A new method for reconstruction of the structure of micro-packed beds of spherical particles from desktop X-ray microtomography images. Part A. Initial structure generation and porosity determination
2016-04-21T14:08:36Z (GMT) by
Micro-packed beds (μPBs) are seeing increasing use in the process intensification context (e.g. micro-reactors), in separation and purification, particularly in the pharmaceutical and bio-products sectors, and in analytical chemistry. The structure of the stationary phase and of the void space it defines in such columns is of interest because it strongly influences performance. However, instrumental limitations - in particular the limited resolution of various imaging techniques relative to the particle and void space dimensions - have impeded experimental study of the structure of μPBs. We report here a new method that obviates this issue when the μPBs are composed of particles that may be approximated by monodisperse spheres. It achieves this by identifying in successive cross-sectional images of the bed, the approximate centre and diameter of the particle cross-sections, replacing them with circles, and then assembling them to form the particles by identifying correlations between the successive images. Two important novel aspects of the method proposed here are: it does not require specification of a threshold for binarizing the images, and it preserves the underlying spherical geometry of the packing. The new method is demonstrated through its application to a packing of a near-monodispersed 30.5 μm particles of high sphericity within a 200 μm square cross-section column imaged using a machine capable of 2.28 μm resolution. The porosity obtained was, within statistical uncertainty, the same as that determined via a direct method whilst use of a commonly used automatic thresholding technique yielded a result that was nearly 10% adrift, well beyond the experimental uncertainty. Extension of the method to packings of spherical particles that are less monodisperse or of different regular shapes (e.g. ellipsoids) is also discussed.