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Analysis of Influencing Factors of Fluidity and Strength for Ultra-Fine Tailings Filling Materials and Prediction of Proportioning
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Weixin LIN1, 2, Ke JIANG1, 2, Yongchao GONG1, 2, Haoqi JIA1, 2, Zhenjian YU3, Renze OU1, 2, Yuefei JIA3
Mining Research and Development | 2025, 45(11) : 78 - 87
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Mining Research and Development | 2025, 45(11): 78-87
Analysis of Influencing Factors of Fluidity and Strength for Ultra-Fine Tailings Filling Materials and Prediction of Proportioning
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Weixin LIN1, 2, Ke JIANG1, 2, Yongchao GONG1, 2, Haoqi JIA1, 2, Zhenjian YU3, Renze OU1, 2, Yuefei JIA3
Affiliations
  • 1.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
  • 2.State Key Laboratory of Safety Technology of Metal Mine, Changsha, Hunan 410012, China
  • 3.Anhui Development Mining Co., Ltd., Lu'an, Anhui 237191, China
Published: 2025-11-25
Outline
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Taking the ultra-fine mine tailings as the experimental research object, the optimum mass concentration of the slurry under the conditions of gravity flow and pumping was explored through slump tests, strength ratio tests and response surface analysis. The key factors affecting the compressive strength of the backfill were analyzed, and the NGO-BP neural network model was used to predict the strength of the specimen. The results show that the optimum mass concentration is about 74% when the pump pressure is transported, and the optimum mass concentration is about 70% when the gravity flow is transported. When the cement-sand ratio is 1:4, 1:6, 1:8 and 1:20, the 7 d strength of the backfill at each mass concentration reaches 0.8 MPa, and the 28 d strength reaches 1.5 MPa, which meets the strength requirements. When the cement-sand ratio remains unchanged, the strength of the specimen will increase with the increase of mass concentration and the extension of curing age. When other variables remain unchanged, the effect of cement-sand ratio on the strength of the specimen is particularly obvious. The higher the cement-sand ratio, the higher the strength of the specimen. The average absolute error (MAE), average relative percentage error (MAPE), mean square error (MSE), root mean square error (RMSE) predicted by NGO-BP neural network decreases by 0.47, 0.70, 0.96, 0.66, respectively, and the coefficient of determination (R2) increases by 0.26 compared with BP neural network at 7 d of curing. NGO-BP neural network model can be used to guide the filling ratio of production practice, and provide a new idea for the optimization of filling ratio.

Ultra-fine tailings  /  Slump  /  Compressive strength  /  Response surface analysis  /  NGO-BP neural network prediction
林卫星, 江科, 龚永超, 贾皓琦, 于振建, 欧任泽, 贾月飞. 超细尾砂充填材料流动性与强度的影响因素分析以及配比预测研究. 矿业研究与开发, 2025 , 45 (11) : 78 -87 .
Weixin LIN, Ke JIANG, Yongchao GONG, Haoqi JIA, Zhenjian YU, Renze OU, Yuefei JIA. Analysis of Influencing Factors of Fluidity and Strength for Ultra-Fine Tailings Filling Materials and Prediction of Proportioning[J]. Mining Research and Development, 2025 , 45 (11) : 78 -87 .
Year 2025 volume 45 Issue 11
63
34
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Article Info
  • Receive Date:2024-09-10
  • Online Date:2026-10-28
  • Published:2025-11-25
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  • Received:2024-09-10
Funding
Affiliations
    1.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
    2.State Key Laboratory of Safety Technology of Metal Mine, Changsha, Hunan 410012, China
    3.Anhui Development Mining Co., Ltd., Lu'an, Anhui 237191, 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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