选矿过程中矿石品位分析光学方法的优化

V. Morozov
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引用次数: 0

摘要

流内矿石品位分析是有效控制选矿过程的新趋势。矿石品位的在线估计可以通过直接在河流中连续测量矿物成分或利用河流取样并进行以下分析来实现。使用现代格式的彩色图像识别,可以对矿石中的矿物进行可靠的分离测定。矿物在可见光下的光谱特征是基于光谱估计矿石矿物成分的信息来源(图1)。确定矿石品位的任务是确定其与矿石主要工艺类型[1]的相似性。采用关联度份额计算的多准则方法进行矿石品位计算。系统数学模型通过六个(或更多)重要参数(矿石中的矿物含量)来计算入矿的隶属关系。在Erdenet加工厂(蒙古),对一种基于矿物成分光学分析仪的先进矿石诊断新设备进行了测试。基于光谱的分析系统安装在输送机(图2)上方,用于将矿石送入磨矿操作[2]。矿石在输送带上连续扫描。然后,利用该算法对矿石品位进行识别。为了精确分析,我们开发了专用的平板设备。测量技术包括准备矿石样品,以样品的平坦部分的形式形成测量区域,照明和可见光光谱图像的捕获(图3)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of the Optical Methods of Ore Grade Analysis at Mineral Processing
In-stream ore grade analysis is a new trend in effective control of ore benefication processes. On-line estimation of ore grade can be implemented based on continuous measurement of the mineralogical composition directly in-stream or using instream sampling with the following analysis. Reliable separate determination of minerals in ore is possible using modern formats of colour image recognition. The spectral characteristics of minerals in visible light are the source of information for optical spectrum-based estimation of ore mineralogical composition (Figure 1). The task of determining the grade of ore, entering processing, is to determine its similarity to the main technological types of ores [1]. The calculation of ore grade was carried out using a multi-criteria method for affiliation shares calculations. The system mathematical model provides for calculation of the incoming ore affiliation by six (or more) significant parameters (minerals contents in the ore). At the Erdenet processing plant (Mongolia), a new facility for advanced ore diagnostics, based on optical analyzer of mineral composition, was tested. The optical spectrum-based analysis system was installed above the conveyor (Figure 2) for feeding ore to the grinding operation [2]. The ore scanning on the conveyor belt is carried out continuously. Then, using the algorithm, recognition of the ore grade is carried out. For exact analysis, the special flatbed facility was developed [3]. The measurement technique involves preparing the ore sample, forming the measurement area in the form of a flat portion of the sample, illumination and capture of the images in the visible spectrum (Figure 3).
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