Particle size distribution analysis of ground coal by machine vision ?volume approach

Abstract

Coal is the most widely used fossil fuel in the world, providing more than 25% of world's electrical power. Since, the behavior of coal particles in crushing and grinding circuits, concentration operations, and solid-liquid separations is strongly dependent on size, a reliable and accurate measurement of particle size and particle size distribution (PSD) is a vital aspect in coal cleaning. This paper presents a method for the characterization of ground coal particles using image analysis. We utilized a machine vision (MV) approach that used a document scanner as the imaging device and a user-coded ImageJ plug-in that processed the image and automated the PSD analysis and output results in textual and graphical forms. This approach used a sum of volumes (?Volume) as weighting factor for particle length as the primary dimension of analysis and was successfully applied to ground coal. The plug-in was further improved for computational performance in this study. Mechanical sieving (MS) was also used to compare the MV results. MV results showed that the PSD of ground coal followed a uni-modal normal distribution, and the log-normal plot against particle size exhibited a linear trend for most of the range. MS results, however, had three linear segments, when the width-based sieving results were transformed to lengths by applying the shape factor (width/length), but a definite deviation between the PSD plots was observed. This study demonstrates successful application of the MV ?Volume approach for PSD analysis of ground coal, which can be extended to similar particulate minerals and products.