WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 1Citation - Scopus: 1Predictive Modeling and SHAP-Based Interpretability of Manganese and Iron Dissolution in Multi-Acid Leaching Systems Using Hybrid Machine Learning(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Top, Soner; Altiner, Mahmut; Kursunoglu, Sait; Kuzu, EmrahHydrometallurgical leaching processes contain complex and nonlinear parameter interactions that are difficult to capture with conventional empirical models. In this study, a multiple hybrid machine learning approach was developed to predict manganese (Mn) and iron (Fe) dissolution efficiency in leaching systems and performed using sulfuric acid (H2SO4), hydrochloric acid (HCl), and nitric acid (HNO3). A large-format dataset consisting of 204 independent leaching experiments was generated in which acid type, acid concentration (0.5-5 M), temperature (25-90 degrees C), solid/liquid ratio (100-200 g/L), leaching time (1-4 h), and eight different reducing agent types were systematically varied. XGBoost, LightGBM, CatBoost, and Random Forest algorithms were individually trained and subsequently combined with a Soft Voting Ensemble architecture. Hyperparameters were optimized using the RandomizedSearchCV method with 3-fold cross-validation. The XGBoost model achieved the highest prediction accuracy for Mn dissolution (R-2 = 0.8993, RMSE = 8.06%), while CatBoost demonstrated the best performance in Fe dissolution (R-2 = 0.8415, RMSE = 4.43%). SHAP analysis suggested that the dosage and type of reducing agents are the most influential predictive features for Mn dissolution, while acid molarity and temperature were identified as the dominant predictors for Fe leaching. Friedman test confirmed that performance differences among both Mn and Fe models were statistically significant (Mn: chi(2) = 32.76, p < 0.001; Fe: chi(2) = 25.96, p < 0.001). The developed models contribute significantly to hydrometallurgical process optimization by predicting the nonlinear effects of leaching parameters on metal dissolution with high accuracy. This study presents a comprehensive and interpretable machine learning framework supported by an extensive experimental dataset, a substantial portion of which has not been previously utilized or comparatively analyzed within a unified multi-acid framework, enabling systematic modeling of selective Mn-Fe dissolution across multiple acid systems and reducing agents.Article Citation - WoS: 148Citation - Scopus: 167Critical Review on Secondary Zinc Resources and Their Recycling Technologies(Elsevier, 2020-08) Kaya, Muammer; Hussaini, Shokrullah; Kursunoglu, SaitIn a race to save the earth of its rapidly depleting natural resources, the use of Secondary Raw Materials (SMRs) as alternative replacements in several processes is currently intensively pursued. The valorization of SMRs is consistent with the sustainable circular economy, where resource efficiency is maximized for the benefit of both the economy and green environment. In line with this mandate, this article focuses on investigating recent studies on secondary zinc (Zn) resources and describing state-of-art Zn recycling technologies. Globally, some of the main Zn-containing secondary raw materials are mine/concentrator/smelter tailings, wastes, slags, scraps, dust, etc. Although the pyrometallurgical process has been dominant in the secondary metal recycling processes, there has been growing interest and pressure to achieve sustainable and greener recycling methods to remediate the environmental problems caused by emissions of toxic heavy metals and sulfur oxides in the traditional smelting process. In the last decades, many sustainable and environmentally friendly novel hydrometallurgical processes for Zn extraction were developed to overcome tougher legislation and meet cost competitiveness. Secondary Zn recycling focuses on the development of selective Zn-rich but Pb, Fe, As-lean recovery processes.