CN112229863A - 一种铁矿石的原产国或品牌的鉴别方法 - Google Patents
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Abstract
本发明公开了一种铁矿石的原产国或品牌的鉴别方法。该铁矿石的原产国的鉴别方法,其步骤包括:S1.取至少3个原产国,每个原产国至少12个批次的铁矿石中元素含量的数据,依次进行缺失值处理和多变量异常数据检验后,建立Fisher判别模型;S2.将待测样品铁矿石的元素含量代到步骤S1的Fisher判别模型中,确定待测样品铁矿石的原产国;其中,所述步骤S1和步骤S2的元素含量单位均为质量百分含量。本发明的鉴别方法系统、准确、可靠,且随着样品的增多,能够建立用于铁矿石原产国或品牌的数据库。
Description
技术领域
本发明涉及一种铁矿石的原产国或品牌的鉴别方法。
背景技术
铁矿石是钢铁工业的重要原材料,不同原产国来源的铁矿石由于地质成因差异,主次元素含量存在一定区域特征。澳大利亚、南非、巴西、哈萨克斯坦、印度等国作为全球铁矿石最主要的出口国,主要铁矿产区相对集中。由于地质成因相似,与原产国的鉴别相比,同一原产国不同品牌铁矿石的鉴别将更有难度。现有技术中,没有相关技术能够系统、准确、可靠地进行进口铁矿石原产国和品牌的鉴别,也没有公开用于铁矿石鉴别相关数据库的建立方法。
发明内容
本发明为了解决现有技术中暂未有一种系统、准确、可靠的铁矿石原产国和品牌的鉴别方法,且未有建立一种能够用于鉴别铁矿石原产国或品牌的数据库,而提供了一种铁矿石的原产国或品牌的鉴别方法。本发明的鉴别方法系统、准确、可靠,且随着样品的增多,能够建立用于铁矿石原产国或品牌的数据库。
本发明通过以下技术方案解决上述技术问题。
本发明提供了一种铁矿石的原产国的鉴别方法,其步骤包括:
S1.取至少3个原产国,每个原产国至少12个批次的铁矿石中元素含量的数据,依次进行缺失值处理和多变量异常数据检验后,建立Fisher判别模型;
S2.将待测样品铁矿石的元素含量代到步骤S1的Fisher判别模型中,确定待测样品铁矿石的原产国;
其中,所述步骤S1和步骤S2的元素含量单位均为质量百分含量。
本发明中,本领域技术人员知晓,用于建立模型的原产国和批次的数据量为越多越好,因此对于原产国和批次的数据量上限不作特别限定,较佳地,原产国数为3~5个,批次数为12~298。
本发明中,所述元素含量的检测方法为本领域常规元素含量的检测方法,较佳地为波长色散X射线荧光光谱无标样分析方法,或者为元素含量定量分析方法,所述元素含量定量分析方法较佳地为波长色散X射线荧光光谱定量分析方法、三氯化钛还原法和/或高频燃烧红外吸收法。
本发明一优选实施方式中,采用元素含量定量分析方法时,所述步骤S1中或步骤S2中的元素含量的检测方法采用GB/T 6730.62-2005《铁矿石钙、硅、镁、钛、磷、锰、铝和钡含量的测定波长色散X射线荧光光谱法》测定铁矿石中钙、镁、硅、铝、钛、磷、锰、铜的含量;采用GB/T 6730.5-2007《铁矿石全铁含量的测定三氯化钛还原法》测定铁矿石中全铁的含量;采用GB/T 6730.61-2005《铁矿石碳和硫含量的测定高频燃烧红外吸收法》测定铁矿石中硫的含量。
其中,采用波长色散X射线荧光光谱无标样分析方法时,所述步骤S1中或所述步骤S2中的元素含量的检测方法,所述铁矿石一般按照本领域常规方法进行前处理,先对铁矿石干燥、之后压片,再进行检测步骤:具体地,可包括如下步骤:将每个铁矿石分装到干燥瓶中于105℃下烘干4h;采用压片机对烘干铁矿石进行压片,压片前用乙醇清洗模具,使用聚乙烯环使粉末铁矿石聚拢,压制所述粉末铁矿石在30t压力下维持30s;检查压制铁矿石样品表面均匀且无裂纹、脱落现象,测量前用洗耳球吹净所述压制铁矿石样品表面。采用波长色散X射线荧光光谱无标样分析方法时,本领域技术人员知晓,所述步骤S1中的元素含量的测定与所述步骤S2中元素含量的测定为同一台检测仪器。
本发明中,所述缺失值处理一般可分为元素含量无法检出和元素含量未进行检测两种情况。当元素含量因无法检出而导致缺失时,所述缺失值处理较佳地采用以下几种方式的一种:(1)用0替代缺失值;(2)用检测限替代缺失值;(3)用检测限替代缺失值,并增加一组逻辑变量,若元素含量能够检出,则标记为1,若元素含量无法检出,则标记为0;(4)删除存在缺失值的元素含量数据。当元素含量因未进行检测而导致缺失时,所述缺失值处理较佳地采用同一原产国的样品中该元素含量的平均值替代。
本发明中,所述多变量异常数据检验一般可包括库克距离判断、马哈拉诺比斯距离判断、基于剩余方差的F检验法等,较佳地为基于剩余方差的F检验法。
本发明一优选实施方式中,所述多变量异常数据检验选用Pirouette多元数据分析软件基于剩余方差的F检验进行。
本发明中,较佳地,所述多变量异常数据检验后,先进行逐步判别分析,后建立Fisher判别模型。所述逐步判别分析本领域技术人员知晓为一种对变量进行逐步筛选的分析方法。所述逐步判别分析中,变量能否进入所述Fisher判别模型主要取决于协方差分析的F检验的显著性水平,当F值大于指定值时保留该变量,而F值小于指定值时,该变量从所述Fisher判别模型中剔除。选取合适的F值可以用最少的变量达到最佳的判别效果。其中,较佳地,所述逐步判别分析选取的F值为3.84。
本发明中,所述Fisher判别模型本领域技术人员可通过运用商用的软件自带的判别分析模块进行计算,例如,可以是SPSS软件;或者通过本领域技术人员知晓的操作自行编写程序得出所述Fisher判别模型。
较佳地,采用波长色散X射线荧光光谱无标样分析方法时,所述Fisher判别模型为至少四维以上的Fisher判别模型;例如,所述Fisher判别模型为四维Fisher判别模型,所述四维Fisher判别模型的变量元素为Fe、O、Si、Ca、Al、Mn、Ti、Mg、P、Na、Cr、K、Sr、S、Zn、V、Cu、Ba、Ni、Mo和Pb。其中,所述四维Fisher判别模型的4组判别函数为:
