Intelligent Ore Sorting Method Based on Multi-Feature Fusion of Dual-Energy X-Ray Responses

Abstract

The conventional attenuation-ratio feature R remains affected by particle thickness, material heterogeneity, and beam hardening in practical dual-energy X-ray transmission (DE-XRT) measurements. This study proposes an ore-sorting method based on multi-feature fusion of dual-energy X-ray transmission responses. An aluminium step wedge was used to establish a reference relationship between R and high-energy attenuation αH. The deviation from this relationship was defined as the residual feature Rc. Particle-level statistics of R, αH, and Rc were combined in a linear support vector machine (SVM) classifier. For lead–zinc ore, the SVM was compared with a conventional double-threshold R-value rule. In batch-grouped out-of-fold evaluation, the SVM increased combined Pb–Zn recovery from 90.99% to 97.42% and reduced the combined Pb–Zn grade of the tailings from 2.101% to 0.357%. In an independent 216-particle sorting-and-assay test, the SVM recovered 98.78% of Pb–Zn at 22.03% mass rejection, compared with 90.72% recovery at 26.05% rejection for the conventional R-value rule.