A PLS model for predicting rejection of trace organic compounds by nanofiltration using treated wastewater as feed

Flyborg, Lena; Björlenius, Berndt; Ullner, Magnus; Persson, Kenneth M

doi:10.1016/j.seppur.2016.10.029

Cited by 15 publications

(12 citation statements)

References 27 publications

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“…This result was surprising because the molecular weight of dimethoate (229.28 g mol −1 ) is slightly higher than atrazine (215.69 g mol −1 ). Torres et al and Flyborg et al reported that the effect induced by hydrophobicity of the solutes shall not be omitted although the molecular sieving factor plays a vital role in governing the retention performance of membranes. The log octanol/water partition coefficient, K o/w , is normally employed to gauge the hydrophobicity of an organic solute, whereby a higher log K o/w value indicates a better solute retention.…”

Section: Resultsmentioning

confidence: 99%

Impact of Solute Properties and Water Matrix on Nanofiltration of Pesticides

et al. 2019

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The application of different nanofiltration membranes for the separation of pesticides, i.e., atrazine and dimethoate, from aqueous solutions is described. The nanoflitration membranes DK, NF270, NF200, and NF90 were tested for the pesticide retention performance in a stirred dead-end filtration system. NF90 demonstrated the best pesticide retention with over 95 % for atrazine and approximately 80 % for dimethoate. All membranes consistently showed better retention of atrazine than of dimethoate. Dissolving the pesticides in river or tap water amplified the overall pesticide retention performance, indicating that filtration in water treatment plants could render superior pesticide retention. However, a lower flux was obtained for the filtration of tap and river water.

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Section: Resultsmentioning

confidence: 99%

Impact of Solute Properties and Water Matrix on Nanofiltration of Pesticides

et al. 2019

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“…The adjusted R 2 (R 2 adj ) is a modified version of R 2 that considers the number of independent variables and the sample size. A large difference between R 2 and R 2 adj implies an excessive number of independent variables in the model [23].…”

Section: Theorymentioning

confidence: 99%

“…Polarizability is the ability of a molecule (or atom) to have its electron cloud distorted by an external electric field, changing its original configuration. Thus, this distortion causes the molecule (or atom), originally non-polar, to acquire a dipolar moment, that is, polarizability is the ability of a molecule (or atom) to form instantaneous dipoles [23,42]. The greater the polarizability, the greater the polar character of the species, increasing the forces of interactions with other species.…”

Section: Effect Of Control Factors On Rejectionmentioning

confidence: 99%

“…These developments were relative to synthetic solutions [19][20][21][22]. On the other side, Flyborg et al [23] developed a Partial Least Squares Projection of Latent Structures (PLS) model for predicting the rejection of pharmaceutical residuals by NF using a real treated urban wastewater as feed solution. The study was carried out with the compounds found on the treated wastewater, as atenolol, azithromycin, erythromycin, diazepam, SMX, and trimethoprim, among others.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Design as a Tool for Optimizing and Predicting the Nanofiltration Performance by Treating Antibiotic-Containing Wastewater

et al. 2020

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In recent years, there has been an increase in studies regarding nanofiltration-based processes for removing antibiotics and other pharmaceutical compounds from water and wastewater. In this work, a 2k factorial design with five control factors (antibiotic molecular weight and concentration, nanofiltration (NF) membrane, feed flow rate, and transmembrane pressure) was employed to optimize the NF performance on the treatment of antibiotic-containing wastewater. The resulting multiple linear regression model was used to predict the antibiotic rejections and permeate fluxes. Additional experiments, using the same membranes and the same antibiotics, but under different conditions of transmembrane pressure, feed flow rate, and antibiotic concentration regarding the 2k factorial design were carried out to validate the model developed. The model was also evaluated as a tertiary treatment of urban wastewater for removing sulfamethoxazole and norfloxacin. Considering all the conditions investigated, the tightest membrane (NF97) showed higher antibiotics rejection (>97%) and lower permeate fluxes. On the contrary, the loose NF270 membrane presented lower rejections to sulfamethoxazole, the smallest antibiotic, varying from 65% to 97%, and permeate fluxes that were about three-fold higher than the NF97 membrane. The good agreement between predicted and experimental values (R2 > 0.97) makes the model developed in the present work a tool to predict the NF performance when treating antibiotic-containing wastewater.

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“…Eureau suggests conducting further studies on development of quantitative structure activity relationships (QSARs) [11]. In recent years, some research has been conducted investigating Multiple Linear Regression models [12], artificial neural network models (ANN) [13,14], principal component analysis [12,15] and QSAR models [16][17][18][19] in order to predict the removal efficiencies of MPs by different water treatment processes. For example, some QSAR models were developed to predict the MPs' removal efficiency of the river-bank filtration process [7].…”

Section: Introductionmentioning

confidence: 99%