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The Occurrence Law of Residual Leaching Reagent and Rare Earth by In-Situ Leaching Based on Data Analysis

机译:基于数据分析的原位浸出剩余浸出剂和稀土的赋存规律

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Rare earth is an important strategic resource like the oil resource, and the national and local government pay more and more attention on it. The increasing research on rare earth tailings by in-situ leaching, and improving the utilization of rare earth resources, reducing the impact of tailings on the environment, is very significant. Because the experiment data is messy, so that it is difficult to directly draw the remnants law of rare earth elements, ammonium nitrogen and nitrate. In this paper, we firstly propose an improved k-meaning: optimizing the initial center points through computing the density of data objects. A new evaluation function is proposed, namely equalization function, which enable the cluster number to be generated automatically. Then we utilize the proposed k-means technology to pre-process the experiment data. We split the data to some classes according to the different concentration of leaching solution. And then, the nitric concentration and residual tailings leaching reagent of rare earth are classed based on k-means technique. Experimental study found that, when the concentration of ammonium sulfate is 2.0%, leaching flowing is 1.0ml/min and leaching solution is 400ml per kilogram of tailings, the leaching rate of the rare earth and the concentration of rare earth in the leaching solution were both higher, which will have good economic benefits.
机译:稀土是重要的战略资源,就像石油资源一样,国家和地方政府对此也越来越重视。通过原地浸出对稀土尾矿的研究不断增加,提高稀土资源的利用效率,减少尾矿对环境的影响,具有十分重要的意义。由于实验数据比较混乱,因此很难直接得出稀土元素铵态氮和硝酸盐的残留定律。在本文中,我们首先提出一种改进的k含义:通过计算数据对象的密度来优化初始中心点。提出了一种新的评估函数,即均衡函数,该函数可使簇号自动生成。然后,我们利用提出的k-means技术对实验数据进行预处理。我们根据浸出溶液的不同浓度将数据分为几类。然后,基于k均值技术对稀土的硝酸浓度和残留尾矿浸出剂进行了分类。实验研究发现,当硫酸铵的浓度为2.0%时,浸出流速为1.0ml / min,浸出液为400ml / kg尾矿时,稀土的浸出率和浸出液中的稀土浓度为两者都较高,将具有良好的经济效益。

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