Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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 - Scopus: 17Hydrometallurgical Processing of Nickel Laterites- A Brief Overview on the Use of Solvent Extraction and Nickel/Cobalt Project for the Separation and Purification of Nickel and Cobalt;(Chamber of Mining Engineers of Turkey, 2019-06-01) Kursunoglu, Sait; Kaya, MuammerIn this study, a brief overview of the solvent extraction (SX) application for the separation and purification of nickel and cobalt in hydrometallurgical processing of laterite nickel ores is presented. This paper consists of two parts: firstly, the SX of nickel and cobalt from sulphate leach solutions is described; and secondly, the development of hydrometallurgical plants for the processing of laterite nickel ores that involve solvent extraction technique is discussed. The most important extractants are shortly given in the first part of the study. It is seen that the extraction and separation of nickel and cobalt from the impure leach solution of laterite nickel ores can be performed by either using a single extractant system that is called as a direct solvent extraction (DSX) or a mixture of two or more extractant which is known as a synergistic solvent extraction system (SSX). Mixed sulphide precipitation (MSP) and mixed hydroxide precipitation (MHP) processes are mentioned. This paper is also addressed advantage and disadvantages of each extraction system. It is shown that capital investment expenditure, operational investment expenditure and mineralogical content are the most important factors that can affect the selection of the appropriate hydrometallurgical process for lateritic nickel ore. © 2023 Elsevier B.V., All rights reserved.