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PARTICLE SHAPE ANALYSIS IN MINERAL LIBERATION.

机译:矿物解放中的颗粒形状分析。

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摘要

One of the main reasons for size reduction is to liberate minerals for recovery, but simple measurements of size reduction alone are not enough to determine the fraction liberated. Additional measurements such as specific gravity and linear intercept distributions are necessary. Since linear intercept distributions have been transformed into volumetric abundances with reasonable success, it was hypothesized that mineral perimeter distributions could also be transformed into volumetric abundances. The problem is to detect the perimeter of different minerals without expensive microprobes or image analyzers. The solution is to use Fourier descriptors of particle silhouettes.; A newly developed method for particle shape analysis, called elliptic shape decomposition, was tested against the most widely used one, namely, the R-(theta), and it was found that the former is better than any thing the R-(theta) can come up with. The elliptic shape method was further tested on gold particles from different geographic locations at Moose Range Drainage, Alaska. An average classification accuracy of 96 percent was obtained.; Computer-generated Type 1 random particles were used to generate hypothetical liberated particles. The results encouraged the construction of real particles. A micron eye digital camera attached to an IBM PC was used to digitize the fractionated mineral grade particles for each of the four performed ball mill grinding tests. Statistical analysis performed on liberated particles indicated that liberated grades within each grinding period are distinct. However, the classification accuracy was not as good as expected. The reason for this was attributed to the performance of the digital camera and to impurities of the sample.; A model which predicts particle grade distribution resulting from the constructed material is presented. This model is based on shape descriptors and was derived from the general population balance model. The model is limited to one descriptor and is based on some limiting assumptions. The agreement to experimental data was marginally successful. This study should be considered a preliminary one and more research is needed.
机译:减小尺寸的主要原因之一是释放矿物质以进行回收,但是仅靠减小尺寸的简单测量就不足以确定释放出的分数。必须进行其他测量,例如比重和线性截距分布。由于线性截距分布已经成功地转化为体积丰度,因此可以假设矿物周长分布也可以转化为体积丰度。问题是在没有昂贵的微探针或图像分析仪的情况下检测不同矿物的周长。解决方案是使用粒子轮廓的傅立叶描述符。针对最广泛使用的R-θ方法,测试了一种新开发的粒子形状分析方法,称为椭圆形状分解法,发现该方法优于任何一种R-θ方法。可以提出来。椭圆形方法在来自阿拉斯加Moose Range Drainage的不同地理位置的金颗粒上进行了进一步测试。平均分类精度为96%。计算机生成的1型随机粒子用于生成假设的自由粒子。结果鼓励了真实粒子的构造。连接到IBM PC的微米级眼睛数码相机用于对进行的四个球磨机研磨测试中的每一个数字化分级的矿物级颗粒。对析出的颗粒进行的统计分析表明,在每个研磨时间内的析出的等级是不同的。但是,分类精度不如预期。其原因归因于数码相机的性能以及样品中的杂质。提出了一个模型,该模型可预测由构造材料产生的颗粒等级分布。该模型基于形状描述符,并从总体人口平衡模型派生而来。该模型仅限于一个描述符,并且基于一些限制性假设。对实验数据的协议勉强成功。这项研究应被认为是初步的,还需要进行更多的研究。

著录项

  • 作者

    GHENAW, ALI AHMED.;

  • 作者单位

    University of Idaho.;

  • 授予单位 University of Idaho.;
  • 学科 Engineering Metallurgy.
  • 学位 Ph.D.
  • 年度 1986
  • 页码 183 p.
  • 总页数 183
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 冶金工业;
  • 关键词

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