Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Manganzı Demir Cevherinden Manganın Çözündürülmesinde Farklı İndirgeme Maddelerinin Etkisi(Chamber of Mining Engineers of Turkey, 2022-06-26) Top, S.; Altiner, Mahmut; Kursunoglu, SaitIIn this paper, the manganese extraction from a manganiferous iron ore was investigated using reductive leaching. Various chemicals were used as a reducing agent to leach manganese selectively from the ore in the presence of sulfuric acid (H2SO4) solution. Firstly, optimum dissolution values were determined for selective manganese dissolution without using a reducing agent. As it was aimed at the selective extraction of manganese from the ore, the reductive leaching tests were conducted by adding the reducing agents under the following optimal parameters: a leaching time of 1 h, a stirring speed of 300 rpm, a temperature of 70°C, a sulfuric acid concentration of 1 M where the ore was leached with an extraction ratio of 11.54% Mn and 2.16% Fe. Manganese was dissolved with high efficiencies (up to 97.46%) from the ore by using different organic compounds (tartaric acid (C<inf>4</inf>H<inf>6</inf>O<inf>6</inf>), oxalic acid (C<inf>2</inf>H<inf>2</inf>O<inf>4</inf>), citric acid (C<inf>6</inf>H<inf>8</inf>O<inf>7</inf>), glucose (C<inf>6</inf>H<inf>12</inf>O<inf>6</inf>), sucrose (C<inf>12</inf>H<inf>22</inf>O<inf>11</inf>), and maleic acid (C<inf>4</inf>H<inf>4</inf>O<inf>4</inf>)) as the reducing agents. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 3Protein İkincil Yapı Tahmini Için Makine Öǧrenmesi Yöntemlerinin Karşılaştırılması(Institute of Electrical and Electronics Engineers Inc., 2018-05) Aydin, Zafer; Kaynar, Oǧuz; Görmez, Yasin; Işik, Yunus EmreThree-dimensional structure prediction is one of the important problems in bioinformatics and theoretical chemistry. One of the most important steps in the three-dimensional structure prediction is the estimation of secondary structure. Due to rapidly growing databases and recent feature extraction methods datasets used for predicting secondary structure can potentially contain a large number of samples and dimensions. For this reason, it is important to use algorithms that are fast and accurate. In this study, various classification algorithms have been optimized for the second phase of a two-stage classifier on EVAset benchmark both in the original input space and in the space reduced using the information gain metric. The most accurate classifier is obtained as the support vector machine while the extreme learning machine is significantly faster in model training. © 2018 Elsevier B.V., All rights reserved.