F1=-0.029X1-0.121X2-0.166X3+13.393X4+1.016X5+1.117X6-8.709X7-3.014X8-35.081X9+5.715X10-6.516X11-9.674X12+49.536X13-53.125X14+158.478X15+36.648X16+68.739X17-15.032X18-291.388X19+560.344X20-920.863X21+9.604
F2=0.266X1-0.115X2+0.036X3+6.576X4+0.652X5-2.365X6+13.54X7-7.79X8-46.172X9-11.156X10-49.525X11+31.216X12+262.112X13+2.559X14+13.094X15-23.33X16+10.458X17+27.165X18+168.438X19-2190.239X20+317.394X21-13.546
F3=0.02X1+0.843X2+0.089X3-4.128X4-0.751X5-0.438X6-9.018X7+6.14X8+37.322X9+8.864X10-81.072X11+15.107X12+135.869X13+40.742X14-25.678X15+31.234X16+8.987X17+10.862X18-161.474X19+426.269X20-90.978X21-28.975
F4=0.051X1+0.622X2+0.6X3+5.972X4-0.109X5-2.297X6-4.482X7+1.302X8-16.684X9-1.351X10+118.16X11+3.65X12+18.161X13+49.477X14-54.647X15-13.305X16-176.834X17-33.23X18+509.022X19-837.642X20+980.466X21-30.568
式中X1-X21分别代表Ca、K、O、V、Mg、Sr、Na、Zn、Al、Ti、Ni、Pb、P、Cr、Cu、Mo、Mn、S、Ba、Fe、Si的含量。
其中,所述四维Fisher判别模型中的各原产国组质心的坐标为:澳大利亚(-1.313,-2.088,0.229,0.311)、巴西(-0.507,-0.853,-3.589,-3.449)、南非(-1.715,9.877,1.244,-0.145)、哈萨克斯坦(16.519,-1.012,3.204,-0.66)、印度(9.5,5.368,-9.678,2.778)。
本发明中,所述步骤S2为将一待测样品铁矿石的元素含量代入步骤S1的Fisher判别模型中,确定待测样品铁矿石的原产国的确定方式,本领域技术人员知晓为,根据判别函数和组质心处坐标函数,计算每个样品坐标与质心的距离,与哪个类别的质心最近,该样品就判定为哪个原产国类别。
本领域技术人员知晓,本发明的鉴别铁矿石原产国的方法测定待测样品的适用范围为建立Fisher判别模型的原产国范围。非判别模型的原产国范围待测样品测得结果本领域技术人员知晓一般为定性结果,即非判别模型中的全部原产国。
本发明提供了一种铁矿石的品牌的鉴别方法,其步骤包括:
S1.取至少16个品牌,每个品牌至少11个批次的铁矿石中元素含量的数据,依次进行缺失值处理和多变量异常数据检验后,建立Fisher判别模型;
S2.将待测样品铁矿石的元素含量代到步骤S1的Fisher判别模型中,确定待测样品铁矿石的品牌;
其中,所述步骤S1和步骤S2的元素含量单位均为质量百分含量。
本发明中,本领域技术人员知晓,用于建立模型的品牌和批次的数据量为越多越好,因此对于品牌和批次的数据量上限不作特别限定,较佳地,品牌数为16~21个,批次数为11~308。
本发明中,所述元素含量的检测方法可如前所述。
本发明中,所述缺失值处理的方法可如前所述。
本发明中,所述多变量异常数据检验的方法可如前所述。
本发明中,较佳地,所述多变量异常数据检验后,先进行逐步判别分析,后建立Fisher判别模型。所述逐步判别分析的方法可如前所述。
本发明中,所述Fisher判别模型的建立方法可如前所述。
较佳地,采用波长色散X射线荧光光谱无标样分析方法时,所述Fisher判别模型为至少二十维以上的Fisher判别模型;例如,所述Fisher判别模型为二十维Fisher判别模型,所述二十维Fisher判别模型的变量元素为V、Ca、K、Al、O、Ti、Mn、Mo、Ni、P、Mg、Cu、Pb、Si、S、Na、Cl、Zn、Ba、Cr、Fe、Sr和Zr。
其中,所述二十维Fisher判别模型的20组判别函数为:
F1=-0.063X1-0.101X2+0.042X3-0.211X4-0.393X5+1.274X6+12.681X7+2.43X8-2.359X9+1.25X10-84.836X11+1.62X12+81.752X13-61.727X14-66.185X15-7.208X16+320.464X17+149.594X18+15.808X19+34.49X20+1341.336X21+2212.518X22-714.712X23-13.105
F2=-0.083X1+0.132X2+0.274X3-0.966X4+1.162X5-0.418X6-62.659X7-0.961X8+8.828X9-2.241X10+81.887X11+10.502X12-97.849X13+31.64X14+333.138X15+1.229X16+283.852X17-173.621X18-0.366X19-15.513X20+462.515X21+116.469X22+36.171X23-1.25
F3=0.315X1+0.334X2+0.64X3-3.535X4-0.409X5+0.758X6-14.496X7+0.578X8+16.922X9+2.247X10-2.182X11+48.737X12+76.039X13-44.933X14+110.534X15+63.579X16-0.155X17+142.501X18+73.302X19-9.263X20+237.108X21+7905.493X22-1784.531X23-19.163
