameters on permeate flux decline is further computed based on all flux experiments for the whole period of measurement. Because of the good initialization of the neural network, the computed effect of the operation condition extracting from the same set of network connection weights can be kept consistent. Thus, the validation results of the cause-effect information quantitatively extracted from the network match our expectation of the coupling effect on permeate flux decline in CMF at any time.