Comparison of particle size distribution of celestite mineral by machine vision ΣVolume approach and mechanical sieving

A reliable and accurate measurement of particle size and particle size distribution (PSD) is central to characterization of particulate minerals. Using mineral celestite (SrSO4) as the test material, an inexpensive machine vision approach as an alternative to standard mechanical sieving was proposed and results were compared. The machine vision approach used a user-coded ImageJ plugin that processed the digital image in a sieveless manner and automated the PSD analysis. A new approach of employing sum of volumes (ΣVolume) as weighting factor was developed and utilized in the ASABE standard PSD analysis. The plugin also evaluated 22 significant dimensions characterizing samples and 21 PSD parameters. According to Folk and Ward's classification, the PSD of ball-milled celestite was “very finely skewed” and “leptokurtic”. The PSD of celestite followed a lognormal distribution, and the plot against particle size exhibited almost a linear trend for both machine vision and mechanical sieving methods. The cumulative undersize PSD characteristics of both methods matched closely when the width-based mechanical sieving results were transformed to lengths by applying the shape factor (width/length). Based on the study, this machine vision approach can be utilized for PSD analysis of particulate minerals and similar products.

Graphical abstractA new machine vision approach of employing sum of volumes (Σ Volume) as weighting factor and distinct particle lengths grouping evaluated the particle size distribution (PSD) of celestite minerals from images. Developed ImageJ plugin evaluated several dimensional and PSD parameters, and produced PSD comparable to mechanical sieving. This machine vision approach was accurate and inexpensive, and applicable to other particulate materials.Figure optionsDownload full-size imageDownload as PowerPoint slideHighlights► ImageJ machine vision plugin evaluated celestite particle size distribution (PSD). ► Σ Volume served as a good weighing factor and gave logical grouping of particles. ► PSD results of machine vision and length-based mechanical sieving were comparable. ► Machine vision method is accurate, inexpensive, versatile, and widely applicable.