Study on Radiometric Sorting of Uranium Ore Based on Deconvolution

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.