数值分类技术在铁矿浮选中确定适宜pH值和粒度分布的应用

R. Khosravi, F. Dehghani, H. Siavoshi, A. Pazoki, R. Jahanian, T. Ghosh
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引用次数: 3

摘要

数值分类学技术是评价排序的多变量决策技术之一。它被广泛用于规划和发展研究。本研究对纳尔格斯铁矿铁矿在不同pH值和粒度范围下进行了浮选试验。数值分类技术是一种重要的多属性决策技术,用于确定浮选机输入物料的最佳粒度范围和矿浆的pH值。为此,在六种不同的pH值下选择了两种不同的粒径范围作为选项。确定了评价粒径范围和ph值的标准。随后,通过进行各种试验确定了有效的标准。最后,采用数值分类法确定测试的排序。根据Fi值(表示选择物的适当状态或发展速度的参数),提出粒径<74µm、pH = 9为Band Narges铁矿石浮选的最佳条件。
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The Application of Numerical Taxonomy Technique in the Iron Ore Flotation to Determine Appropriate pH and Particle Size Distribution
The numerical taxonomic technique is one of the multivariate decision-making techniques for assessment and ranking. It is widely used for planning and development studies. In this research, flotation experiments were conducted for Band Narges mine iron ore using different pH and particle size ranges. Numerical taxonomy technique, as one of the most important multi-attribute decision-making techniques, was used to determine the best range of particle sizes for the input feed of the flotation cell as well as the pH of the pulp. For this purpose, two different particle size ranges were selected for six different pH values as options. Criteria for evaluating particle size ranges and pHs were determined. Subsequently, effective criteria were determined by performing various tests. Finally, the ranking of the tests was determined using numerical taxonomy. Based on the Fi value (a parameter indicating the appropriate status or developmental rate of the option), the particle sizes <74 µm and pH = 9 were proposed as the optimum conditions to float Band Narges Iron Ore.
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