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Modeling the separation performance of depth filter considering tomographic data

doi: 10.1002/ep.13423
AbstractFibrous depth filters are frequently used for the purification of gas streams with low dust loadings, as well as processes where a high initial filtration efficiency is required (e.g., clean rooms for aseptic production). One tool suitable for supporting the development of optimized filter media is the use of numerical simulations. The drawback of this technique is the high computational resources required. In this work, a new and fast approach based on a one‐dimensional model was applied. Structural characteristics (e.g., porosity distribution and fiber diameter) of two different filter media were successfully determined using a novel X‐ray microscope. These characteristics were incorporated in the filtration model, and their influence on the calculations was evaluated. It was found that the porosity distribution does have an impact on local (microscopic) deposition rates, but only a minor influence on the macroscopic filtration efficiency (around 3%). Benefits of the model are the application of measured structural data and the low computational expense. Compared to experimental data (VDI 3926 / ISO 11057), the prediction of the filtration efficiency can be improved by incorporating the structural data in the model.
- Anhalt University of Applied Sciences Germany
- Anhalt University of Applied Sciences Germany
- TU Dortmund University Germany
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).9 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
