Mechanical filtration of a fluid is typically described by the transport of colloids in porous media. Its prediction, addressed by CFD methods, is important in many practical applications, including stormwater filters and air cyclone bag filters. Recently, for 2D packed bed-type filters, Marcato et al. [1] developed a computational workflow that systematically connects the filter microstructure to the filtration capacity. However, they did not address the accumulation of colloids on the solid particles, which, eventually, results in clogging, and hence the flow patterns will change. The clogging becomes more profound as the filter ages which will force the pressure gradient to keep increasing all the time. Yue et al. [2] attempted to model the colloid accumulation in filters using a coupled CFDDEM model. In their model, the accumulating substance is described by a set of particles that can collide and be deposited on the solid fibers in the flow field. This approach is computationally very expensive, hence, one can not model the very long evolution of clogging and filter aging. In this work, we present a computational approach that couples the advection-diffusion model [1] to a microstructure flow model for a filter. The model predicts the filtering efficiency and the remained mass of the dirt (due to filtering) in a given filter design. Based on this, the model predicts the potential areas of clogging with the mass/volume of the accumulated dirt. The volume/mass of clogging, which is evolving with filtering, is then fed back into the flow equations as a sink/source term to model the effects of clogging in the flow fields. In this way, the present model shows the filtering efficiency and clogging (where and how fast) with filter aging. This model also paves the way for studying filter material and/or design optimization, as well as dynamical control policies to remove the accumulated dirt by pressure pulses. REFERENCES [1] A. Marcato, G. Boccardo and D. Marchisio, A computational workflow to study particle transport and filtration in porous media: Coupling CFD and deep learning. Chem. Eng. J., Vol. 417, article id 128936, 2021. [2] C. Yue, Q. Zhang and Z. Zhai, Numerical simulation of the filtration process in fibrous filters using CFD-DEM method. J. Aeros. Sci. Vol. 101, pp. 174-87, 2016