Work

A Gated Recurrent Unit and Physics-Informed Neural Network-Based Method for Throughput Prediction of High-Pressure Grinding Rolls

Year: 2026

Type: article

Abstract: As an important part in the mining crushing and grinding circuit, the high-pressure grinding roll (HPGR) is essential for energy efficiency, cost reduction, quality improvement and efficiency gains; therefore, it plays a key role in sustaining stable production and intelligent control of mining operations. Its instantaneous throughput and processing capacity directly influence the efficiency of the comminution system, the compatibility of production scheduling, and overall energy consumption. Therefore, these metrics serve as important indicators for intelligent optimization and stable operation. However, it is a difficult task to predict the throughput of HPGRs by traditional purely data-driven models. The process is characterized by strong nonlinearity, significant time-lag effects, and limited physical consistency. To address these challenges, this work develops a tailored throughput prediction framework for HPGRs by combining Gated Recurrent Units with Physics-Informed Neural Networks (GRU-PINN), which integrates an HPGR-specific volumetric throughput mechanism as a dedicated physical constraint. This approach first exploits the GRU network’s “reset” and “update” gates to selectively filter historical operating information while adaptively updating the current process features. This allows the model to better capture complex long-term temporal dependencies in the production data. Additionally, based on volumetric analysis and the principles of bed comminution, the HPGR throughput formula is incorporated into the loss function as a physical constraint. Together, these components establish a joint optimization framework that combines data-driven learning with physical constraints to correct prediction biases generated by the neural network during abrupt changes in operating conditions. The proposed GRU-PINN model was validated using real operational data collected from an industrial mining site. The results show that the proposed framework significantly improves the physical consistency of HPGR throughput predictions and, at the same time, effectively corrects prediction biases, which are commonly observed in conventional purely data-driven models under complex operating conditions. It also exhibits improved robustness and lower inference latency. Compared with other benchmark models, the proposed method displays superior overall performance across key evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), prediction accuracy, and inference speed. These findings confirm that the proposed method greatly enhances the physical interpretability of the prediction model without compromising accuracy. Consequently, it lays a solid foundation for process parameter optimization and intelligent control of HPGR system.

Source: Automation

DOI: 10.3390/automation7050135