Uranium ore preconcentration is a critical step in achieving environmentally sustainable uranium mining and reducing the operational load of hydrometallurgical processing systems. Conventional radioactive sorting systems predominantly employ a “single-ore-particle intermittent measurement” mode. Under continuous ore flow and high-throughput operating conditions, however, the radiation fields of adjacent ore particles inevitably overlap, which results in gamma-counting interference and blurred ore-segment boundaries, thereby limiting sorting accuracy and system capacity. To address these challenges, this study established a convolutional model that describes the relationship between ore-grade distribution and gamma-response characteristics under continuous ore flow conditions. On this basis, a deconvolution-based method for uranium ore grade calculation was proposed, and an adaptive determination strategy for the characteristic parameter α was introduced to improve grade estimation accuracy and enable reliable identification of ore-segment boundaries. The experimental results showed that, for uranium grades ranging from 0.05% to 0.18% and ore-segment lengths of 16–40 cm, the relative errors between the inverted and true grades of individual segments were all less than 10%. Compared with conventional intermittent measurement and identification schemes, the proposed method achieves stable and accurate grade inversion under conditions of overlapping radiation fields in continuous ore segments.
Wang et al. (Sat,) studied this question.