Assessment of phenomena underlying the removal of micropollutants during water treatment by nanofiltration using multivariate statistical analysis

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Abstract

Multivariate statistically-based models were developed for the comprehension of the phenomena underlying the removal of several micropollutants during drinking water treatment. Projection to latent structures (PLS) modeling was used to describe the apparent rejection as well as adsorption using specific descriptors of the molecules (physico-chemical properties and molecular size parameters), descriptors of the membrane used and of the water matrices, and operating related conditions. Multilinear PLS models with good descriptive capability towards rejection were developed. Alkalinity, molecular size descriptors, molecular weight, molar volume, and LogD were found to be the most relevant contributors for the rejection of the selected micropollutants, showing the impact of size exclusion and electrostatic interactions with minor contributions of hydrophobic interactions. The incorporation of molecular size descriptors demonstrated that the geometry of the molecule is important to determine rejection when molecules have very different geometry. Conversely, PLS modeling of adsorption required also the inclusion of quadratic and interaction terms to achieve good description. Adsorption of the selected compounds seems to be mainly determined by polar and electrostatic interactions rather than hydrophobic and may be described by polarizability as well as several specific interactions. Additionally, the incorporation of molecular size descriptors did not improve modeling of adsorption, showing that molecular geometry is not so important to describe this process as for rejection. (C) 2013 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)377-386
Number of pages10
JournalSeparation and Purification Technology
Volume118
Issue numberNA
DOIs
Publication statusPublished - 2013

Keywords

  • Rejection and adsorption mechanisms
  • Drinking water treatment
  • Multivariate statistical analysis
  • Nanofiltration
  • PLS

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