Variable importance assessments of an innovative industrial-scale magnetic separator for processing of iron ore tailings

A. Tohry, M. Jafari, Mehrdad Farahani, M. Manthouri, S. C. Chelgani
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引用次数: 11

Abstract

ABSTRACT Reprocessing of iron ore tailings (IOTs) and extracting recoverable valuable iron oxides will become increasingly financially attractive for mining companies and also may reduce environmental problems. Using databases built based on long term monitoring of units installed on plants to control the operational conditions to generate artificial intelligence models can decrease the cost of reprocessing operations Although some investigations have been focused on the reprocessing of IOTs, several challenges still remain which need to be addressed, especially for fine particles. SLon®, has developed a pulsating high gradient magnetic separator for the processing of fine iron oxides. However, there has been no systematic optimisation and variable assessments for SLon® operating variables to examine their effects on metallurgical responses (separation efficiency) on the industrial scale. This study addressed these drawbacks by linear (Pearson correlation) and non-linear (random forest) variable importance measurements (VIM) through an industrial SLon® installation.
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一种新型工业规模磁选机处理铁矿尾矿的可变重要性评价
对矿业公司来说,铁矿尾矿的后处理和可回收的有价氧化铁的提取在经济上越来越具有吸引力,同时也可以减少环境问题。使用基于对安装在工厂上的设备的长期监测而建立的数据库来控制操作条件以生成人工智能模型,可以降低后处理操作的成本。尽管一些研究已经集中在物联网的后处理上,但仍然存在一些需要解决的挑战,特别是对于细颗粒。SLon®开发了一种脉动高梯度磁选机,用于处理细氧化铁。然而,对于SLon®操作变量,没有系统的优化和变量评估来检查它们对工业规模上的冶金响应(分离效率)的影响。本研究通过工业SLon®安装,通过线性(Pearson相关)和非线性(随机森林)变量重要性测量(VIM)解决了这些缺点。
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CiteScore
3.50
自引率
0.00%
发文量
6
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