Optimization of a Membrane Filtration Process for Drinking Water Treatment using Fluorescence-based measurements

Abstract

Membrane fouling control is of paramount importance for sustainable operation of membrane-based drinking water treatment processes. Natural organic matter (NOM) is considered as the major membrane foulant and therefore its characterization is important for implementing fouling control strategies. This study proposes a fluorescence-based modeling approach for estimating and predicting the fouling dynamics in a bench-scale ultrafiltration (UF) membrane cross flow set-up for drinking water treatment. Principal component analysis (PCA) was used to extract the information that is relevant for membrane fouling from fluorescence excitation-emission matrix measurements captured during UF operation. PCA extracted principal components (PCs) that were related to major NOM membrane foulants. The model predictions were based on PC scores of retentate and permeate captured at time = 15 min of the UF experiments. The proposed fluorescence-based modeling approach is able to forecast different fouling behaviours with good accuracy. This proposed approach was then used for 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.