Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 7Citation - Scopus: 4Separation of Fe and Mn From Manganiferous Iron Ores via Reductive Acid Leaching Followed by Magnetic Separation(Springer, 2019-08-01) Top, S.In this study, a process to separate manganese and iron from manganiferous iron ores by reductive acid leaching followed by magnetic separation was conceived and experimentally tested. In the leaching process, sulfuric acid was used as lixiviant and oxalic acid was used as reductant. The experimental results showed that the manganese and iron separation was optimum when the concentration of the sulfuric acid and oxalic acid were 0.75 M and 30 g/L, respectively, at a temperature of 80 °C, a solid/liquid ratio of 67 g/L, stirring speed of 400 rpm, and leaching duration of 60 min. Under this condition, 90.49% and 6.78% of Mn and Fe were dissolved, respectively, from the ore sample with a size fraction of − 106 μm. It was determined that the leaching of manganese from the ores was a second-order reaction with an activation energy (E<inf>a</inf>) of 53.38 kJ/mol. The leaching residues obtained under the optimum condition were subjected to high-intensity wet magnetic separation tests to recover the remaining iron content. This separation process produced a concentrate containing 56.20% Fe and 1.79% Mn with iron and manganese recoveries of 56.83% and 66.73%, respectively. A magnetic separation test from an unleached ore sample was also carried out as a benchmark. To the best of our knowledge, this is the first time that a magnetic separation process was used to a residue obtained from reductive acid leaching of manganiferous iron ores to recover iron. © 2019, Society for Mining, Metallurgy & Exploration Inc.Article Citation - WoS: 51Citation - Scopus: 56Machine 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, YongshengPolymeric 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.