Journal of Central South University | 2019
Lithological classification of cement quarry using discriminant algorithms
Abstract
As such in any industrial raw material site characterization study, making a lithological evaluation for cement raw materials includes a description of physical characteristics as well as grain size and chemical composition. For providing the cement components in accordance with the specifications required, making the classification of the cement raw material pit is needed. To make this identification in a spatial system at a quarry stage, the supervised pattern recognition analysis has been performed. By using four discriminant analysis algorithms, lithological classifications at three levels, which are with limestone, marly-limestone (calcareous marl) and marl, have been made based on the main chemical components such as calcium oxide (CaO), alumina (Al2O3), silica (SiO2), and iron (Fe2O3). The results show that discriminant algorithms can be used as strong classifiers in cement quarry identification. It has also recorded that the conditional and mixed classifiers perform better than the conventional discriminant algorithms.摘要在任何工业原料场地特征的研究中,对水泥原材料进行岩性评价的研究均包括其物理特征、粒 度和化学组成的相关描述。为了按照要求规格提供水泥组分,需要对水泥原料坑进行分类。采用监督 模式识别分析法在采石场阶段的空间系统中进行识别。通过使用四种识别分析算法,基于主要化学成 分如氧化钙(CaO)、氧化铝(A12O3)、二氧化硅(SiO2)和铁(Fe2O3),在石灰石,泥灰质石灰岩(钙 质泥灰岩)和泥灰岩三个层面进行岩性分类。结果表明:识别算法可以作为水泥采石场识别中的强分 类器,而且该条件和混合分类器比传统的识别算法效果更好。