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Physics-guided ML model accelerates saturation control in heap leaching

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a physics-guided machine learning (PGML) surrogate model to predict saturation levels in heap leaching operations. This model, trained on a large dataset of numerical simulations, can predict hourly saturation profiles and is approximately 70 times faster than traditional numerical solvers. When used in a simulated daily predictive controller, the PGML approach achieved comparable tracking accuracy to a tuned PI controller while using less irrigation, though moderate physics guidance slightly worsened closed-loop tracking. AI

IMPACT This physics-guided machine learning approach could enable faster and more efficient simulations for industrial processes like heap leaching.

RANK_REASON This is a research paper detailing a new machine learning model for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Physics-guided ML model accelerates saturation control in heap leaching

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This is a research paper detailing a new machine learning model for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Sultan Alhamdan, Daniel Navia, Paulina Quintanilla ·

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

    arXiv:2610.15947v1 Announce Type: cross Abstract: 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) surr…

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