Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching
Heap-leaching hydraulics vary between regions of an ore bed, motivating fast prediction models that accommodate different material properties. This study develops a parameter-conditioned physics-guided machine-learning (PGML) surrogate and demonstrates its use in simulated saturation control. A dataset of 4096 numerical Richards-equation trajectories was divided into 2867 training, 614 validation and 615 evaluation cases. The network predicts hourly saturation profiles from the current profile, irrigation rate and four hydraulic parameters, and is trained with an approximate Gardner mass-balance penalty. A daily predictive controller compared 200 constant irrigation rates over a 24-hour h...
| Authors | Sultan Alhamdan, Daniel Navia, Paulina Quintanilla |
|---|---|
| Primary category | eess.SY |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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