F4=-0.473X1+0.694X2-0.121X3-2.552X4-0.961X5+5.697X6+2.682X7-7.921X8+49.775X9-1.376X10+5.835X11-28.097X12-102.866X13+20.6X14+44.635X15+71.872X16+21.298X17+147.882X18-9.479X19+58.986X20+229.354X21+2013.315X22-1323.799X23+9.025
F5=0.108X1+0.175X2-0.295X3-8.833X4-0.507X5-5.243X6+4.46X7-4.335X8+89.273X9-12.712X10-36.739X11+32.377X12+58.251X13+45.737X14-59.931X15-94.447X16-5.173X17+45.387X18+71.978X19-13.194X20+365.185X21-473.43X22+402.527X23-16.388
F6=0.259X1-1.18X2-0.117X3+5.973X4+4.182X5+2.551X6-1.029X7-8.209X8+94.513X9-10.296X10-16.609X11-6.28X12-130.495X13+26.263X14-21.244X15+78.535X16+11.012X17+62.304X18+3.182X19-8.022X20+53.495X21-3081.716X22-193.861X23+20.074
F7=-0.237X1+0.885X2-0.241X3+12.495X4+0.004X5+1.33X6+17.339X7-9.261X8-107.635X9-2.546X10+33.419X11+22.89X12+138.002X13-17.655X14+5.997X15+31.913X16+3.242X17-70.375X18+19.414X19+27.802X20-3.866X21-779.248X22+147.537X23-13.975
F8=0.133X1-0.405X2+1.142X3-5.801X4+0.669X5+7.614X6+7.494X7+3.116X8-17.998X9+0.024X10+58.323X11+6.647X12-86.098X13+67.68X14+82.742X15-124.629X16-30.193X17-150.62X18-13.873X19+91.953X20+197.849X21+316.906X22+645.565X23-5.737
F9=-0.118X1+0.666X2+0.162X3-3.057X4+1.71X5-3.628X6-7.012X7+8.576X8+38.102X9+10.308X10+15.782X11+13.503X12-18.877X13+6.272X14-52.801X15-50.711X16+25.108X17-44.043X18+4.047X19-22.067X20-187.925X21+1087.285X22+451.136X23-22.626
F10=0.17X1-0.023X2-0.902X3-1.778X4-1.257X5+9.724X6-2.135X7-2.973X8+77.872X9+9.571X10-156.977X11+6.929X12+27.111X13+38.703X14-118X15+23.827X16+23.706X17+120.225X18+12.035X19-13.022X20-318.435X21-1597.18X22-778.946X23+0.774
F11=0.262X1+0.255X2+0.656X3+4.495X4-1.675X5+2.201X6-7.671X7-1.168X8+73.007X9-1.284X10+36.638X11-4.172X12+129.771X13+186.729X14-160.162X15-44.892X16+1.742X17-144.907X18-30.262X19-41.62X20+315.988X21-4500.335X22+589.115X23-21.928
F12=0.213X1-0.188X2+1.096X3-0.356X4+0.284X5-3.161X6+0.565X7-3.883X8-26.717X9-1.096X10-160.913X11-13.164X12+380.179X13+52.561X14+76.201X15+134.173X16+11.243X17+69.368X18-34.754X19+3.668X20-142.142X21-4005.181X22-664.484X23+3.441
F13=-0.129X1-0.284X2-0.881X3-0.178X4-0.852X5-6.2X6+1.138X7+0.005X8+51.045X9+0.335X10+21.467X11-0.343X12-1.323X13+32.949X14-71.372X15+11.172X16+3.683X17-76.715X18+2.016X19+180.351X20-156.647X21-1148.028X22+264.453X23+19.223
F14=0.172X1+0.247X2-0.096X3+2.279X4+0.763X5+2.537X6-16.731X7+5.567X8-33.832X9-5.58X10-68.753X11-4.472X12+244.379X13-109.24X14-166.04X15-162.059X16+10.271X17+125.185X18-19.609X19+98.565X20+107.302X21+3090.702X22+1386.216X23-31.034
F15=0.297X1+0.42X2-0.584X3+1.712X4+0.514X5-1.647X6-4.37X7-5.255X8-22.224X9+13.195X10+39.976X11+12.38X12-17.661X13-7.259X14+445.893X15+56.371X16-21.351X17-14.384X18-73.177X19+5.709X20+691.479X21-1317.43X22-101.087X23-29.894
