Production prediction and synergistic optimization of oxygen and lixiviant injection in neutral in-situ uranium leaching.
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| Title: | Production prediction and synergistic optimization of oxygen and lixiviant injection in neutral in-situ uranium leaching. |
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| Authors: | Liu, Zhifeng1,2 (AUTHOR) zfliu@ecut.edu.cn, Li, Mengjiao1 (AUTHOR), Wei, Zhenhua1,2 (AUTHOR), Wu, Yuhang1 (AUTHOR) |
| Source: | Mining Technology (2572-6668). Jun2026, Vol. 135 Issue 2, p212-223. 12p. |
| Subjects: | Uranium mining, Injection wells, Autoencoders, Machine learning, Forecasting, Mathematical optimization, Water chemistry |
| Abstract: | In neutral in-situ leaching (CO2 + O2) of sandstone-hosted uranium deposits, oxygen injection, lixiviant injection, and hydrochemical conditions are mutually coupled, leading to nonlinear responses of daily uranium output to operating parameters. Meanwhile, field monitoring data are also noisy and incomplete, limiting empirical optimization. This study focuses on seven-spot leaching units in a mining area, constructs a daily-scale dataset including injection/production parameters, hydrochemical indicators of the produced solution, residual uranium inventory, and time-series derived features, and develops a multilayer perceptron prediction model (VAE-SEMLP) that integrates a variational autoencoder (VAE) with a Squeeze-and-Excitation (SE) attention mechanism. Across three representative unit tests, the proposed model achieves an average R2 of 0.95 and a normalized RMSE of 0.06, outperforming support vector regression (SVR), random forest (RF), and XGBoost overall. Ablation experiments further confirm the synergistic gain from combining the VAE and SE modules. Parameter scanning under typical static operating-condition slices reveals a pronounced unimodal response of daily uranium output to lixiviant injection volume, indicating an optimal injection interval that shifts with oxygen-injection level. This work provides data-driven support for daily uranium-output prediction and quantitative optimization of coupled oxygen-lixiviant injection schemes in neutral in-situ uranium leaching. [ABSTRACT FROM AUTHOR] |
| Copyright of Mining Technology (2572-6668) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194757660 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Production prediction and synergistic optimization of oxygen and lixiviant injection in neutral in-situ uranium leaching. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Zhifeng%22">Liu, Zhifeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zfliu@ecut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Mengjiao%22">Li, Mengjiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Zhenhua%22">Wei, Zhenhua</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Yuhang%22">Wu, Yuhang</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mining+Technology+%282572-6668%29%22">Mining Technology (2572-6668)</searchLink>. Jun2026, Vol. 135 Issue 2, p212-223. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Uranium+mining%22">Uranium mining</searchLink><br /><searchLink fieldCode="DE" term="%22Injection+wells%22">Injection wells</searchLink><br /><searchLink fieldCode="DE" term="%22Autoencoders%22">Autoencoders</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Water+chemistry%22">Water chemistry</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In neutral in-situ leaching (CO2 + O2) of sandstone-hosted uranium deposits, oxygen injection, lixiviant injection, and hydrochemical conditions are mutually coupled, leading to nonlinear responses of daily uranium output to operating parameters. Meanwhile, field monitoring data are also noisy and incomplete, limiting empirical optimization. This study focuses on seven-spot leaching units in a mining area, constructs a daily-scale dataset including injection/production parameters, hydrochemical indicators of the produced solution, residual uranium inventory, and time-series derived features, and develops a multilayer perceptron prediction model (VAE-SEMLP) that integrates a variational autoencoder (VAE) with a Squeeze-and-Excitation (SE) attention mechanism. Across three representative unit tests, the proposed model achieves an average R2 of 0.95 and a normalized RMSE of 0.06, outperforming support vector regression (SVR), random forest (RF), and XGBoost overall. Ablation experiments further confirm the synergistic gain from combining the VAE and SE modules. Parameter scanning under typical static operating-condition slices reveals a pronounced unimodal response of daily uranium output to lixiviant injection volume, indicating an optimal injection interval that shifts with oxygen-injection level. This work provides data-driven support for daily uranium-output prediction and quantitative optimization of coupled oxygen-lixiviant injection schemes in neutral in-situ uranium leaching. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mining Technology (2572-6668) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/25726668261447314 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 212 Subjects: – SubjectFull: Uranium mining Type: general – SubjectFull: Injection wells Type: general – SubjectFull: Autoencoders Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Water chemistry Type: general Titles: – TitleFull: Production prediction and synergistic optimization of oxygen and lixiviant injection in neutral in-situ uranium leaching. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Zhifeng – PersonEntity: Name: NameFull: Li, Mengjiao – PersonEntity: Name: NameFull: Wei, Zhenhua – PersonEntity: Name: NameFull: Wu, Yuhang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 25726668 Numbering: – Type: volume Value: 135 – Type: issue Value: 2 Titles: – TitleFull: Mining Technology (2572-6668) Type: main |
| ResultId | 1 |
