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| Predicting the Variation of Uranium Leaching Metal Content in Ground-leaching Process Based on Machine Learning Methods |
| Received:October 23, 2023 Revised:November 07, 2023 |
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| DOI：doi：10.3969/j.issn.1007-7545.2024.02.013 |
| KeyWord:ground-leaching uranium mining；linear regression model；machine learning prediction |
| Author | Institution |
| YU Dongyuan |
东华理工大学 水资源与环境工程学院 |
| LUO Yue |
东华理工大学 水资源与环境工程学院 |
| LIANG Daye |
中核内蒙古矿业有限公司;中核内蒙古矿业有限公司 |
| LI Liyao |
东华理工大学 水资源与环境工程学院 |
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| Abstract: |
| In the process of in-situ leaching of uranium, it is of great significance to accurately predict the amount of uranium metal extracted. Multiple machine learning methods such as multiple linear regression, Multi-Layer Perceptron (MLP) and Random forest (RF) were used to build prediction models. The results indicate that: 1) Compared with the traditional multiple linear regression algorithms, MLP and RF methods can obtain models with better prediction performance. 2) The MLP model has the best performance in predicting the change of uranium leaching metal content (R2=0.91). 3) With the same prediction accuracy, the RF model takes less time than that of the MLP model, and the hyperparameters setting is simpler. 4) The weight ratio of the total flow rate to the amount of uranium metal leached is 81.6% when the total flow rate and the amount of uranium leaching metal content per square meter are taken as the key factors. |
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