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Prediction of Separation Indices of Shaking Table Based on XGBoost
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Wei HE1, Haolin DAI1, 2, Wen CHEN1
Mining and Metallurgical Engineering | 2025, 45(1) : 70 - 75
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Mining and Metallurgical Engineering | 2025, 45(1): 70-75
• MINERAL PROCESSING •
Prediction of Separation Indices of Shaking Table Based on XGBoost
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Wei HE1, Haolin DAI1, 2, Wen CHEN1
Affiliations
  • 1.Changsha Research Institute of Mining and Metallurgy Co., Ltd., Changsha 410012, Hunan, China
  • 2.School of Automation, Central South University, Changsha 410083, Hunan, China
Published: 2025-02-01 doi: 10.3969/j.issn.0253-6099.2025.01.013
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A prediction method for mineral processing indices of shaking table was proposed based on eXtreme Gradient Boosting (XGBoost). As for the separation product zone, it can quantify the colour difference by colour moments, the shape characteristics by image moments, and the texture characteristics by evaluation indices of grey-level co-occurrence matrix, including contrast, homogeneity, correlation and ASM energy. The features of the separation product zone can be effectively extracted according to the difference in its colour, shape and texture during separation process. Subsequently, those features can be filtered by employing XGBoost, with which a prediction model can be then constructed. After being trained with the test set to predict the grade, recovery rate and yield of concentrate, this model can achieve accurate prediction of separation indices. It is found that the XGBoost-based model outperforms the decision tree model and random forest model in terms of accuracy when applied for predicting recovery and yield of concentrate.

shaking table  /  feature extraction  /  grade prediction  /  XGBoost  /  feature selection  /  image recognition  /  intelligent control  /  separation index  /  prediction model  /  separation product zone
Wei HE, Haolin DAI, Wen CHEN. Prediction of Separation Indices of Shaking Table Based on XGBoost[J]. Mining and Metallurgical Engineering, 2025 , 45 (1) : 70 -75 . DOI: 10.3969/j.issn.0253-6099.2025.01.013
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Year 2025 volume 45 Issue 1
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Article Info
doi: 10.3969/j.issn.0253-6099.2025.01.013
  • Receive Date：2024-09-06
  • Online Date：2026-03-17
  • Published：2025-02-01
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  • Received：2024-09-06
Affiliations
    1.Changsha Research Institute of Mining and Metallurgy Co., Ltd., Changsha 410012, Hunan, China
    2.School of Automation, Central South University, Changsha 410083, Hunan, 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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