F16=0.222X1+0.119X2+0.737X3+1.946X4-0.366X5-0.744X6-8.885X7-4.436X8+15.704X9+6.564X10-127.562X11-11.813X12-60.213X13-26.78X14+450.072X15-121.086X16+18.853X17+207.622X18+90.621X19+39.786X20+77.822X21-396.644X22+700.916X23-24.072
F17=-0.186X1-0.084X2-0.586X3-0.984X4+0.155X5-1.49X6+10.751X7-3.176X8-30.355X9+10.455X10+139.124X11-1.368X12+152.237X13+75.898X14-96.072X15-201.967X16+5.898X17+62.329X18-18.722X19-38.364X20-271.459X21-1101.622X22+1313.381X23+9.716
F18=0.51X1+0.11X2-0.258X3+0.442X4+1.146X5-1.288X6+13.381X7+2.629X8-94.323X9+0.317X10+42.203X11-10.375X12+49.959X13+72.684X14+132.622X15-3.616X16-23.371X17-219.481X18+33.73X19-22.302X20-163.399X21+1162.658X22-97.907X23-34.317
F19=0.609X1+0.608X2+1.058X3+1.24X4-0.33X5-0.605X6+17.483X7-6.498X8+22.124X9+6.305X10+99.083X11+2.021X12-93.031X13-58.243X14-181.554X15+2.958X16+8.375X17+77.766X18-7.43X19+33.915X20-279.637X21+852.295X22-251.507X23-57.948
F20=0.378X1+0.205X2+0.207X3-0.628X4+0.837X5-0.622X6+6.064X7+0.222X8-52.396X9+8.217X10+13.307X11-15.451X12+150.942X13+24.719X14-308.208X15+68.887X16-20.844X17-40.765X18+69.994X19+8.296X20+137.474X21-597.944X22-534.342X23-27.342
式中X1-X23分别代表V、Ca、K、Al、O、Ti、Mn、Mo、Ni、P、Mg、Cu、Pb、Si、S、Na、Cl、Zn、Ba、Cr、Fe、Sr、Zr的含量。
其中,所述二十维Fisher判别模型中的各品牌组质心的坐标为:津布巴混合粉铁矿(-13.74,4.041,-4.937,2.913,2.048,7.485,-1.912,0.948,4.704,-1.615,-0.169,1.088,-0.779,-0.669,0.785,0.819,0.275,0.323,-0.03,0.005)、巴西铁矿石精粉(-12.064,2.005,-0.822,-2.521,-0.81,3.068,-4.306,1.365,-4.193,-1.141,-0.824,0.535,0.078,-0.734,-1.457,-0.318,0.396,0.378,0.053,-0.009)、皮尔巴拉混合块(-12.949,1.908,-3.727,1.266,0.791,-0.457,-1.373,-1.672,-0.851,1.372,1.062,-0.543,0.267,0.257,0.176,-0.027,-0.155,0.224,-0.103,-0.033)、纽曼混合块铁矿(-12.899,2.385,-2.854,0.572,1.044,0.35,-2.78,-0.914,-0.663,0.424,0.945,0.488,0.199,0.13,-0.416,0.491,-0.376,-0.142,0.083,0.016)、国王粉铁矿(-12.533,0.497,-4.675,3.654,-0.135,-5.287,1.685,-2.999,-0.898,-1.014,-0.534,0.829,-0.079,-1.907,0.463,-0.566,-0.796,-0.004,-0.029,0.01)、皮尔巴拉混合粉(-12.984,2.086,-4.459,2.448,1.324,2.452,-1.199,-0.704,1.796,0.022,0.33,-0.509,0.23,-0.151,0.196,-0.364,0.229,-0.081,0.131,-0.004)、澳大利亚球团矿(165.394,79.118,-3.584,2.763,3.639,-0.23,-0.996,0.966,-1.315,-0.745,-0.003,-0.147,-0.031,-0.002,0.109,0.004,-0.011,-0.019,-0.004,-0.000009175)、杨迪粉铁矿(-11.702,1.767,-5.162,3.143,-0.382,-7.174,2.306,-2.686,-0.077,-1.819,-0.282,0.196,-0.562,0.073,-0.229,0.273,0.367,-0.153,-0.003,-0.046)、哈杨粉铁矿(-11.906,1.278,-4.557,1.875,0.001,-5.839,1.09,-3.249,-1.037,-0.702,-0.787,-0.477,0.063,0.687,0.212,0.056,0.149,0.237,0.038,0.072)、纽曼混合粉铁矿(-12.761,3.012,-4.069,0.77,0.271,3.789,-1.739,0.525,3.576