Modeling of membrane fouling in a submerged membrane reactor using support vector regression

Abstract

Removal rate of Fe2+ and Mn2+ using submerged membrane reactor for drinking water in the presence of fulvic acid and iron hydroxide is studied using the data from the experiments obtained from various concentrations of Fe2+, Mn2+, fulvic acid, and iron hydroxide. The relationship between these contaminants and membrane fouling is investigated. In the experiments, flux is kept as constant, and the pressure change with time is observed. To model the relationship, a regression analysis using the support vector regression (SVR) model is presented. Hyperparameter optimization for SVR is important, that is, wrong selection may cause underfitting/overfitting phenomena. In order to find optimal values, grid search method is performed with various parameters such as different kernel functions (radial basis functions, polynomial, linear), cost parameter (C), and scale parameters and epsilon. The results obtained by SVR show that proposed method is feasible.