Predicting ultrafiltration performance using Principal Component Analysis

Teychene B, Touffet A, Baron J, Welte B, Joyeux M, Gallard H. Predicting of ultrafiltration performances by advanced data analysis. Water research. 2017 Nov 9;129:365-374. doi: 10.1016/j.watres.2017.11.023.

In order to optimize drinking water production operation, membrane users can use several analytical tools that help membrane fouling prediction and alleviate fouling by a proper feed water resource selection. However, during strong fouling event, membrane decision-makers still face short-term deadline to decide between different options (e.g. optimization of pretreatment or change in feed water quality). Hence, statistical approach might help to better select the most relevant analytical parameter related to fouling potential of a specific resource in order to speed-up decision taking. In this study, the physical and chemical properties and the filtration performances (at lab-scale) of five groundwater resources, selected as potential resources of a large drinking production site of Paris (France), was evaluated through one year. Principal component analysis emphasizes the strong link between waters’ organic matrix and fouling propensity. Cluster analysis of filtration performances allowed classifying the water samples into three groups exhibiting strong, low and intermediate fouling. Finally, multiple linear regressions performed on all collected data indicated that strong fouling events were related to a combined increase of carbon content and protein like-substances while intermediate fouling might only be anticipated by an increase of fluorescence signal associated to protein like-substances. This study demonstrates that advanced data analysis might be a powerful tool to better manage water resources selection used for drinking water production and to forecast filtration performances in a context of water quality degradation.

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