Computer program for simulation of mass transport in nanofiltration membranes

Geraldes, Vı́tor; Alves, Ana Sofia

doi:10.1016/j.memsci.2008.04.054

Cited by 79 publications

(92 citation statements)

References 42 publications

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“…14, applies at the feed/membrane interface, while the second condition, Eq. 15, applies in the permeate region [31].…”

Section: Concentration Polarization and Mass Transfer Modelingmentioning

confidence: 99%

“…In writing these expressions, we follow the notation presented by Geraldes and Alves [31], where Eq. 26 describes the partitioning as it occurs just inside and just outside the membrane at the feed/membrane interface, and Eq.…”

Section: Solute Partitioning At Electrochemical Equilibriummentioning

confidence: 99%

“…These equations are coupled with the concentration polarization equation and the boundary conditions derived from solute partitioning to form a closed system that is solved numerically. Our numerical approach is in tandem with that presented by Geraldes and Alves [31].…”

Section: Membrane Discretization and Modelingmentioning

confidence: 99%

“…Consequently, dielectric exclusion was later incorporated as a partitioning mechanism by Bowen et al [29]. This paved the way for the development of the Donnan-Steric Pore Model with dielectric exclusion (DSPM-DE) by Bandini and Vezzani [30], the open source program, NanoFiltran, by Geraldes and Alves [31], and lately the large-scale models for NF flat-sheet and spiral-wound modules by Roy et al [32].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fundamentals of low-pressure nanofiltration: Membrane characterization, modeling, and understanding the multi-ionic interactions in water softening

Labban

Liu

Chong

et al. 2017

Journal of Membrane Science

148

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Recently, a novel class of low-pressure nanofiltration (NF) hollow fiber membranes, particularly suited for water softening and desalination pretreatment have been fabricated in-house using layer-by-layer (LbL) deposition with chemical crosslinking. These membranes can operate at exceedingly low pressures (2 bar), while maintaining relatively high rejections of multivalent ions. In spite of their great potential, our understanding as to what makes them superior has been limited, demanding further investigation before any large-scale implementation can be realized. In this study, the Donnan-Steric Pore Model with dielectric exclusion (DSPM-DE) is applied for the first time to these membranes to describe the membrane separation performance, and to explain the observed rejection trends, including negative rejection, and their underlying multi-ionic interactions. Experiments were conducted on a spectrum of feed chemistries, ranging from uncharged solutes to single salts, salt mixtures, and artificial seawater to characterize the membrane and accurately predict its performance. Modeling results were validated with experiments, and then used to elucidate the working principles that underlie the low-pressure softening process. An approach based on sensitivity analysis shows that the membrane pore dielectric constant, followed by the pore size, are primarily responsible for the selectively high rejections of the NF membranes to multivalent ions. Surprisingly, the softening process is found to be less sensitive to changes in membrane charge density. Our findings demonstrate that the unique ability of these membranes to exclusively separate multivalent ions from the solution, while allowing monovalent ions to permeate, is key to making this low-pressure softening process realizable.

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“…14, applies at the feed/membrane interface, while the second condition, Eq. 15, applies in the permeate region [31].…”

Section: Concentration Polarization and Mass Transfer Modelingmentioning

confidence: 99%

Section: Solute Partitioning At Electrochemical Equilibriummentioning

confidence: 99%

Section: Membrane Discretization and Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fundamentals of low-pressure nanofiltration: Membrane characterization, modeling, and understanding the multi-ionic interactions in water softening

Labban

Liu

Chong

et al. 2017

Journal of Membrane Science

148

View full text Add to dashboard Cite

show abstract

“…DSPM-DE is a comprehensive model for nanofiltration. As the name suggests, the model provides information regarding the magnitudes of the different modes of solute exclusion occurring at the membrane-solution interfaces, namely steric exclusion (size-based exclusion at the pore opening), Donnan effect (repulsion or attraction effect due to membrane potential) and dielectric exclusion (resistance to the solute entering the membrane pores due to an energy barrier associated with shedding of the solute hydration shell in order to enter the pore) [20] [21] [22]. The model uses the Nernst-Planck equation to describe solute transport through the membrane and hence provides information on the individual modes of transport within the membrane, namely diffusion (movement of solute down a concentration gradient), convection (solute transported by bulk fluid motion) and electro-migration (ion movement due to the membrane potential gradient).…”

Section: The Dspm-de Model Of Nanofiltrationmentioning

confidence: 99%

Effect of temperature on ion transport in nanofiltration membranes: Diffusion, convection and electromigration

2017

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Nanofiltration performance as a function of feed temperature is relevant to several industrial settings including pretreatment for scale control in thermal desalination. Understanding of solute transport as a function of temperature is critical for effective membrane and system design. In this study, nanofiltration is modeled at 22, 40 and 50 o C using the Donnan Steric Pore Model with dielectric exclusion (DSPM-DE).This modeling includes the temperature dependence of the three modes of solute transport, namely the convective, electromigrative, and diffusive modes, and the three mechanisms of solute exclusion, namely Donnan, steric, and dielectric exclusion. The effect of temperature is captured through the variation of membrane parameters and solvent and ionic mobilities with temperature. We compare the most abundant ionic compound in natural water, sodium-chloride with magnesium-chloride to portray how the salt passage and rejection change for a 1:1 salt compared to a 2:1 salt, and we analyze Arabian Gulf seawater to understand how rejection of scale-forming ions, such as Mg 2+ and Ca 2+ , is affected by feed temperature.In all cases, solute transport increases with temperature, attributed predominantly to the cumulative effect of membrane parameters and only to a small extent (up to 5%) to the solvent viscosity and ion diffusivity together.Keywords: Nanofiltration, Temperature, Ion transport, Desalination

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Recent Advances in Stimuli‐Responsive Smart Membranes for Nanofiltration

Xiao

Shokoohi

et al. 2022

Adv Funct Materials

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Nanofiltration membrane plays an increasingly important role in many industrial applications, such as water treatment and resource recovery. The performance of the smart nanofiltration membrane is largely controlled by pore size, the Donnan effect, and surface wettability, which are determined by the function of stimuli‐responsive components. Smart membranes, which contain stimuli‐responsive components, are capable of changing their physical and chemical properties in response to changes in the environment so that the microstructure of the membrane will have more efficient performances and broader application prospects than the current traditional nanofiltration membranes. Herein, the preparation methods of stimuli‐responsive membranes are described and they are systematically classified accordingly to their mechanisms. Moreover, the latest progress of stimuli‐responsive membranes in nanofiltration and the main mechanism of each stimuli‐response type through relevant examples are discussed and summarized. Finally, this review provides new insights into the remaining challenges and future directions of stimuli‐responsive membranes. Fueled by advances in chemistry and materials science, it is expected to build a smart and efficient nanofiltration membrane platform for the benefit of mankind.

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Computer program for simulation of mass transport in nanofiltration membranes

Cited by 79 publications

References 42 publications

Fundamentals of low-pressure nanofiltration: Membrane characterization, modeling, and understanding the multi-ionic interactions in water softening

Fundamentals of low-pressure nanofiltration: Membrane characterization, modeling, and understanding the multi-ionic interactions in water softening

Effect of temperature on ion transport in nanofiltration membranes: Diffusion, convection and electromigration

Recent Advances in Stimuli‐Responsive Smart Membranes for Nanofiltration

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