Multi-Feature Fusion for Dual-Energy X-Ray Transmission Sorting of Copper and Lead–Zinc Ores

Dual-energy X-ray transmission sorting of copper ores is often constrained by strong coupling between conventional features and particle thickness. To address this, we propose two decoupled features: R3, an improved R-value feature, and Zeff from basis material decomposition, which are designed to mitigate thickness interference and improve high-Z mineral identification. Copper and lead–zinc ore particles from the Yinshan Mine (Dexing, China), with particle thickness ranges of 8–60 mm for the copper ore and 8–25 mm for the lead–zinc ore, were tested on a laboratory DE-XRT sorter. A support vector machine regression model was trained on 314 phyllite copper ore particles (8–60 mm) to predict a weighted comprehensive grade (Cu+Fe+S), using R-value, R3, Zeff, and w3–w2 as inputs. The method showed favorable performance for porphyry copper ores (particle thickness: 8–60 mm): feed grades of 0.068%–0.109% Cu were upgraded to concentrates of 0.127%–0.244% Cu, with waste grades of 0.030%–0.051% and recoveries of 61.69%–79.67%. For phyllite copper ores (particle thickness: 8–60 mm), enrichment was observed through yield adjustment, with recoveries of 33.38%–71.69%. A plausible reason for the poor performance of the intermediate-grade Cu phyllite ore (Cu: 0.098%) is a combination of two factors: the chalcopyrite grain size (~0.1 mm) being below the detector resolution (0.8 mm), which causes mineral signals to be swamped by gangue, and the fundamental limitation of dual-energy XRT in distinguishing Cu from Fe. For lead–zinc ore (4.744% Pb+Zn feed), the sorter rejected 21.99% as waste while achieving 98.87% Pb+Zn recovery, with only 0.245% Pb+Zn in the waste. These results indicate that the proposed multi-feature fusion and SVM regression strategy provides a feasible approach for the preconcentration of low-grade copper ores and waste rejection for lead–zinc ores.