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Statistical optimization study of jigging process on beneficiation of fine size high ash Indian non-coking coal

机译:细粒高灰印度非焦煤选矿跳汰工艺的统计优化研究

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Non-coking coal is used for metallurgical and cement industries apart from generating energy. Indian non-coking coals are high in ash content because of drift origin. These high ash coals require beneficiation before being used. Jigging is one of the unit operations used for beneficiation of coal. Beneficiation by jigging is carried out for the coarser size of particles. Jigging of fine size coal is limited. However, in present study two fine non-coking coal samples having sizes -3+2 mm and -2+1 mm were used for jigging study. In present study jigging experiments were performed using laboratory Denver mineral jig by varying feed size, water rate, and feed rate. 2(3) full factorial experimental design was used to study the performance of jigging operation. The performance of jigging was judged by statistical analysis; where ash content and combustible recovery of concentrate were considered to be responses. In addition to statistical analysis, optimization study was also carried out by Nelder-Mead multidimensional pattern search method. Statistical models developed in the present study could predict ash content and yield of the concentrate accurately. At optimized condition, it was possible to achieve 22.6% ash content with 64.15% combustible recovery. (C) 2016 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.
机译:除生产能源外,非焦煤还用于冶金和水泥行业。由于漂移原因,印度的非焦煤灰分含量很高。这些高灰分煤在使用前需要进行选矿。跳汰是用于选煤的单位操作之一。对于较粗的颗粒,通过夹具进行选矿。细煤的跳动是有限的。但是,在本研究中,两个尺寸为-3 + 2 mm和-2 + 1 mm的细粉非焦煤样品用于跳汰研究。在本研究中,使用实验室丹佛矿物夹具通过改变饲料大小,进水速度和进料速度进行了跳汰实验。 2(3)全因子实验设计用于研究跳汰作业的性能。通过统计分析判断跳汰的性能。灰分和浓缩物的可燃回收被认为是反应。除统计分析外,还通过Nelder-Mead多维模式搜索方法进行了优化研究。本研究开发的统计模型可以准确预测精矿的灰分含量和产量。在最佳条件下,可达到22.6%的灰分,可燃回收率为64.15%。 (C)2016日本粉末技术学会。由Elsevier B.V.和日本粉末技术学会出版。版权所有。

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