Image data from µ-CT measurements of filter cakes, which serve as a model basis and validation of the artificial filter cakes for the publication. abstract: In order to link the properties of a feed stream to those of a product stream in separation operations such as cake filtration, a more comprehensive database is required. For this purpose, a stochastic 3D model for generation of virtual filter cake structures is constructed and calibrated to tomographic image data of experimentally built filter cake structures. Within this model, artificial particles are simulated using mixed Gaussian random fields on a sphere, which are then spatially arranged to form a three-dimensional artificial filter cake that is validated with respect to geometric descriptors that were not used for model fitting, such as constrictivity, porosity, tortuosity and specific surface area. By adjusting interpretable parameters of the model, a large data base of systematically varying artificial filter cakes can be generated, allowing for transfer functions to be determined and structure-property relationships to be statistically investigated.

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