Dual-energy X-ray transmission identification method of multi-thickness coal and gangue based on SVM distance transformation

Abstract

By weakening the influence of dual-energy X-ray transmission (DE-XRT) thickness on the coal and gangue identification, this study aims to reveal the effect of thickness, density, and coal type on sorting. Considering the imaging law of coal and gangue in R-I two-dimensional extended space, on the basis of particle swarm optimization (PSO) and support vector machine (SVM), a distance transform image processing and identification method is provided in this study. For this method, the distance between the (R, I) coordinates of coal and gangue and the SVM hyperplane is expressed as the relative density, indicating the details of the internal density change of the coal and gangue target. The average value ME of a single target distance d and the proportion RA of positive value of d can be used as sorting parameters to adjust the sorting. Both ME and RA are verified to have high correlation with density. When the gangue density is higher than 1.8 g/cm3, ME and RA are set to 0.05 and 50%, respectively, thereby realizing the gangue pre-discharge of 5 ∼ 150 mm thick raw coal. Raw coal pre-discharge has the identification rate of 100%, and the recognition rate of different densities and coal types mixed is 96.32%. Within the thickness range of 5 ∼ 150 mm, ME and RA still depend on thickness. By adjusting ME and RA, coal with large thickness and low density may be excluded by mistake. Compared with RA, ME exhibits a higher correlation with density and a lower correlation with thickness. Hence, ME can be selected to participate in regulatory sorting, thereby obtaining more accurate results. For parameter selection and regulation of DE-XRT separation of multiple thicknesses, densities, and coal types, this study provides a new method and practical guidance.