MECHANISTIC FILTER FOULING MODELLING DEVELOPMENT FOR NORMAL-FLOW BIOPROCESS APPLICATION

Depth filters is utilized to separate monoclonal antibodies from the mammalian cell culture broth in the biopharmaceutical field. This thesis is developed an enhanced filter fouling model to predict the performance of the depth filtration step. Individual and combinations of classical fouling mechanisms are the basis of the model described in this work. The model is enhanced by adding the 1st principle terms such as settling time, residence time, lifting force and van der Waals force to the classical model. These additions enabled an enhanced ability to make the prediction and forecast in terms of the filter fouling and breakthrough. The inline Process Analytical Technologies (PAT) were utilized to help capture missing terms in the filter fouling model and help verify the fouling model. The differential pressure profile generated from the fouling experiments combined with the filter information and the feed material form the input of the enhanced filter fouling model. The enhanced filter fouling model developed in this work was utilized to predict fouling in different types of filters, such as primary clarification, chromatography guard filters, virus filtration process. The model developed predicted pressure breakthrough and turbidity breakthrough events. In the biopharmaceutical industry, filters are purposefully oversized, and the modelling approach developed in this thesis will save data and time at bench scale to conduct filter sizing studies. In addition, the enhanced filter fouling modelling can be used to understand how flux impacts filter capacity and size all filter within a train concurrently, such as trains filter versus singular filters