This study reveals the importance of the module geometry on the flow field and pressure distribution during membrane permeation for multibore membranes. The pathways of permeation are unraveled within a custom-made single multibore membrane module. For this, we combine flow velocimetry of magnetic resonance imaging (MRI) with computational fluid dynamic (CFD) simulations and permeation experiments. First, a systematic simulation study identifies flow patterns based on simplified geometry features that are supported experimentally through flow-MRI measurements. This comprehensive study shows how small geometric deviations from the idealistic assumptions result in unexpected fluid flow on the shell and lumen side in the module. Second, the influence of those non-ideal flow patterns during the filtration of silica particles are revealed by MRI. The results indicate heterogeneous silica deposition due to geometry induced flow fields. Contrary to the idealized assumption, the subsequent backwashing is also influenced by those deposition patterns. Hence, unavoidable non-idealities of membrane positioning during the construction of the module influence the performance of the membrane filtration. With this study, we stimulate to analyze and pioneer new strategies to fully recover the membrane's performance after filtration cycles during backwashing based on a precisely designed backwash optimized permeate channel geometry in membrane modules.

Synthetic membranes for desalination and ion separation processes are a prerequisite for the supply of safe and sufficient drinking water as well as smart process water tailored to its application. This requires a versatile membrane fabrication methodology. Starting from an extensive set of new ion separation membranes synthesized with a layer-by-layer methodology, we demonstrate for the first time that an artificial neural network (ANN) can predict ion retention and water flux values based on membrane fabrication conditions. The predictive ANN is used in a local single-objective optimization approach to identify manufacturing conditions that improve permeability of existing membranes. A deterministic global multi-objective optimization is performed in order to identify the upper bound (Pareto front) of the delicate trade-off between ion retention characteristics and permeability. Ultimately, a coupling of the ANN into a hybrid model enables physical insight into the influence of fabrication conditions on apparent membrane properties.

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