Machine vision based particle size and size distribution determination of airborne dust particles of wood and bark pellets

Abstract Dust management strategies in industrial environment, especially of airborne dust, require quantification and measurement of size and size distribution of the particles. Advanced specialized instruments that measure airborne particle size and size distribution apply indirect methods that involve light scattering, acoustic spectroscopy, and laser diffraction. In this research, we propose a simple and direct method of airborne dust particle dimensional measurement and size distribution analysis using machine vision. The method involves development of a user-coded ImageJ plugin that measures particle length and width and analyzes size distribution of particles based on particle length from high resolution scan images. Test materials were airborne dust from soft pine wood sawdust pellets and ground pine tree bark pellets. Subsamples prepared by dividing the original dust using 230 mesh (63 μm) sieve were analyzed as well. A flatbed document scanner acquired the digital images of the dust particles. Proper sampling, layout of dust particles in singulated arrangement, good contrast smooth background, high resolution images, and accurate algorithm are essential for reliable analysis. A “halo effect” around grey-scale images ensured correct threshold limits. The measurement algorithm used Feret's diameter for particle length and “pixel-march” technique for particle width. Particle size distribution was analyzed in a sieveless manner after grouping particles according to their distinct lengths, and several significant dimensions and parameters of particle size distribution were evaluated. Results of the measurement and analysis were presented in textual and graphical formats. The developed plugin was evaluated to have a dimension measurement accuracy in excess of 98.9% and a computer speed of analysis of