Particle size distribution analysis of pine sawdust: comparison of traditional oscillating screen method and photo-optical analysis
Czech University of Life Sciences Prague, Faculty of Tropical AgriSciences, Department of Sustainable Technologies, Kamýcká 129, CZ165 21 Prague, Czech Republic
*Correspondence: ivanova@ftz.czu.cz
Abstract:
Particle size and particle size distribution (PSD) are crucial parameters which affect properties of particulate and agglomerated materials, and have an impact on a quality and utilization of a final product. The aim of this paper was to determine PSD as well as to assess dimensional features of pine sawdust fractions via mechanical sieve analysis and photo-optical analysis. The first one is a traditional and standard method taking into account only one parameter of particle shape and the second one is a modern method based on a digital image processing that considers also irregular shapes of biomass particles. Pine sawdust was grinded into three fractions: 4, 8 and 12 mm and analysed using two mentioned methods. A horizontal vibrating sieve shaker comprising 11 sieves and a bottom pan was used, and the obtained data of retained particles on each sieve were evaluated. For comparison, a computerized photo-optical particle analyser was applied with max Feret’s diameter as a measurement algorithm for a particle length, and PSD was analyzed by grouping the particles according to their distinct lengths adjusted to the sieves’ sizes used in the screening method. Moreover, additional results in dimensions and parameters of PSD were obtained and evaluated through the photo-optical method. Pine sawdust particles can be described as non-uniform, mainly prolonged, finer particles dominated in all fraction samples. The study showed differences in the results, inaccuracy and other drawbacks of the conventional sieving method such as clogging and falling-through phenomena as well as the limitations of the machine vision. Strong sides of both methods were discussed, too. Overall, the results contributed to a better knowledge of the material properties and different methods of PSD analysis.
Key words:
computerized particle analyzer, image analysis, machine vision, mechanical sieving, particle size classification