Physics-Informed AI for Enhanced Forecasting in Mineral Processing
In the domain of mineral processing, optimal control and forecasting are pivotal for efficient and sustainable operation. As data-driven modeling becomes increasingly prevalent, the challenge lies in achieving both high accuracy and reliability across multiple forecasting steps. This research explores the integration of physics-based knowledge into established data-driven forecasting models—such as CNN, GRU, and LSTM—to enhance predictive performance in a real-world flotation process. By incorporating mass balance constraints through physics-guided loss functions, this study demonstrates how physics-informed machine learning (PIML) can outperform traditional models.
Data-Driven Forecasting Models in Mineral Processing
Data-driven models, particularly deep learning architectures like Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) networks, have gained traction for time series prediction in mineral processing systems. Their ability to capture complex nonlinear patterns from historical sensor data makes them ideal for multistep forecasting. However, their performance often lacks robustness due to the absence of physical understanding, which can lead to unrealistic or physically implausible predictions in critical process applications.
Role of Feature Engineering and Model Harmonization
To ensure fair benchmarking of model performance, the study employed harmonized pre-processing and feature engineering techniques across all models. Standardizing inputs allows a true evaluation of architectural differences and isolates the impact of physics integration. By aligning the data treatment pipeline, the study eliminates bias introduced through varying input representations, making the baseline comparisons more reliable and scientifically valid.
Physics-Guided Loss Functions and Mass Balance Integration
The cornerstone of the physics-informed approach was the inclusion of mass balance constraints through custom loss functions. Two physics-guided loss strategies were tested: one leveraging Mean Absolute Error (MAE) and the other using Mean Squared Error (MSE) combined with mass balance penalties. These hybrid loss functions guide the models toward physically consistent predictions, thereby embedding domain knowledge directly into the training process—a key innovation of this study.
Performance Comparison and Evaluation Metrics
The PIML models consistently outperformed their purely data-driven counterparts across all key metrics. The most significant improvement was observed in LSTM models, especially in terms of Normalized Root Mean Squared Error (NRMSE) and Normalized Mean Absolute Error (NMAE). CNN models, while still benefiting from the integration of physics, exhibited the smallest relative improvement. The use of MAE-based loss functions yielded superior accuracy compared to MSE-based alternatives, suggesting robustness to outliers and better generalization.
Practical Considerations for Implementing PIML in Industry
While physics-informed approaches offer measurable improvements in forecasting accuracy, they come with increased requirements in terms of domain expertise, model complexity, and computational resources. This research underscores the importance of a case-by-case evaluation when choosing to integrate physics. In industrial practice, the trade-offs between performance gain and implementation cost must be carefully balanced to justify the use of PIML over conventional models.
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