https://creativecommons.org/licenses/by/4.0/Jonathan Castillo SeguraSaldaña, ManuelManuelSaldañaToro, NormanNormanToroMura, MauricioMauricioMuraCastillo, IgnacioIgnacioCastilloAlexis Guzmán MendezGálvez, Edelmira DelfinaEdelmira DelfinaGálvezRossana Sepúlveda Rivera2025-10-102025-10-10202520763417https://hdl.handle.net/20.500.12740/23459Black copper oxides, a significant copper resource, present challenges in leaching due to their refractory nature and complex mineralogical composition. This study investigates the sulfation dynamics of the reductive leaching process of black copper ores with the purpose of increasing the copper leaching, focusing on the influences of time and the addition of NaCl and FeSO<inf>4</inf> on sulfation behavior. Experiments were designed to replicate industrial conditions using oxidized minerals from the Codelco Salvador hydrometallurgy plant. Multivariate nonlinear regression models and response surface methodology were employed to analyze sulfation behavior. The findings demonstrate that analytical acid consumption (AAC) exerts a consistently positive and statistically significant effect on sulfation across the sampled domain, while NaCl and FeSO₄ also influence the process. However, variations in their levels showed limited impact. Time was significant only within the 24–48 h range. The optimized model predicted maximum sulfation at 60 h with 60% AAC, 90 g NaCl, and 42 g FeSO₄, with strong alignment between the observed and predicted values. These insights emphasize the importance of pretreatment methods, including sulfuric acid curing and NaCl addition, in improving leaching efficiency. © 2025 Elsevier B.V., All rights reserved.openAccessBLACK COPPERREFRACTORY ORERESPONSE SURFACE METHODOLOGYSTATISTICAL MODELINGCOPPER REFININGCOPPER SMELTINGHYDROMETALLURGYORE SAMPLINGREGRESSION ANALYSISACID CONSUMPTIONBLACK COPPER OXIDESCOPPER OREPRETREATMENT EFFECTSREFRACTORY ORESRESPONSE-SURFACE METHODOLOGYSTATISTIC MODELINGSULFATION BEHAVIORSSULPHATIONCOPPER DEPOSITSArtículo https://doi.org/10.3390/app15031375