Optimizing gas mass transfer pathways in membrane modules via machine learning-driven offset-perforation column node spacers

The designation of feed spacers in membrane modules is crucial for suppressing feed-side concentration polarization and enhancing gas separation performance. Nevertheless, challenges persist in addressing uneven flow distribution and inadequate mass transfer enhancement, especially for the high-performance membranes. To overcome these limitations, this study proposes a novel spacer design with optimized flow paths utilizing offset-perforation column node spacers. The feed-side flow field was simulated to elucidate mass transfer mechanisms and energy dissipation pathways in offset and perforation configurations. The perforation strategy results in a reduction of symmetric vortex regions and a decrease in the Fanning friction coefficient. This phenomenon transpires due to the elongation of convective mass transfer pathways, thereby augmenting the transport process between the mainstream zone and the membrane surface. The offset strategy disrupts flow symmetry while increasing convective mass transfer pathways, thereby improving mass transfer efficiency. Finally, a model was established using machine learning parameters to predict the extent to which five structural parameters—including perforation angle—contribute to improvements in the Fanning friction coefficient and membrane module processing capacity. The optimal structure should combine the spacer filament diameter (0.6–0.8 times the channel height), perforation angle (60°–70°), perforation diameter (0.25–0.35 times the channel height), and offset distance (0.7–0.8 times gap distance). The channel height is determined based on process requirements to achieve a balance between efficient mass transfer and low energy consumption.