Evaluating Nanofiltration and Reverse Osmosis Membranes for Pharmaceutically Active Compounds Removal: A Solution Diffusion Model Approach

Trace organic contaminants (TrOCs), including pharmaceutically active compounds (PhACs), present significant challenges for conventional water treatment processes and pose potential risks to environmental and human health. To address these issues, nanofiltration (NF) and reverse osmosis (RO) membrane technologies have gained attention. This study aims to evaluate the performance of NF and RO membranes in removing TrOCs from wastewater and develop a predictive model using the Solution Diffusion Model. Experiments were conducted using a stirred cell setup at various target concentrations, stirring speeds, and operating pressures, with acetaminophen and caffeine selected as representative pharmaceutical compounds. The results demonstrated that most of the pharmaceutical compounds were effectively removed, showing excellent performance. NF membranes exhibited high permeate flux with somewhat lower removal efficiency (average 84.17%), while RO membranes demonstrated high removal efficiency (average 99.21%), highlighting their importance in trace pharmaceutical treatment. The predictive model based on the solution diffusion model correlated well with the experimental data, suggesting its potential utility for large-scale system applications. This study confirms that NF and RO membranes are effective technologies for the removal of TrOCs from wastewater, offering a promising solution to the challenges posed by trace pharmaceutical contaminants.

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Publication Details

Journal
Membranes
Published
2024-11-25
DOI
https://doi.org/10.3390/membranes14120250
Citations
7
Primary Topic
Membrane Separation Technologies
Type
article
Field-Weighted Citation Impact
1.41
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article

Evaluating Nanofiltration and Reverse Osmosis Membranes for Pharmaceutically Active Compounds Removal: A Solution Diffusion Model Approach

Tae-Mun Hwang, Homin Kye, Jeongbeen Park, Jae-Wuk Koo et al.
7 citations
Membranes
Membrane Separation Technologies
1.41
article

Evaluating Nanofiltration and Reverse Osmosis Membranes for Pharmaceutically Active Compounds Removal: A Solution Diffusion Model Approach

Tae-Mun Hwang, Homin Kye, Jeongbeen Park, Jae-Wuk Koo, Yong-Jun Choi, Yonghyun Shin, Sook-Hyun Nam, Eunju Kim
article en
7 citations

Abstract

Trace organic contaminants (TrOCs), including pharmaceutically active compounds (PhACs), present significant challenges for conventional water treatment processes and pose potential risks to environmental and human health. To address these issues, nanofiltration (NF) and reverse osmosis (RO) membrane technologies have gained attention. This study aims to evaluate the performance of NF and RO membranes in removing TrOCs from wastewater and develop a predictive model using the Solution Diffusion Model. Experiments were conducted using a stirred cell setup at various target concentrations, stirring speeds, and operating pressures, with acetaminophen and caffeine selected as representative pharmaceutical compounds. The results demonstrated that most of the pharmaceutical compounds were effectively removed, showing excellent performance. NF membranes exhibited high permeate flux with somewhat lower removal efficiency (average 84.17%), while RO membranes demonstrated high removal efficiency (average 99.21%), highlighting their importance in trace pharmaceutical treatment. The predictive model based on the solution diffusion model correlated well with the experimental data, suggesting its potential utility for large-scale system applications. This study confirms that NF and RO membranes are effective technologies for the removal of TrOCs from wastewater, offering a promising solution to the challenges posed by trace pharmaceutical contaminants.

MembranesVol. 14(12)
Kookmin University (KR), Korea Institute of Civil Engineering and Building Technology (KR), Korea University of Science and Technology (KR)
Clean water and sanitation
Openalex Percentile: Top 20%
Membrane Separation Technologies
1.41
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