,-2.575,-1.626,-0.254,-0.316,0.956,-0.405,-0.297,-0.567,-0.041,-0.076,-0.01)、南非铁矿石精粉(85.285,-76.585,-2.265,-1.14,0.265,0.017,-0.023,-0.033,0.331,-0.05,0.069,0.018,-0.003,0.001,-0.022,0.002,-0.002,0.007,0.001,0.00003174)、澳大利亚铁矿石精粉(-10.88,2.65,8.809,-12.993,-3.43,-6.47,-6.007,3.98,0.477,-0.725,-0.065,-0.371,-0.019,-0.161,0.375,0.042,0.034,-0.049,-0.01,0.00006576)、弗特斯克混合粉(-11.728,0.615,-4.602,6.385,-2.414,-0.8,3.188,5.057,-0.562,1.769,0.486,-0.207,-0.953,0.041,-0.119,-0.101,-0.062,0.006,0.008,0.011)、卡拉加斯铁矿石(-10.983,-2.358,-1.665,1.208,0.649,4.443,-3.304,-1.598,-3.57,4.173,-2.215,-0.002,-0.195,-0.053,0.376,0.146,0.168,-0.224,-0.07,-0.001)、哈萨克斯坦球团矿(29.034、22.503、2.421、-11.711、-15.447、1.488、4.282、-4.022、4.645、3.528、-0.124、0.541、0.163、-0.055、-0.385、-0.026、0.03、0.06、0.014、0.001)、哈萨克斯坦粉铁矿(4.705,-2.123,53.032,16.516,-3.587,1.059,-0.883,-1.324,-0.045,-0.735,0.134,-0.041,0.018,0.052,0.01,-0.002,0.006,0.006,-0.002,0.00009545)、昆巴标准粉(-11.268,3.332,13.031,-10.701,9.927,0.94,6.159,0.211,1.066,0.745,-0.412,-1.013,0.064,-0.521,-0.35,0.275,-0.08,0.011,-0.005,0.002)、超特粉铁矿(-11.207,-0.385,-5.802,7.727,-2.864,-1.666,5.102,5.92,-0.283,-0.366,-1.314,0.522,2.21,0.14,0.26,0.319,-0.043,0.044,0.037,-0.023)、麦克粉铁矿(-13.259,2.805,-4.929,3.418,1.387,0.621,-0.363,0.584,2.171,-0.77,1.172,0.316,0.755,-0.371,-0.464,-0.221,0.531,-0.265,-0.318,0.051)、昆巴标准块(-11.037,3.029,8.749,-8.766,6.34,0.21,2.456,0.413,-1.23,0.429,0.471,2.019,-0.118,0.911,0.365,-0.505,0.103,0.008,0.018,-0.005)、印度球团矿(-12.274,-0.026,-1.88,-8.696,-8.259,10.016,4.745,-0.796,-5.937,-4.612,0.798,-0.632,-0.103,0.037,0.474,0.104,-0.027,0.121,-0.022,0.005)。
较佳地,采用元素含量定量分析方法时,所述Fisher判别模型为至少九维以上的Fisher判别模型;例如,所述Fisher判别模型为九维Fisher判别模型,所述九维Fisher判别模型的变量元素为Fe、Al、Si、Cu、P、Ti、Ca、Mg和S。
其中,所述九维Fisher判别模型的9组判别函数为:
F1=-0.141X1-2.026X2-1.667X3+37.182X4+13.058X5+29.733X6+5.865X7+45.805X8-3.605X9+5.864
F2=3.801X1+3.457X2+4.088X3-5.378X4+37.252X5-4.948X6+0.77X7+0.052X8+36.704X9-248.587
F3=0.297X1+0.024X2+4.666X3-15.711X4-85.076X5-8.798X6+0.873X7+11.986X8-16.159X9-21.18
F4=0.249X1+6.715X2+3.81X3-24.212X4+47.508X5+7.362X6-0.79X7-2.194X8+41.336X9-34.085
F5=0.396X1+0.768X2-0.304X3+108.72X4-61.619X5+33.1X6+2.343X7-25.031X8+69.08X9-20.631
F6=-0.251X1-5.098X2+1.456X3-37.041X4+60.3X5+9.783X6+8.868X7-14.049X8+79.13X9+11.807
F7=0.097X1-0.352X2+1.195X3+64.192X4+35.604X5+14.205X6+4.483X7-11.092X8-175.299X9-8.031
F8=-0.153X1+1.719X2-1.303X3-96.561X4-17.174X5-17.788X6+16.88X7+6.077X8+15.726X9+10.836
F9=-0.107X1+0.293X2+0.33X3+497.005X4+7.154X5-18.482X6+2.855X7-1.072X8+18.587X9+5.359
式中X1-X9分别代表Fe、Al、Si、Cu、P、Ti、Ca、Mg、S的含量。
