Modeling of RO/NF membrane rejections of PhACs and organic compounds: a statistical analysis

V Yangali-Quintanilla,T.-U Kim,M Kennedy,G Amy

doi:10.5194/dwes-1-7-2008

Abstract

Abstract. Rejections of pharmaceutical compounds (Ibuprofen, Diclofenac, Clofibric acid, Naproxen, Primidone, Phenacetin) and organic compounds (Dichloroacetic acid, Trichloroacetic acid, Chloroform, Bromoform, Trichloroethene, Perchloroethene, Carbontetrachloride, Carbontetrabromide) by NF (Filmtec, Saehan) and RO (Filmtec, Saehan, Toray, Koch) membranes were studied. Chloroform presented the lowest rejection due to small molar volume, equivalent width and length. Diclofenac and Primidone showed high rejections related to high molar volume and length. Dichloroacetic acid and Trichloroacetic acid presented good rejections caused by charge exclusion instead of steric hindrance mechanism influencing rejection. Bromoform and Trichloroethene showed low rejections due to small length and equivalent width. Carbontetrabromide, Perchloroethene and Carbontetrachloride with higher equivalent width than BF and TCE presented better rejections. A qualitative analysis of variables using Principal Component Analysis was successfully implemented for reduction of physical-chemical compound properties that influence membrane rejection of PhACs and organic compounds. Properties such as dipole moment, molar volume, hydrophobicity/hydrophilicity, molecular length and equivalent width were found to be important descriptors for simulation of membrane rejection. For membranes used in the experiments, we may conclude that charge repulsion was an important mechanism of rejection for ionic compounds. After analysis with Multiple Linear Regression, we also may conclude that membrane rejection of neutral compounds was well predicted by molar volume, length, equivalent width, hydrophobicity/hydrophilicity and dipole moment. Molecular weight was a poor descriptor variable for rejection modelling. We were able to provide acceptable statistical significance for important results.

Highlights

The presence of pharmaceutically activated compounds (PhACs) and endocrine disrupters compounds (EDCs) in surface waters has been reported, detailed and quantified in many studies (Ternes, 1998; Hirsch et al, 1999; Heberer, 2002; Kolpin et al, 2002)
Principal Component Analysis (PCA) is essentially a method of data reduction that aims to produce a small number of derived variables that can be used in place of the larger number of original variables to simplify subsequent analysis of the data
A list of selected compounds is presented in Table 1; this table shows physical-chemical estimations of compound properties, such as: molecular weight (MW), dipole moment, water-octanol partition coefficient, acid dissociation constant, molar volume (MV), length, width and depth

Summary

Introduction

The presence of pharmaceutically activated compounds (PhACs) and endocrine disrupters compounds (EDCs) in surface waters has been reported, detailed and quantified in many studies (Ternes, 1998; Hirsch et al, 1999; Heberer, 2002; Kolpin et al, 2002). Many studies have investigated the removal of micropollutants i.e. PhACs, EDCs, by membrane treatment (NF, RO) and their separation mechanisms such as size/steric exclusion, hydrophobic adsorption, partition and electrostatic repulsion (Kiso et al, 2001a, b, 2002; Schafer et al, 2003; Nghiem et al, 2004; Kimura et al, 2003, 2004; Kim et al, 2005) Characteristics such as MWCO, porosity, membrane morphology, charge, and hydrophobicity of the membrane influence rejection of compounds (Schaep and Vandecasteele, 2001; Childress and Elimelech, 2000); compound properties such as Published by Copernicus Publications on behalf of the Delft University of Technology. In the case of components derived from the correlation matrix of the data, these rescaled coefficients give the correlations between the components and the original variables. Those values are often presented as the result of a principal components analysis. The procedure of PCA starts with assigning eigenvalues to each component for transforming a set of multi variables into a set of components

Objectives

Methods

Results