Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Department: search.filters.department.Abdullah Gül UniversitySubject: search.filters.subject.Separation

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  • Article
    Recent Progress in the Beneficiation of Iron-Manganese Ores: An Overview
    (Pleiades Publishing Ltd, 2025) Top, Soner
    Iron-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
    Citation - WoS: 2
    Citation - Scopus: 2
    A 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, Srecko
    This 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: 51
    Citation - Scopus: 56
    Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation
    (Amer Chemical Soc, 2024-12-16) Dangayach, Raghav; Jeong, Nohyeong; Demirel, Elif; Uzal, Nigmet; Fung, Victor; Chen, Yongsheng
    Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are inadequate to meet the growing demands for high-performance membranes. Machine learning (ML) has demonstrated huge potential to accelerate design and discovery of membrane materials. In this review, we cover strengths and weaknesses of the traditional methods, followed by a discussion on the emergence of ML for developing advanced polymeric membranes. We describe methodologies for data collection, data preparation, the commonly used ML models, and the explainable artificial intelligence (XAI) tools implemented in membrane research. Furthermore, we explain the experimental and computational validation steps to verify the results provided by these ML models. Subsequently, we showcase successful case studies of polymeric membranes and emphasize inverse design methodology within a ML-driven structured framework. Finally, we conclude by highlighting the recent progress, challenges, and future research directions to advance ML research for next generation polymeric membranes. With this review, we aim to provide a comprehensive guideline to researchers, scientists, and engineers assisting in the implementation of ML to membrane research and to accelerate the membrane design and material discovery process.