基于识别算法的水泥矿山岩性分析（英文）
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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(Al_2O_3)、二氧化硅(SiO_2)和铁(Fe_2O_3),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。
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(Al_2 O_3), silica(SiO_2), and iron(Fe_2 O_3).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.
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(Al_2 O_3), silica(SiO_2), and iron(Fe_2 O_3).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.
引文
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[2]KURDOWSKI W.Cement and concrete chemistry[M].New York:Springer,2014.
[3]ONUR A H,KONAK G,KARAKUS D.Limestone quarry quality optimization for a cement factory in Turkey[J].JSouth Afr Inst Min Metall,2008,108(12):753-757.
[4]TUTMEZ B.Analyzing non-stationarity in cement stone pit by median polish interpolation:A case study[J].Journal Applied Statistics,2013,41(2):454-466.
[5]JOSHI D,CHATTERJEE S,EQUEENUDDIN SkMd.Limestone quarry production planning for consistent supply of raw materials to cement plant:A case study from Indian cement industry with a captive quarry[J].Mineral Mining Technology,2015,51(5):980-992.
[6]BRAMER M.Principles of data mining[M].London:Springer-Verlag,2016.
[7]ASAD M W A.Implementing a blending optimization model for short-range production planning of cement quarry operation[J].J Min Sci,2010,46(5):525-535.
[8]KARAKUS D.Raw material homogenization production plan in multiple quarries-slope stability assessment:Cement raw material clay pit sample[J].Journal of Mining Science,2012,48(1):154-166.
[9]AGHA M,FERRELL R E,HART G F.Mineralogy of Egyptian bentonitic clays I:Discriminant function analysis[J].Clays and Clay Minerals,2012,60(4):387-404.
[10]KUYUK H S,YILDIRIM E,DOGAN E.Clustering seismic activities using linear and nonlinear discriminant analysis[J].Journal of Earth Science,2014,25(1):140-145.
[11]GHANNADPOUR S S,HEZARKHANI A.Exploration geochemistry data-application for anomaly separation based on discriminant function analysis in the Parkam porphyry system(Iran)[J].Geosciences Journal,2016,20(6):837-850.
[12]ZHANG W,LI X B,GONG F G.Stability classification model of mine-lane surrounding rock based on distance discriminant analysis method[J].Journal of Central South University,2008,15:117-120.
[13]ADACHI K.Matrix-based introduction to multivariate data analysis[M].New York:Springer,2016.
[14]TSAKIRIDIS P E,SAMOUHOS M,PEPPAS A.Silicoaluminous bottom ash valorization in cement clinker production:Synthesis,characterization and hydration properties[J].Construction and Building Materials,2016,126:673-681.
[15]JAMES G,WITTEN G,HASTIE T,TIBSHIRANI R.An introduction to statistical learning[M].New York:Springer,2013.
[16]HASTIE T,TIBSHIRANI R,JAMES G,FRIEDMAN J.The elements of statistical learning[M].New York:Springer,2009.
[17]SRIVASTAVA S,GUPTA M R,FRIGYIK B A.Bayesian quadratic discriminant analysis[J].Journal of Machine Learning Research,2007,8:1277-1305.
[18]FRALEY C,RAFTERY A E.Model-based clustering,discriminant analysis,and density Estimation[J].Journal of the American Statistical Association,2002,97:611-631.
[19]HASTIE T,TIBSHIRANI R,BUJA A.Flexible discriminant analysis by optimal scoring[J],Journal of the American Statistical Association,1994,89(428):1255-1270.
[20]WEHRENS R.Chemometrics with R[M].Heidelberg:Springer,2011.
[21]TUTMEZ B,DAG A.Regression-based algorithms for exploring the relationships in a cement raw material quarry[J].Computers and Concrete,2012,10(5):457-467.