所述九维Fisher判别模型中的各品牌组质心的坐标为:津布巴混合粉铁矿(-3.68,3.382,-4.525,7.245,-1.86,-0.289,1.153,-0.357,0.27)、皮尔巴拉混合块(-2.522,1.313,-3.494,-3.05,-0.251,0.347,-0.302,-0.029,-0.025)、皮尔巴拉混合粉(-2.201,1.313,-3.676,2.304,-0.678,0.074,0.006,0.085,0.014)、纽曼混合块铁矿(-2.783,4.496,-1.858,-2.153,-0.277,0.580,-0.161,-0.107,-0.027)、纽曼混合粉铁矿(-1.451,5.482,-0.482,2.723,-0.886,-1.609,0.5,0.245,-0.139)、杨迪粉铁矿(-3.582,-15.16,5.795,-0.649,-0.105,0.553,0.547,-0.107,0.244)、哈杨粉铁矿(-2.441,-13.014,1.734,-3.094,-0.4,-0.821,0.153,0.456,-0.091)、澳大利亚铁矿石精粉(4.172,15.551,18.347,-1.537,-3.274,-0.512,-0.813,-0.279,0.061)、国王粉铁矿(-3.351,-12.551,2.657,0.985,0.491,0.735,0.939,-1.101,-0.328)、弗特斯克混合粉(-3.772,-8.168,2.451,5.086,2.137,-0.015,-1.496,0.023,0.013)、麦克粉铁矿(-3.769,0.427,-0.181,3.75,-0.122,0.132,-0.353,-0.582,0.118)、超特粉铁矿(-4.128,-11.961,4.262,7.55,2.738,-1.127,-1.172,-0.593,-0.316)、昆巴标准粉(-3.219,10.222,5.062,1.567,2.472,0.876,0.402,1.293,0.065)、昆巴标准块(-4.581,11.283,6.096,0.143,2.412,0.437,0.903,-0.364,-0.169)、南非铁矿石精粉(151.238,-1.833,-0.92,0.417,0.406,0.174,0.082,0,-0.008)、卡拉加斯铁矿石(-0.605,8.763,-4.11,-5.676,3.935,-2.216,0.077,-0.64,0.317)。
本发明中,所述步骤S2为将一待测样品铁矿石的元素含量代入步骤S1的Fisher判别模型中,确定待测样品铁矿石的品牌的确定方式,本领域技术人员知晓为,根据判别函数和组质心处坐标函数,计算每个样品坐标与质心的距离,与哪个类别的质心最近,该样品就判定为哪个品牌类别。
本领域技术人员知晓,本发明的鉴别铁矿石品牌的方法测定待测样品的适用范围为建立Fisher判别模型的品牌范围。非判别模型的品牌范围待测样品测得结果本领域技术人员知晓一般为定性结果,即非判别模型中的全部品牌。
在符合本领域常识的基础上,上述各优选条件,可任意组合,即得本发明各较佳实例。
本发明的积极进步效果在于:
本发明的铁矿石原产国或品牌鉴别方法系统、准确、可靠,且随着样品的增多,能够建立用于铁矿石原产国或品牌的数据库。
具体实施方式
下面通过实施例的方式进一步说明本发明,但并不因此将本发明限制在所述的实施例范围之中。下列实施例中未注明具体条件的实验方法,按照常规方法和条件,或按照商品说明书选择。
实施例1.1-1.422
本系列实施例为判别铁矿石原产国的方法(波长色散X射线荧光光谱无标样分析方法)。
1、样品收集
根据GB/T 10322.1-2014《铁矿石取样和制样方法》,从我国主要的铁矿石进口口岸采集并制备来自澳大利亚、巴西、南非、哈萨克斯坦、印度5个原产国的21个品牌422批次进口铁矿石化学分析样品。所述21个品牌铁矿石包括津布巴混合粉铁矿、巴西铁矿石精粉、皮尔巴拉混合块、纽曼混合块铁矿、国王粉铁矿、皮尔巴拉混合粉、澳大利亚球团矿、杨迪粉铁矿、哈杨粉铁矿、纽曼混合粉铁矿、南非铁矿石精粉、澳大利亚铁矿石精粉、弗特斯克混合粉、卡拉加斯铁矿石、哈萨克斯坦球团矿、哈萨克斯坦铁矿粉、昆巴标准粉、超特粉铁矿、麦克粉铁矿、昆巴标准块、印度球团矿。样品信息如表1所示。
表1样品信息
2、样品检测
将采集样品分装到玻璃广口瓶中于105℃下烘干4h。采用压片机对烘干样品进行压片,压片前用乙醇清洗模具,使用聚乙烯环使粉末样品聚拢,压制样品在30t压力下维持30s~60s。检查压制样品表面均匀且无裂纹、脱落现象,测量前用洗耳球吹去样品表面浮粉。
使用德国布鲁克公司S4 Pioneer波长色散X射线荧光光谱仪中的无标样分析方法检测铁矿石中各元素的含量。检测中使用铑靶光管、四个分析仪晶体(LiF200、XS-55、PET和Ge)、流气计数器(FC)、闪烁计数器(SC)等元件。
针对采集的422个铁矿石样品,采用波长色散-X射线荧光光谱无标样分析可以检测到Fe、O、Si、Ca、Al、Mn、Tb、Ti、Mg、P、Na、Cr、K、Sr、S、Zr、Zn、V、Cu、Gd、Ba、Cl、Ni、Co、Mo、Pb共26个元素的含量,其中Na、Cr、K、Sr、S、Zr、Zn、V、Cu、Gd、Ba、Cl、Ni、Co、Mo、Pb共16个元素含量存在未检出的情况。
3、缺失值处理
对于Na、Cr、K、Sr、S、Zr、Zn、V、Cu、Gd、Ba、Cl、Ni、Co、Mo、Pb中存在未检出的情况,进行缺失值处理。缺失值用检测限替代。
4、多变量异常数据检验
本实施例使用Pirouette多元数据分析软件基于剩余方差的F检验进行异常数据的剔除。通过与F0.01检验临界值表比对,皮尔巴拉混合块、纽曼混合块铁矿、纽曼混合粉铁矿各有一组数据计算得出的F统计量大于F0.01检验临界值,认为这3组数据为异常数据,因此将这3组数据剔除,剩余419组数据用于后续分析。
5、逐步判别分析
