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Title: Predictive modelling of membrane nanofiltration
Author: Welfoot, J. St. J.
Awarding Body: University of Wales Swansea
Current Institution: Swansea University
Date of Award: 2000
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The main objective of this work was to develop predictive models for nanofiltration (NF) membrane processes. A one-parameter model (pore radius) for uncharged solute rejection has been developed. The good agreement between the proposed model and experimental data confirmed that uncharged solute rejection is well described by continuum models. A two-parameter model (pore radius and membrane charge) for electrolyte rejection has also been developed. Dielectric exclusion was included as an energy barrier to ion partitioning into the pores, the reassessment of which using NaCl rejection at the membrane isoelectric point introduced a third model parameter, the average pore solvent dielectric constant. The predicted membrane charge densities with the three-parameter model were more realistic in magnitude than those from previous models and their variation with concentration for divalent salts was in better agreement with physical models of ion adsorption. Analysis of experimental rejection data with truncated pore size distributions and a variation of viscosity with pore radius resulted in model parameters that represented the average value over all pore sizes. Further, analysis of salt mixtures showed that large experimentally observed negative rejections were very well described with fitted charge densities of similar magnitude to those from single salts. Finite Difference linearisation of pore concentration gradient greatly simplified the numerical solution of the three-parameter model. The validity of the linearised model was tested both experimentally and theoretically, showing the model to be a powerful tool for characterisation of NF membranes and subsequent prediction of separation performance. Overall, the work presented in this thesis has improved the understanding of the separation mechanisms of NF membranes, especially dielectric exclusion. The developed models are more rigorous than those proposed previously and represent a significant contribution to the field of predictive NF modelling.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available