AI-POWERED PREDICTION & OPTIMIZATION
Revolutionizing Uranium Leaching with Advanced Deep Learning
Published: Wei et al. Journal of Analytical Science and Technology (2026) 17:14
This study introduces a deep learning model, CNN-GRU-Attention, to improve the accuracy of uranium concentration prediction and optimize acid injection in in-situ uranium leaching. By integrating CNNs for local feature extraction, GRUs for temporal modeling, and an attention mechanism for critical information enhancement, the model dynamically forecasts trends in extraction uranium concentration. Experimental results demonstrate superior performance (MAE: 0.0543, MSE: 0.0315, R²: 0.915) compared to LSTM, CNN-LSTM, and Transformer models, especially during abrupt concentration changes. Ablation studies confirm the significant contributions of CNN and attention modules. Single-variable simulation analysis reveals a "rise-then-stabilize" pattern for uranium concentration with increasing acid injection, leading to the determination of optimal acid injection concentrations for different residual uranium reserves (11.69 g/L, 10.85 g/L, 11.41 g/L, and 10.99 g/L), thereby achieving production optimization while balancing leaching efficiency and cost.
Executive Impact Summary
This deep learning framework offers a robust solution for predicting uranium extraction and optimizing acid injection, leading to enhanced efficiency and cost savings in mining operations. The model's superior accuracy enables precise control, reducing waste and maximizing resource recovery.
0.0543
Mean Absolute Error (MAE)
0.0315
Mean Squared Error (MSE)
0.915
Coefficient of Determination (R²)
Deep Analysis & Enterprise Applications
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Model Architecture: CNN-GRU-Attention
The proposed model synergistically integrates Convolutional Neural Networks (CNNs) for efficient local feature extraction, Gated Recurrent Units (GRUs) for robust temporal sequence modeling, and a Multi-Head Attention mechanism for dynamically enhancing critical information. This combined approach allows the model to capture short-term variation patterns, long-term dependencies, and assign adaptive importance weights to different features across time steps, significantly improving prediction accuracy and interpretability in complex mining environments.
Experimental Validation: Robustness & Sensitivity
To ensure model robustness and generalizability, a five-fold cross-validation strategy was employed. The average R² of approximately 0.891 across folds, with minor fluctuations, confirms stable predictive performance. Hyperparameter sensitivity studies identified optimal settings for learning rate (0.001), input sequence length (1), and training epochs (400), demonstrating careful tuning for peak performance without overfitting. These rigorous validations confirm the model's reliability in practical applications.
Performance Comparison: Superior Accuracy
The CNN-GRU-Attention model consistently outperformed baseline deep learning models, including LSTM, CNN-LSTM, and Transformer, across all evaluation metrics. Achieved MAE of 0.0543, MSE of 0.0315, and R² of 0.915 highlight its superior predictive accuracy and stability. This advantage is particularly pronounced in segments with abrupt concentration changes, underscoring the effectiveness of integrating attention mechanisms for capturing dynamic temporal features.
Optimization Analysis: Optimal Acid Injection Strategy
Single-variable simulation analysis revealed a "rise-then-stabilize" pattern for uranium concentration with increasing acid injection. This insight enabled the determination of optimal acid injection concentrations for various residual uranium reserves: 11.69 g/L (30 t), 10.85 g/L (25 t), 11.41 g/L (20 t), and 10.99 g/L (15 t). These optimized concentrations balance maximum uranium leaching efficiency with acid consumption, leading to significant cost savings and improved economic viability for mining operations.
0.915
R² on test set (Coefficient of Determination)
The model explains 91.5% of the data variance, indicating high predictive accuracy.
Enterprise Process Flow
Data Collection & Preprocessing
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Feature Selection
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Data Normalization & Sample Construction
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Model Training & Optimization
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Performance Evaluation
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Results & Analysis
| Model | MAE | MSE | R² |
|---|---|---|---|
| CNN-GRU-Attention | 0.0543 | 0.0315 | 0.915 |
| Transformer | 0.0623 | 0.0362 | 0.854 |
| CNN-LSTM | 0.0691 | 0.0403 | 0.803 |
| LSTM | 0.0767 | 0.0497 | 0.751 |
The proposed CNN-GRU-Attention model significantly outperforms other deep learning models in all key metrics, especially in capturing abrupt concentration changes.
Optimized Acid Injection for Uranium Leaching
Balancing Efficiency and Cost
The study reveals that as injection acid concentration increases, extraction uranium concentration generally exhibits a 'rise-then-stabilize' pattern. Optimal acid injection concentrations were determined for various residual uranium reserves: 11.69 g/L (30 t), 10.85 g/L (25 t), 11.41 g/L (20 t), and 10.99 g/L (15 t). This optimization balances maximum uranium leaching with acid consumption, leading to enhanced economic viability.
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Annual Savings Potential
$0
Hours Reclaimed Annually
0
Your Implementation Roadmap
A structured approach to integrating AI into your uranium leaching operations for optimal results.
Data Collection & Preprocessing
Gathering historical operational data, cleaning, imputing missing values, and outlier correction. Standardizing data for model readiness.
Model Development & Training
Constructing and training the CNN-GRU-Attention model using selected features. Hyperparameter tuning and cross-validation to ensure robustness.
Validation & Optimization
Evaluating model performance against test data using MAE, MSE, and R². Conducting ablation studies to confirm module contributions. Performing sensitivity analysis for injection acid concentration.
Deployment & Monitoring
Integrating the trained model into a closed-loop control system for online prediction and dynamic optimization of acid injection. Continuous monitoring and retraining to adapt to changing conditions.
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