铁矿石生产原产国判别模型中,对Fe、O、Si、Ca、Al、Mn、Tb、Ti、Mg、P、Na、Cr、K、Sr、S、Zr、Zn、V、Cu、Gd、Ba、Cl、Ni、Co、Mo、Pb共26个元素的含量进行变量筛选,变量能否进入模型主要取决于协方差分析的F检验的显著性水平,当F值大于指定值时保留该变量,而F值小于指定值时,该变量从模型中剔除。选取合适的F值可以用最少的变量达到最佳的判别效果。其中,选取的F值为3.84。经过逐步判别分析筛选出Ca、K、O、V、Mg、Sr、Na、Zn、Al、Ti、Ni、Pb、P、Cr、Cu、Mo、Mn、S、Ba、Fe、Si共21个元素含量作为特征变量(F-score值>3.84)保留在模型中,Zr、Tb、Cl、Gd、Co等元素含量因未通过F检验(F-score值<2.71)而从模型中剔除。
6、建立四维Fisher判别模型
本实施例选用共计419个铁矿石样品用于建立Fisher判别模型,建模过程中选取318个样品作为训练集,101个样品作为验证集检验模型的准确性。
采用Ca、K、O、V、Mg、Sr、Na、Zn、Al、Ti、Ni、Pb、P、Cr、Cu、Mo、Mn、S、Ba、Fe、Si共21个元素含量,建立四维Fisher判别模型和相应的组质心处的坐标。
四维Fisher判别模型的4组判别函数为:
F1=-0.029X1-0.121X2-0.166X3+13.393X4+1.016X5+1.117X6-8.709X7-3.014X8-35.081X9+5.715X10-6.516X11-9.674X12+49.536X13-53.125X14+158.478X15+36.648X16+68.739X17-15.032X18-291.388X19+560.344X20-920.863X21+9.604
F2=0.266X1-0.115X2+0.036X3+6.576X4+0.652X5-2.365X6+13.54X7-7.79X8-46.172X9-11.156X10-49.525X11+31.216X12+262.112X13+2.559X14+13.094X15-23.33X16+10.458X17+27.165X18+168.438X19-2190.239X20+317.394X21-13.546
F3=0.02X1+0.843X2+0.089X3-4.128X4-0.751X5-0.438X6-9.018X7+6.14X8+37.322X9+8.864X10-81.072X11+15.107X12+135.869X13+40.742X14-25.678X15+31.234X16+8.987X17+10.862X18-161.474X19+426.269X20-90.978X21-28.975
F4=0.051X1+0.622X2+0.6X3+5.972X4-0.109X5-2.297X6-4.482X7+1.302X8-16.684X9-1.351X10+118.16X11+3.65X12+18.161X13+49.477X14-54.647X15-13.305X16-176.834X17-33.23X18+509.022X19-837.642X20+980.466X21-30.568
式中X1-X21分别代表Ca、K、O、V、Mg、Sr、Na、Zn、Al、Ti、Ni、Pb、P、Cr、Cu、Mo、Mn、S、Ba、Fe、Si的含量。
四维Fisher判别模型中的各原产国组质心的坐标为:澳大利亚(-1.313,-2.088,0.229,0.311)、巴西(-0.507,-0.853,-3.589,-3.449)、南非(-1.715,9.877,1.244,-0.145)、哈萨克斯坦(16.519,-1.012,3.204,-0.66)、印度(9.5,5.368,-9.678,2.778)。
本次判别铁矿石原产国的方法的实施例包括两类实施例:
第一类为构建模型所用的样品,即建模样品实施例。其中,建模样品实施例分别进行建模样品验证和交叉验证。建模样品验证为将构建模型所用的样品数据回代到模型,进行验证;交叉验证法为建模前每次留出一个作为验证的数据再次代入判别函数,进行验证。
第二类为采用未知的待测样品验证的实施例(测试样品验证)。为了确定所建立的Fisher判别模型是否可以对未包含在模型中的样品进行识别,选择101个作为测试样品的铁矿石样品。经过统计,所建立的四维判别模型对原产国的识别正确率如下表2所示,模型判别准确率分别为99.1%、98.4%、100%,说明此模型可以对铁矿石的原产国进行很好的识别。
表2原产国判别模型具体判别结果
原产国 | 建模样品验证 | 交叉验证 | 测试样品验证 |
澳大利亚 | 99.60% | 99.60% | 100% |
巴西 | 95.50% | 95.50% | 100% |
南非 | 100% | 100% | 100% |
哈萨克斯坦 | 100% | 88.90% | 100% |
印度 | 88.90% | 88.90% | 100% |
总计 | 99.10% | 98.40% | 100% |
实施例2.1-2.422
本系列实施例为判别铁矿石品牌的方法(波长色散X射线荧光光谱无标样分析方法)。
本实施例中的样品收集、样品检测、缺失值处理、多变量异常数据检验均同实施例1.1-1.422。
1、逐步判别分析
铁矿石品牌的判别模型中,对Fe、O、Si、Ca、Al、Mn、Tb、Ti、Mg、P、Na、Cr、K、Sr、S、Zr、Zn、V、Cu、Gd、Ba、Cl、Ni、Co、Mo、Pb共26个元素的含量进行变量筛选,变量能否进入模型主要取决于协方差分析的F检验的显著性水平,当F值大于指定值时保留该变量,而F值小于指定值时,该变量从模型中剔除。选取合适的F值可以用最少的变量达到最佳的判别效果。其中,选取的F值为3.84。经过逐步判别分析筛选出V、Ca、K、Al、O、Ti、Mn、Mo、Ni、P、Mg、Cu、Pb、Si、S、Na、Cl、Zn、Ba、Cr、Fe、Sr、Zr共23个元素含量作为特征变量(F-score值>3.84)保留在了模型中,Gd、Co因未通过F检验(F-score值<2.71)而从模型中剔除。
2、建立二十维Fisher判别模型
本实施例选用共计419个铁矿石样品用于建立Fisher判别模型,建模过程中选取318个样品作为训练集,101个样品作为验证集检验模型的准确性。
