Yang Chenguang, Yin Jianqiang, Wu Liqin, Zeng Qiuyu, Zhang Liwei
School of Materials Science and Engineering, Anhui University of Science and Technology, 232001 Huainan, China.
ACS Omega. 2022 Dec 20;8(1):48-55. doi: 10.1021/acsomega.2c05743. eCollection 2023 Jan 10.
For coal and gangue, intelligent sorting processes for separation, the use of coal and gangue mineral components with different fundamental differences, and the study of different properties of minerals and coal with different scales and density regarding the gray value change law are presented. The results show that the gray value of single minerals and mixed minerals gradually decreases with the increase of their thickness and density. The greater the density of minerals, the smaller the gray value at the same thickness, and the same rule applies to different coal ranks. Via regression analysis methods, the values of the regression equation parameter of pure minerals for graphite, quartz, kaolinite, and montmorillonite are 59.25, 65.69, 61.61, and 58.02 in the high-energy region, respectively. In the low-energy region, they are 174.95, 177.31, 186.95, and 161.81. For the regression equation parameter of mixed minerals in the form of two mixed minerals (graphite and quartz, kaolinite, or montmorillonite) and three kinds of mineral mixing (graphite-kaolinite and quartz; graphite-montmorillonite and quartz; graphite-kaolinite and montmorillonite), the gray values are 151.12, 156.00, 153.13,152.43, 152.98, and 151.98 in the high-energy region, respectively; in the low-energy region, they are 193.34, 201.34, 192.93, 191.26, 194.68, and 193.08. The phenomenon for the gray range of two kinds of single minerals locates in the range of mixed minerals that was formed from a single mineral observed after the regression equation of mixed mineral was verified by a single mineral, which agrees with the X-ray recognition pattern. In the end, as the density of coking coal, fat coal, and gas coal increases, the gray value decreases, which was in agreement with single- and mixed-mineral analyses.
针对煤炭与矸石,介绍了用于分离的智能分选工艺、利用煤炭与矸石矿物成分的根本差异以及研究不同粒度和密度的矿物与煤炭在灰度值变化规律方面的不同特性。结果表明,单一矿物和混合矿物的灰度值随其厚度和密度的增加而逐渐降低。矿物密度越大,在相同厚度下灰度值越小,且该规律适用于不同煤级。通过回归分析方法,石墨、石英、高岭土和蒙脱石等纯矿物在高能区的回归方程参数值分别为59.25、65.69、61.61和58.02。在低能区,它们分别为174.95、177.31、186.95和161.81。对于以两种混合矿物(石墨与石英、高岭土或蒙脱石)和三种矿物混合(石墨 - 高岭土与石英;石墨 - 蒙脱石与石英;石墨 - 高岭土与蒙脱石)形式存在的混合矿物的回归方程参数,其在高能区的灰度值分别为151.12、156.00、153.13、152.43、152.98和151.98;在低能区,它们分别为193.34、201.34、192.93、191.26、194.68和193.08。两种单一矿物的灰度范围现象位于由单一矿物形成的混合矿物范围内,这在通过单一矿物验证混合矿物的回归方程后得到观察,与X射线识别模式相符。最后,随着焦煤、肥煤和气煤密度的增加,灰度值降低,这与单一矿物和混合矿物分析结果一致。