This simulation shows concentration polarization: the buildup of rejected solute near a membrane surface caused by convection of feed toward the membrane during filtration.
Watch how the observed rejection (based on measurable bulk and permeate concentrations) falls below the membrane's real rejection as polarization becomes more severe — this is why concentration polarization degrades apparent membrane performance even though the membrane itself hasn't changed.
Within the concentration boundary layer of thickness
where C is the local solute concentration, D is the solute diffusivity, and Cp is the permeate concentration. Integrating from the edge of the boundary layer (x = 0, C = Cb, the bulk concentration) to the membrane wall gives the concentration profile:
Evaluating at the membrane wall (x =
The membrane's real (intrinsic) rejection relates the wall and permeate concentrations:
so that Cp = (1 − R)Cwall. Combining this with the polarization relation and solving for the wall concentration gives:
The ratio Jv/k is the Peclet number for the boundary layer,
Observed rejection is always less than or equal to the real rejection, and the gap between them grows as Jv/k increases — i.e., as concentration polarization becomes more severe.
This simulation uses D = 1.0 × 10−9 m2/s, typical of a small solute in water, so the boundary-layer thickness
| local solute concentration (g/L) | |
| bulk (feed) solute concentration (g/L) | |
| solute concentration at the membrane wall (g/L) | |
| permeate solute concentration (g/L) | |
| solute diffusivity in the feed solution (m2/s); 1.0 × 10−9 m2/s in this simulation | |
| permeate (volumetric) flux through the membrane (μm/s) | |
| mass-transfer coefficient in the boundary layer, D/ | |
| Peclet number, Jv/k (dimensionless) | |
| real (intrinsic) membrane rejection (dimensionless) | |
| observed rejection (dimensionless) | |
| distance from the bulk edge of the boundary layer toward the membrane (μm) | |
| concentration boundary-layer thickness (μm) |
This simulation was created in the Department of Chemical and Biological Engineering at University of Colorado Boulder for LearnChemE.com by John L. Falconer using Claude AI. It was prepared with financial support from the National Science Foundation (DUE 2336987 and 2336988). Address any questions or comments to LearnChemE@gmail.com.