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Title: Characterisation and prediction of membrane separation performance : an industrial assessment
Author: Oatley, Darren Lee
Awarding Body: University of Wales, Swansea
Current Institution: Swansea University
Date of Award: 2004
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The main objective of this work was to develop the existing predictive models for membrane nanofiltration, previously verified at the laboratory scale, and apply these theoretical descriptions to separations of real industrial importance. A detailed comparison was made between the updated Donnan steric partitioning model (UDSPM) model and the simplified linear UDSPM model and the extent of deviation over a wide range of possible nanofiltration conditions was small. This result justified the use of the simplified model for predicting multi-component separations reducing computational time and complexity. A theoretical and experimental comparison was made between two existing continuum descriptions of dielectric exclusion phenomenon. The two models were found to calculate the total contribution of dielectric exclusion effects to the same order of magnitude. The Born model was suggested as the most practical description at present because of the model's inherent simplicity. The UDSPM and linear UDSPM were then employed as a predictive tool in the isolation of N-acetyl-D-neuraminic acid, an important precursor in the production of the influenza antiviral RelenzaTM. The NanomaxTM-50 commercially available NF membrane was characterised and a membrane charge isotherm was developed from a study of the diafiltration components. Excellent agreement between the experimental findings and the model predictions was observed when the membrane charge was varied with pyruvate ion concentration. The linear UDSPM model was then used to assess the performance of a possible full scale industrial process for the recovery of sodium cefuroxime from a process effluent. The model results indicate that inclusion of nanofiltration technology will indeed facilitate the recovery of the high value antibiotic and produce an effluent of significantly improved quality. Overall, as a result of the rational approach taken in this study, the application of existing predictive nanofiltration models for the design, optimisation and scale-up of more complex industrially relevant separations has been established. This will further promote the use of membrane technology in the process industries, such as pharmaceutical and fine chemical manufacture, by significantly reducing development risk and time.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available