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Production Prediction of In-situ Leaching Mining Area Based on Artificial Intelligence Algorithm
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Hao JIA1, 2, Wensheng LIAO1, 2, Zhiming DU1, 2, Yaan WANG1, 2, Limin WANG1, 2
Uranium Mining and Metallurgy | 2023, 42(3) : 10 - 16
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Uranium Mining and Metallurgy | 2023, 42(3): 10-16
• MINING AND HYDROMETALLURGY •
Production Prediction of In-situ Leaching Mining Area Based on Artificial Intelligence Algorithm
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Hao JIA1, 2, Wensheng LIAO1, 2, Zhiming DU1, 2, Yaan WANG1, 2, Limin WANG1, 2
Affiliations
  • 1.Beijing Research Institute of Chemical Engineering and Metallurgy, CNNC, Beijing 101149, China
  • 2.China Nuclear Mining Science and Technology Corporation, CNNC, Beijing 101149, China
Published: 2023-08-20 doi: 10.13426/j.cnki.yky.2023.02.07
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At the later stage of production in the uranium mining area, the metal production usually continues to decline, and the metal concentration also changes with the influence of the intensified leaching process. At this time, the production cannot be accurately calculated by the liquid pumping volume and the concentration. Based on the induction and analysis of production parameters, a multiple linear regression model and an artificial neural network model are established for the prediction of daily metal production in a single mining area. The results show that both of them have good accuracy and generalization ability, the effect of artificial neural network model is better, the average error of metal daily production prediction is only -0.36%, the outliers are less and the error distribution is balanced. The accuracy of this method is significantly improved compared with the simple calculation to obtain the daily metal production. The use of artificial neural network model to predict the production of the mining area can provide solutions and theoretical guidance for the adjustment and change of leaching process and production system.

in-situ leaching uranium  /  production prediction  /  ANN  /  multiple linear regression
贾皓, 廖文胜, 杜志明, 王亚安, 王立民. 基于人工智能算法的地浸采区产量预测. 铀矿冶, 2023 , 42 (3) : 10 -16 . DOI: 10.13426/j.cnki.yky.2023.02.07
Hao JIA, Wensheng LIAO, Zhiming DU, Yaan WANG, Limin WANG. Production Prediction of In-situ Leaching Mining Area Based on Artificial Intelligence Algorithm[J]. Uranium Mining and Metallurgy, 2023 , 42 (3) : 10 -16 . DOI: 10.13426/j.cnki.yky.2023.02.07
Appendix
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Year 2023 volume 42 Issue 3
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doi: 10.13426/j.cnki.yky.2023.02.07
  • Receive Date：2023-02-15
  • Online Date：2026-10-23
  • Published：2023-08-20
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  • Received：2023-02-15
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Affiliations
    1.Beijing Research Institute of Chemical Engineering and Metallurgy, CNNC, Beijing 101149, China
    2.China Nuclear Mining Science and Technology Corporation, CNNC, Beijing 101149, China
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表12种不同金属材料的力学参数
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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