Optimization of a Membrane Filtration Process for Drinking Water Production Using On-Line Fluorescence and Permeate Flux Measurements

Abstract The optimization of membrane filtration processes for controlling fouling is essential for the sustainable application of membrane processes in drinking water treatment applications. Natural organic matter (NOM) and colloidal/particulate matter are considered as the major membrane foulants and therefore their characterization is essential for implementing optimization strategies. In a previous work by the authors, a fluorescence-based modeling approach was developed for prediction of the fouling dynamics and for optimization of a bench-scale ultrafiltration (UF) membrane cross flow set-up for drinking water treatment. In this study, this model's predictive ability was improved by updating the model parameters based on current process measurements. The Extended Kalman Filter (EKF) approach was used to achieve this objective. The EKF approach was implemented to accomplish online-adaptive estimation of key model parameters based on either current (time = t) UF flux measurements or principal component (PC) scores related to current fluorescence measurements of membrane permeate. The model predictions and the corresponding experimental UF flux data of different membrane fouling situations revealed that on-line permeate flux-based parameter adaptation result in improved model predictions as compared to PC scores’ based adaptation. The resulting model based estimator was then employed in the optimization of the UF process in which membrane back-washing times were estimated in order to achieve minimum energy consumption while ensuring maximum production of drinking water.