A comparison between semi-theoretical and empirical modeling of cross-flow microfiltration using ANN

Abstract

The applicability of semi-empirical and artificial neural network (ANN) modeling techniques for predicting the characteristics of a microfiltration system was assessed. Flux decline under various operating parameters in cross-flow microfiltration of BSA (bovine serum albumin) was measured. Two hydrophobic membranes were used: PES (polyethersulfone) and MCE (mixed cellulose ester) with average pore diameters of 0.22 μm and 0.45 μm, respectively. The experiments were carried out to investigate the effect of protein solution concentration and pH, trans-membrane pressure (TMP), cross-flow velocity (CFV), and membrane pore size on the trend of flux decline and membrane rejection at constant trans-membrane pressure and ambient temperature. Subsequently, the experimental flux data were modeled using both classical pore blocking and feed forward ANN models. Semi-empirical models based on classic mechanisms of fouling have been proposed. It was shown that these mechanisms could predict the microfiltration flux for a specified period of processing time; while through appropriate selection of ANN parameters such as the network structure and training algorithm, the ANN-based models are competent in modeling membrane filtration systems for all operating conditions and the entire filtration time with desired accuracy.