Manuel Saldana, Edelmira Gálvez
SAG grinding mills represent critical energy-intensive operations in copper concentrators, accounting for 30%–50% of total plant energy consumption. The accurate prediction of mill power draw and production rate under varying operational conditions is essential for real-time control, production planning, and energy management. This study presents a comprehensive comparison of ML algorithms for modeling Production and Power in a Chilean copper mining industry. Deterministic and stochastic models were fitted and validated using industrial data from a Chilean copper operation. More representative models were re-estimated and subsequently evaluated under different operating regimes to examine their predictive performance under aggregated conditions of the feeding variables.
@article{ec96300c-7afc-4369-9941-2386b41d64ba,
title={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},
author={Manuel Saldana and Edelmira Gálvez},
year={2026},
language={English}
}TY - JOUR TI - 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 AU - Manuel Saldana AU - Edelmira Gálvez PY - 2026 LA - English ER -
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