X-ray microtomography investigations on deep bed filtration

• Applicant: Professor Dr. Stefan Odenbach
• Subject Area: Fluid Mechanics
• Term: from 2013 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 234452889

Within the project experimental investigations, using X-ray microtomography will be used to examine deep bed filtration processes. The major goal of these investigations will be an enhanced understanding of the deposition mechanisms and a quantitative understanding of the concentration profiles of the deposits in the filter as a function of filtration parameters.Within the first phase of the project, we have developed a technique to quantify the content of different phases within a tomogram of a deep bed filter with deposited particles. This technique allows the quantification of the portion of different species of a multi-component system in each single voxel of the tomogram. Thus, it became possible to analyze depositions smaller than the resolution limit of the tomography setup. Within the second phase of the project, we will analyze in detail deviations from the usual deposition profiles with an exponential concentration decay over the height of the filter bed. By tailored composition of the particle size distribution in the suspensions used for filtration, we will try to test the hypothesis that the observed deposition background, growing in time, is a result of the polydispersity of the suspensions. In addition, first investigations concerning the influence of air bubbles in the filter have been performed in the first project phase. Due to their technical relevance, we plan to develop these investigations further to quantify the importance of air bubbles for the filtration process. As an overall goal of the project, we intend to gain an enhanced understanding of the deposition kinetics in deep bed filtration and its dependence on the most important process parameters based on microscopic information about deposits and filter bed geometry.

• DFG Programme: Research Grants