No abstract

No abstract

No abstract

No abstract

The modeling of the permeate flux of a rotating disk membrane (RDM) filtration was investigated in this paper. Two computational intelligence techniques namely artificial neural networks (ANN) and support vector machine (SVM) were employed to model the permeate flux based on seven input variables including time of filtration, concentration of the feed fluid, transmembrane pressure, rotating velocity, dynamic velocity of the feed fluid, pore diameter of the membrane and, density of the feed fluid. The best-fit model was selected through the trial-error method and the two statistical parameters including the coefficient of determination (R²) and the average absolute relative deviation (AARD) between experimental and predicted data. The results obtained showed that the optimized ANN model can predict the permeate flux with R² = 0.999 and AARD% = 2.245 versus the SVM model with R² = 0.996 and AARD% = 4.09. Furthermore, the ANN model showed a high ability to predict the permeate flux in comparison with the SVM approach. The applicability domain of the ANN model was conducted to identify the outliers of the database and, the sensibility analysis of inputs on the output was employed for the determination of the relevant input variables.