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The aim is to compare deterministic and stochastic machine learning approaches for accurate prediction of SAG mill power and production.

April 18, 2026MineralsOpen Access

Comprehensive Comparison of Machine Learning Approaches—Deterministic and Stochastic—In Modeling the Production and Power of an SAG Mill: A Case Study of the Chilean Copper Mining Industry

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The aim is to compare deterministic and stochastic machine learning approaches for accurate prediction of SAG mill power and production.
Compared various machine learning algorithms including deterministic and stochastic models.
Utilized industrial data from a Chilean copper mining operation for model fitting and validation.
Evaluated models under different operational conditions to assess predictive performance.
XGB model showed the best predictive performance with RMSE and R2 values for SAG Production at 87.98 and 97.35%, and for SAG Power at 431.11 and 95.11%.
Stochastic models provided uncertainty quantification, enhancing decision-making under varying conditions.
XGB performed best in Thick hydraulic regime, indicating more reliable predictions with dense pulp operations.

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Compares machine learning models for predicting production and power in copper mining, indicating optimal choices for operational efficiency.

Saldana et al. (2026) studied this question.

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Compares machine learning models for predicting production and power in copper mining, indicating optimal choices for operational efficiency.