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Dissolved Solute Sampling Across an Oxic-Anoxic Soil-Water Interface Using Microdialysis Profilers

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Modeling pH variation in reverse osmosis.

Oded Nir¹, Noga Fridman Bishop², Ori Lahav¹

¹Faculty of Civil and Environmental Engineering, Technion - IIT, Haifa, 32000, Israel.

|October 9, 2015

Summary

This summary is machine-generated.

This study explains how hydronium and hydroxide ions move through reverse osmosis (RO) membranes. Diffusion and electromigration, not convection, are key to understanding permeate pH in RO systems.

Keywords:

Convection Electromigration Ion-flux Reverse-osmosis transport pH

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Area of Science:

Membrane Science and Technology
Physical Chemistry
Water Treatment

Background:

Reverse osmosis (RO) membranes exhibit high permeability for hydronium and hydroxide ions.
Electromigration significantly influences ion transport, leading to complex permeate pH behavior like negative rejection.
Existing models often neglect the combined effects of diffusion and electromigration for acid-base species.

Purpose of the Study:

To develop a rigorous model for trans-membrane transport of hydronium and hydroxide ions.
To accurately predict permeate pH in reverse osmosis systems.
To investigate the contributions of diffusion, electromigration, and convection to ion transport.

Main Methods:

Derived a non-linear transport equation from the Nernst-Planck equation.
Developed an approximate analytical solution for hydronium and hydroxide ion transport.
Validated the model using experimental data from a spiral wound RO module under varying conditions (flux, concentration, pH).

Main Results:

The approximate transport equation showed good agreement with experimental results.
Diffusion and electromigration were identified as the dominant transport mechanisms for hydronium and hydroxide ions.
Convection was found to be a minor contributor to the transport of these ions.

Conclusions:

The developed model accurately describes hydronium and hydroxide ion transport in RO.
The findings improve the understanding of permeate pH behavior in RO processes.
This approach enhances the predictive capabilities of RO models for acid-base species, aiding process design and control.