A network-based approach to interpreting pore blockage and cake filtration during membrane fouling

Abstract

The efficiency of membrane-based separations is limited by various fouling phenomena, which necessitates a mechanistic understanding in order to improve such processes. This study proposes a network-based approach, in which the membrane is discretized and each particle is individually monitored, to explore the underlying fouling mechanisms leading to pore blockage and cake growth during a membrane filtration process. In particular, the network-based approach provides more spatial resolution than the continuum approach, thereby gives a more in-depth insight into membrane fouling. The network model developed involves the construction of a two-dimensional (2D) network of pores to represent the membrane, a series of probabilistic criteria to describe the fate of each individual particle, and finally a protocol for evaluating the fouling during either a constant flux or constant TMP (transmembrane pressure) filtration. Three fouling parameters can be obtained to characterize the fouling behavior, namely, the probabilistic factor for deposition in the dead zone (β), the initial cake resistance (Rc0), and the specific cake resistance with respect to cake thickness (R′c), by best-fitting the experimental flux-decline data to the network model. The capability of the network model to account for the topological and stochastic aspects of a fouling process provides for a more mechanistic understanding of the complex interactions between the fluid flow, membrane, and foulant particles vis-à-vis a model based on the continuum assumption. Although limited fouling cases were examined in the current study, it is expected that the network model developed here can be readily applied to study more complex phenomena involved in a membrane filtration process (e.g., shear-induced diffusion), and the associated insights would be significantly enhanced when coupled with more advanced fouling characterization techniques (e.g., Optical Coherence Tomography).