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 - WoS: 3Citation - Scopus: 4Rare Earth Elements in the Global Economy: Usage, Recovery, and the Quest for Supply Security – A Review(Springer Heidelberg, 2026) Top, Soner; Ayten, Asim Mustafa; Altiner, Mahmut; Demir, Idris; Kursunoglu, SaitOften described as the vitamins of modern industry, rare earth elements (REEs) are indispensable for the deployment of low-carbon and clean energy technologies. However, ensuring a secure and sustainable REE supply remains a major challenge due to the strong interdependence between application-driven demand, extraction and processing technologies, and the geopolitical concentration of resources. This review adopts an integrated analytical framework in which these three dimensions are treated as interconnected components shaping the resilience of global REE supply chains. First, the major application sectors of REEs are examined to clarify how emerging energy and advanced manufacturing technologies drive demand for specific elements and amplify their strategic importance. Second, extraction and processing technologies are reviewed in relation to both primary and secondary resources, highlighting how technological maturity, process selection, and material characteristics constrain or enable supply expansion. Finally, geopolitical and strategic aspects of the REE supply chain are analyzed to demonstrate how resource concentration, policy instruments, and international dependencies directly influence technological deployment and industrial competitiveness. By explicitly linking application-driven demand, technological pathways for extraction and processing, and geopolitical supply structures within a unified framework, this review provides a coherent understanding of the systemic challenges facing the REE sector and identifies key leverage points for improving the robustness and sustainability of REE supply chains in the context of the global clean energy transition.Article Citation - WoS: 2Citation - Scopus: 2Recent Progress in the Beneficiation of Iron-Manganese Ores: An Overview(Pleiades Publishing Ltd, 2025) Top, SonerIron-manganese (Fe-Mn) ores are essential for steelmaking, ferroalloy production, and emerging energy technologies, yet their beneficiation is challenging due to the close association of Fe and Mn oxides and their overlapping physicochemical properties. This review assesses key processing strategies, including gravity separation, magnetic methods, flotation, reduction roasting, and selective reductive leaching. Physical beneficiation offers limited upgrades, being constrained by mineral liberation and ore texture. Reduction roasting with carbonaceous or hydrogen reductants exploits the different reduction stabilities of Fe and Mn oxides, creating magnetic contrasts for effective separation. Hydrometallurgical techniques based on reductive leaching also show strong potential, particularly with biomass-derived or organic reductants, achieving manganese recoveries often above 90-99%. A central focus is the use of Ellingham and Eh-pH diagrams as predictive tools for selective separation. Ellingham diagrams outline the thermodynamic stabilities of Fe and Mn oxides, guiding roasting design, while Eh-pH diagrams describe dissolution behavior under varying acidity and redox conditions, enabling leaching optimization. Integrating these frameworks with experimental evidence demonstrates how thermodynamic and electrochemical principles can improve process selectivity. No single technique universally addresses Fe-Mn beneficiation challenges; instead, hybrid flowsheets combining physical, thermal, and hydrometallurgical routes tailored to ore characteristics are most effective. Future research should prioritize low-carbon and sustainable approaches such as hydrogen roasting, bio-reductant leaching, and zero-waste systems. This review thus provides both a synthesis of current advances and a roadmap for sustainable Fe and Mn resource recovery.Article Production of Waste-Based Lightweight Geopolymer Concretes Using Calcined Bentonite, Fly Ash, Slag, and Expanded Polystyrene Granules(Taylor & Francis Inc, 2026-03-19) Top, Soner; Nussrat Shukur Shukur, Yasir; Vapur, HüseyinThis study investigates the production of environmentally friendly lightweight geopolymer concretes utilizing fly ash (FA) as the primary precursor with calcined calcium bentonite (CCB), ferrochrome slag (SG), and expanded polystyrene (EPS) as supplementary components. A Box-Behnken design was employed to investigate the combined effects of CCB and SG additions, along with the solid-to-liquid ratio, on the compressive strength. Moderate CCB incorporation, particularly around 10%, improved mechanical performance, achieving strengths above 48 MPa, with a maximum of 51.33 MPa at 90 degrees C for a mix containing 5% CCB and 5% SG. Higher CCB dosages (>20%) reduced strength due to matrix dilution, while SG showed limited contribution at elevated levels. Incorporation of EPS granules reduced density to as low as 1292 kg/m & sup3;, yet compressive strengths between 25 and 30 MPa were maintained in mixes with 10% CCB and 0.3% EPS. SEM-EDX analysis confirmed dense geopolymer matrices in FA-CCB composites, whereas SG particles appeared less integrated. These results confirm the potential for producing high-strength, lightweight geopolymer