Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching

Key points

• This study develops a parameter-conditioned physics-guided machine-learning (PGML) surrogate and demonstrates its use in simulated saturation control.1
• A dataset of 4096 numerical Richards-equation trajectories was divided into 2867 training, 614 validation and 615 evaluation cases.1
• A daily predictive controller compared 200 constant irrigation rates over a 24-hour horizon.1
• The results support a reusable physics-guided surrogate for large-scale saturation control in simulation.1

Sources (1)

• [1]Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 10:11 PM

  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.

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