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- MakaleYalnız künye2026Scopus SJRQ1Comparison of Zn recovery prediction from carbonate ores with machine-learning methods
Erkan, Ilker; Günen, Mehmet Akif
This study addresses the challenge of predicting zinc (Zn) recovery from carbonate ores via sodium hydroxide (NaOH) leaching. This complex process influenced by variable ore composition, surface passivation effects, and nonlinear reaction dynamics, which complicate reagent optimization and process control in hydrometallurgical operations. To tackle this, a dataset containing 422 experimental observations was compiled from previous studies, incorporating ore composition and process parameters, such as NaOH concentration, leaching time, temperature, and solid-to-liquid ratio. Four regression models (decision tree, neural network, generalized additive model, and random forest) were trained and evaluated using performance metrics, such as coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetrical mean absolute percentage error (SMAPE). Among these, the random forest model achieved the best predictive accuracy, with R2 value of 0.8541 on the test set and the lowest error rates, demonstrating its effectiveness in capturing the complex relationships between input variables and Zn recovery. Explainable artificial intelligence, particularly SHapley additive exPlanations (SHAP) analysis, revealed that NaOH concentration, leaching time, and solid-to-liquid ratio had the most positive influence on Zn recovery, whereas elements such as Ca, Fe, and Pb had inhibitory effects. These findings align with known geochemical behavior and provide valuable insights for reagent optimization and process efficiency in leaching processes. This study demonstrates the practical potential of machine learning in mineral processing, offering a scalable framework for optimizing Zn recovery from non-sulfide ores and a data-driven approach to enhance decision-making in hydrometallurgical applications.
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İncele - MakaleAçık Erişim2021Scopus SJRQ3Effect of heating on structure and leaching characteristics of a zinc carbonate ore
Kumas, Cavit; Obut, Abdullah
In this work, the structural changes occurred in a zinc carbonate (smithsonite) ore sample following heating at temperatures between 523 K and 1173 K were investigated in detail using X-ray diffraction (XRD), thermal (TG/DTA) and Fourier-transform infrared (FT-IR) spectroscopy analyses. Afterwards, the leaching characteristics of zinc from the ore sample and the heated ore samples in sodium hydroxide solutions were determined. While heating at 523 K did not cause any structural change in the ore sample, heating at 723 K completely converted smithsonite (ZnCO3) in the ore sample to zinc oxide (ZnO), which resulted lower zinc leaching efficiencies of 40.6% and 62.0% for 3 and 4 mol/dm3 NaOH concentrations, respectively, in comparison to zinc leaching efficiencies (67.2% and 70.7%) obtained for the unheated ore sample. On the other hand, due to neoformation of dissolution resistant ZnFe2O4, Ca2ZnSi2O7 and ZmSiO4 phases during heating and formation of CaZm(OHD2H2O phase during leaching, the leaching efficiency of zinc further decreased to 22.2% and 31.3%, respectively, in 3 and 4 mol/dm3 NaOH solutions for the ore sample heated at 1173 K. The formation of zinc-containing dissolution resistant phases by high-temperature heating was observed to be the only reason for the reduction in the zinc leaching efficiency (49.4% at 1173 K) at the highest NaOH concentration (8 mol/dm3) studied. In this work, the comparative precipitation studies were also conducted and crystalline zinc oxides with different morphologies could be precipitated at considerably high efficiencies from the selected pregnant solutions obtained following leaching.
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