concretes through the effective valorization of waste. The combined use of FA, CCB, SG, and EPS offers a sustainable pathway for resource-efficient construction that supports circular resource utilization.Article Citation - WoS: 3Citation - Scopus: 3A Novel Biomass-Derived Reductant for Nitric Acid Dissolution of Manganiferous Iron Ore: Comparative Assessment of Organic Reductants(MDPI, 2025-12-31) Top, Soner; Altiner, Mahmut; Vapur, Huseyin; Kursunoglu, Sait; Stopic, SreckoThis study investigates the selective dissolution of manganese from a manganiferous iron ore using nitric acid (HNO3) in the presence of various organic reductants. A series of leaching experiments was performed to evaluate the effects of temperature, reductant type, and leaching time on Mn recovery, with particular emphasis on biomass (horse dung) and tartaric acid as novel reducing agents. The dissolution behaviour of Fe, Mn, Mg, Ca, and Al was systematically examined, revealing that Mn extraction was strongly enhanced in the presence of reductants, while Fe dissolution remained below 10% under all conditions. The maximum Mn dissolution exceeded 90% at 90 degrees C using biomass and reached nearly 85%-90% with tartaric acid at elevated temperatures. Kinetic studies were conducted by applying reaction order models and the shrinking core model. The results indicated that Mn dissolution in HNO3 medium is predominantly controlled by surface chemical reaction, with Arrhenius analysis yielding activation energies of 27.74 kJ/mol for biomass and 21.26 kJ/mol for tartaric acid. These relatively low values confirm the efficiency of organic reductants in facilitating Mn reduction and dissolution. To sum up, comparison of reductant efficiency revealed that, at the lowest concentrations, the dissolution of Mn followed the sequence glucose > sucrose > oxalic acid > tartaric acid > maleic acid > biomass > citric acid > acetic acid. At the highest concentrations, the trend shifted, with citric acid emerging as the most effective, followed by tartaric acid > oxalic acid > glucose > sucrose > maleic acid > biomass > acetic acid.Article Citation - WoS: 3Citation - Scopus: 2A Comprehensive Review on the Extraction and Recovery of Lithium from Primary and Secondary Sources: Advances Toward Battery-Grade Materials(Wiley, 2025-10-20) Top, Soner; Kursunoglu, Sait; Altiner, MahmutLithium-ion battery (LIB) technologies have become indispensable to modern energy systems, driving global demand for high-purity lithium compounds. This review focuses on lithium recovery and purification strategies for battery-grade lithium carbonate (Li2CO3) and lithium hydroxide (LiOH), addressing both primary sources (brines and minerals) and secondary sources (waste materials). Industrially established processes, such as evaporation-based brine treatment and conventional metallurgical methods, are discussed alongside emerging techniques, including membrane separation, solvent extraction, and CO2-assisted precipitation. Particular attention is given to lithium precipitation mechanisms, the behaviour of co-existing ions during extraction, and the specific quality requirements for cathode material synthesis. By evaluating process scalability, environmental impact, and product purity, this review provides a comprehensive understanding of current practices and future directions. Additionally, it highlights the growing importance of lithium in the context of accelerating electric vehicle (EV) adoption, underscoring the bright and expanding future of the lithium industry.Article Citation - WoS: 7Citation - Scopus: 5Beneficiation of Low-Grade Iron Ore Using a Dry-Roll Magnetic Separator and Its Modeling via Artificial Neural Network(Springer, 2025-02-24) Fariss, Abdourahman Hassan Brahim; Ibrahim, Ahmedaljaali Ibrahim Idrees; Ozdemir, Ali Can; Top, Soner; Kursunoglu, Sait; Altiner, MahmutThe beneficiation of low-grade iron ore (39.5% Fe-(T) grade) using a dry-roll magnetic separator was investigated. The ore was characterized using Mineral Liberation Analysis (MLA). It was determined that the ore was composed of iron oxide (goethite and hematite), quartz, chlorite, muscovite, plagioclase, and other minerals. The effect of particle size (PS, - 1 + 0.500 mm, - 0.500 + 0.300 mm, and - 0.300 + 0.125 mm), splitter position (SP, 43 degrees and 58 degrees), cleaning stage (CS, 1 and 2), conveyor speed (CoS, 3, 5, and 7 Hz), magnetic field strength (MFS, 0.2 T and 0.4 T) on the recovery of the magnetic product was investigated. Experimental results show that the product (- 1 + 0.500 mm) with the Fe-(T) grade of 67.67% can be obtained, but its recovery was not at an acceptable value (< 30%). Furthermore, the Fe-(T) grade of the product (- 0.500 + 0.300 and - 0.300 + 0.125 mm) could not reach satisfactory levels<bold>.</bold> The artificial neural network (ANN) method was conducted on the results of experimental studies. Three different training algorithms were employed for modeling, and their performance was assessed using statistical evaluation criteria. The results demonstrate that Bayesian Regularization (BR) algorithm exhibited better performance compared to others in predicting both Fe(T) grade and recovery rate during the testing phase. These findings support the notion that ANN algorithms can be a powerful modeling and prediction tool in the field of mineral processing.