Development of a predictive kinetic model with statistically analyzed parameters for Donnan Dialysis process

Abstract

Donnan Dialysis (DD) utilizes ion exchange membranes that allow the selective transport of target ions from a feed solution to a concentrated receiver solution. The kinetics of DD involves boundary layer diffusion (BLD), diffusional membrane transport (MD), and a mixed regime where both mechanisms are active. The objective of the research was to develop an automated computational model that can predict nitrate, bicarbonate, and sulfate concentration profiles and transport mechanisms in DD processes. Experiments were conducted in a batch dialyzer in sole- and multi-component feed solutions and NaCl receiver solutions.Training of the model included numerical extraction of the BLD and MD coefficients from the experimental data; determination of the statistically significant parameters that affect the DD transport; development of correlations between these parameters and the kinetic coefficients; and mapping of the ratio between the boundary layer resistance and the membrane resistance to determine the transport mechanism.The predictive model, included the kinetic equations, i.e., the Nernst-Planck formulation, mass balances, and electro-neutrality, the correlations, and the mapping of the transport mechanism. The model was verified by the experimental data used for the model training and validated by independent datasets. Very good fits between the predicted and experimental concentration profiles were obtained, and the ability to determine the transport mechanism was demonstrated.