Literature DB >> 20570271

Statistical analysis of packed beds, the origin of short-range disorder, and its impact on eddy dispersion.

Siarhei Khirevich1, Anton Daneyko, Alexandra Höltzel, Andreas Seidel-Morgenstern, Ulrich Tallarek.   

Abstract

We quantified the microstructural disorder of packed beds and correlated it with the resulting eddy dispersion. For this purpose we designed a set of bulk (unconfined) monodisperse random sphere packings with a systematic, protocol-dependent degree of microstructural heterogeneity, covering a porosity range from the random-close to the random-loose packing limit (ε = 0.366-0.46). With the precise knowledge of particle positions, size, and shape we conducted a Voronoiï tessellation of all packings and correlated the statistical moments of the Voronoiï volume distributions (standard deviation and skewness) with the porosity and the protocol-dependent microstructural disorder. The deviation of the Voronoiï volume distributions from the delta function of a crystalline packing describes the origin of short-range disorder of the investigated random packings. Eddy dispersion was simulated over a wide range of reduced velocities (0.5 ≤ ν ≤ 750) and analyzed with the comprehensive Giddings equation. Transient dispersion was found to correlate with the spatial scales of heterogeneity in the packings. The analysis of short-range disorder based on the Voronoiï volume distributions revealed a strong correlation with the short-range interchannel contribution to eddy dispersion, whereas transchannel dispersion was relatively little affected. The presented approach defines a strictly scientific route to the key morphology-transport relationships of current and future chromatographic supports, including their morphological reconstruction, statistical analysis, and the correlation with relevant transport phenomena. It also guides us in our understanding, comparison, and optimization of the diverse packing algorithms and protocols used in simulations and experimental studies.

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Year:  2010        PMID: 20570271     DOI: 10.1016/j.chroma.2010.05.019

Source DB:  PubMed          Journal:  J Chromatogr A        ISSN: 0021-9673            Impact factor:   4.759


  3 in total

1.  Modeling of flow in a polymeric chromatographic monolith.

Authors:  Harun Koku; Robert S Maier; Kirk J Czymmek; Mark R Schure; Abraham M Lenhoff
Journal:  J Chromatogr A       Date:  2011-04-04       Impact factor: 4.759

2.  Modeling of protein electrophoresis in silica colloidal crystals having brush layers of polyacrylamide.

Authors:  Robert E Birdsall; Brooke M Koshel; Yimin Hua; Saliya N Ratnayaka; Mary J Wirth
Journal:  Electrophoresis       Date:  2013-02-13       Impact factor: 3.535

3.  Prediction of the performance of pre-packed purification columns through machine learning.

Authors:  Qihao Jiang; Sohan Seth; Theresa Scharl; Tim Schroeder; Alois Jungbauer; Simone Dimartino
Journal:  J Sep Sci       Date:  2022-03-20       Impact factor: 3.614

  3 in total

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