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Abstract
Products of human and animal origin (biologicals and pharmaceuticals) and drinking water pose unique health and safety problems, especially due to viral contaminations, and have traditionally been a key concern both for regulators and industrial operations. They have also proven to be a stumbling block for early product research and development and inexperienced small start-up firms. Membrane filtration, being non-invasive, non-destructive and a robust technique, is one of the most preferred choices for virus filtration with ultrafiltration being an efficient process for removing macromolecules, colloids, endotoxins, viruses, and bacteria.
The challenge, in virus filtration, is to remove virus particles from products to close to zero levels especially in emerging water and heavily regulated multi-billion biopharmaceutical industries. In virus capture or clearance operations, the virus particles are distributed in the range of 15nm - 300nm depending upon their protein coat and family, hence it is critical to study impact of particle size in ultrafiltration. A number of previous studies have been concerned with particle characteristics and their relation to flux decline behavior. However the relationship between, on one hand, particle properties of the feed and operating parameters like transmembrane pressure and cross flowrate and on the other hand, the filtration efficiency (log reduction value), are still not adequately established.
This thesis investigates the effect of feed particle size, transmembrane pressure and cross flowrate on the filtration efficiency for tangential flow ultrafiltration. An empirical model is developed to quantify the dependencies of the above said parameters. For this study, a cross flow ultrafiltration rig (built in-house) is used with 30 and 100 kDa polyether sulphone membranes for three different particle sizes of silica (model virus particles) at three different transmembrane pressures (20-60 kPa) and three cross flowrates (0.3-1.0 L/min). The investigation shows that among feed particle size, transmembrane pressure and cross flowrate, feed particle size and transmembrane pressure are significant parameters in controlling the filtration efficiency. In the studied experimental range, higher log reduction values are obtained at lower transmembrane pressure (20 kPa) and at higher cross flowrate (1 L/min). The experimental statistical analysis also gives an insight on the cross interactions of the feed particle size, transmembrane pressure and cross flowrate. It shows that the effect of transmembrane pressure and feed particle size seems to be more significant than that of the interaction of cross flowrate with feed particle size.
The experimental investigations identified that feed particle size is one of the key influential factors on the filtration efficiency. Far less attention is given to this effect in the literature both experimentally and theoretically. This thesis attempts to fill this gap by developing a rigorous model formulation for tangential flow ultrafiltration to predict the evolution of particle size in the filtered stream. Population balance theory is employed to describe the population density of each particle size class in the output streams. In this formulation, the population balance equation is coupled with filtration kinetic constitutive relations and with mass balance equations and solved using discretisation method. The model predicts permeate particle size distribution and the log reduction value as primary model outputs. A novel approach in the form of optimization-based parameter estimation is employed to estimate filtration kinetic parameters. This approach which directly uses the population balance model, as well as particle size data, represents a stark deviation from previous filtration kinetic calculations that only utilize flux data and no models that describe the particle state. As such, this approach offers more comprehensive description of filtration kinetics incorporating the effects of particle size. The model is validated and is found to be in good agreement with experimental results. The model serves as a predictive tool for filtration efficiency and filtrate particle size distribution. Overall, this model-based prediction and estimation capability serves well and facilitates the design, operation and scale-up of ultrafiltration processes.