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Husab Uranium Mine is a super-large-scale open-pit uranium mine. Currently, the mine adopts a “one-time design, long-term use” approach to blasting production, leading to issues such as a lack of dynamic adjustment of blasting parameters, high explosive consumption, and unsatisfactory blasting results. To address these issues, a solution can be achieved through dynamic blastability classification management of blasting blocks and feedbackcontrolled blast design. This study utilizes the production history big data of the mine's blasting blocks. It proposes a method to calculate the blasting index K using drilling rate (α), explosive consumption per unit volume (β), and fragmentation index (γ). Here, α represents the drill hole cross-sectional area per unit area, where a higher value indicates more drilling required and higher drilling costs. β represents the amount of explosives required per unit volume of crushed rock, where a higher value implies a more significant amount of explosives required and higher blasting costs. γ represents the distribution of fragment size after ore blasting, where a higher value indicates worse blasting effects, higher transportation costs, and greater difficulty in blasting. Based on the value of the blasting index K, the blastability of historical blasting blocks is classified into different levels. Uniaxial compressive strength (UCS) of the blasting blocks, rock quality designation (RQD) of the ore, and geological strength index (GSI) of the ore deposit are used as blastability indicators, establishing a dataset correlating blastability indicators with blastability levels. The dataset consists of 69 sets of historical data, with 20 sets classified as level one (easily blastable), 24 sets as level two (relatively difficult to blast), and 25 sets as level three (difficult to blast). Subsequently, a deep learning neural network model is constructed, comprising an input layer, five hidden layers, a dropout layer, and an output layer. The model is trained using blastability indicators as inputs and blastability levels as outputs. The traditional SVM model is used for comparison, revealing that the trained deep learning neural network model achieves higher prediction accuracy on the test set than the traditional SVM model. Finally, the reliability and accuracy of the trained deep learning neural network model in predicting the blastability level of blasting blocks are verified through on-site experiments, optimizing the blast design and blasting effects. The research findings indicate that the trained deep learning neural network model, based on a large amount of historical production data from Husab Uranium Mine, can be used for blastability classification of blasting blocks and optimization of blasting effects.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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