采用V、Ca、K、Al、O、Ti、Mn、Mo、Ni、P、Mg、Cu、Pb、Si、S、Na、Cl、Zn、Ba、Cr、Fe、Sr、Zr共23个元素含量,建立二十维Fisher判别模型和相应的组质心处的坐标。
二十维Fisher判别模型的20组判别函数为:
F1=-0.063X1-0.101X2+0.042X3-0.211X4-0.393X5+1.274X6+12.681X7+2.43X8-2.359X9+1.25X10-84.836X11+1.62X12+81.752X13-61.727X14-66.185X15-7.208X16+320.464X17+149.594X18+15.808X19+34.49X20+1341.336X21+2212.518X22-714.712X23-13.105
F2=-0.083X1+0.132X2+0.274X3-0.966X4+1.162X5-0.418X6-62.659X7-0.961X8+8.828X9-2.241X10+81.887X11+10.502X12-97.849X13+31.64X14+333.138X15+1.229X16+283.852X17-173.621X18-0.366X19-15.513X20+462.515X21+116.469X22+36.171X23-1.25
F3=0.315X1+0.334X2+0.64X3-3.535X4-0.409X5+0.758X6-14.496X7+0.578X8+16.922X9+2.247X10-2.182X11+48.737X12+76.039X13-44.933X14+110.534X15+63.579X16-0.155X17+142.501X18+73.302X19-9.263X20+237.108X21+7905.493X22-1784.531X23-19.163
F4=-0.473X1+0.694X2-0.121X3-2.552X4-0.961X5+5.697X6+2.682X7-7.921X8+49.775X9-1.376X10+5.835X11-28.097X12-102.866X13+20.6X14+44.635X15+71.872X16+21.298X17+147.882X18-9.479X19+58.986X20+229.354X21+2013.315X22-1323.799X23+9.025
F5=0.108X1+0.175X2-0.295X3-8.833X4-0.507X5-5.243X6+4.46X7-4.335X8+89.273X9-12.712X10-36.739X11+32.377X12+58.251X13+45.737X14-59.931X15-94.447X16-5.173X17+45.387X18+71.978X19-13.194X20+365.185X21-473.43X22+402.527X23-16.388
F6=0.259X1-1.18X2-0.117X3+5.973X4+4.182X5+2.551X6-1.029X7-8.209X8+94.513X9-10.296X10-16.609X11-6.28X12-130.495X13+26.263X14-21.244X15+78.535X16+11.012X17+62.304X18+3.182X19-8.022X20+53.495X21-3081.716X22-193.861X23+20.074
F7=-0.237X1+0.885X2-0.241X3+12.495X4+0.004X5+1.33X6+17.339X7-9.261X8-107.635X9-2.546X10+33.419X11+22.89X12+138.002X13-17.655X14+5.997X15+31.913X16+3.242X17-70.375X18+19.414X19+27.802X20-3.866X21-779.248X22+147.537X23-13.975
F8=0.133X1-0.405X2+1.142X3-5.801X4+0.669X5+7.614X6+7.494X7+3.116X8-17.998X9+0.024X10+58.323X11+6.647X12-86.098X13+67.68X14+82.742X15-124.629X16-30.193X17-150.62X18-13.873X19+91.953X20+197.849X21+316.906X22+645.565X23-5.737
F9=-0.118X1+0.666X2+0.162X3-3.057X4+1.71X5-3.628X6-7.012X7+8.576X8+38.102X9+10.308X10+15.782X11+13.503X12-18.877X13+6.272X14-52.801X15-50.711X16+25.108X17-44.043X18+4.047X19-22.067X20-187.925X21+1087.285X22+451.136X23-22.626
F10=0.17X1-0.023X2-0.902X3-1.778X4-1.257X5+9.724X6-2.135X7-2.973X8+77.872X9+9.571X10-156.977X11+6.929X12+27.111X13+38.703X14-118X15+23.827X16+23.706X17+120.225X18+12.035X19-13.022X20-318.435X21-1597.18X22-778.946X23+0.774
F11=0.262X1+0.255X2+0.656X3+4.495X4-1.675X5+2.201X6-7.671X7-1.168X8+73.007X9-1.284X10+36.638X11-4.172X12+129.771X13+186.729X14-160.162X15-44.892X16+1.742X17-144.907X18-30.262X19-41.62X20+315.988X21-4500.335X22+589.115X23-21.928
F12=0.213X1-0.188X2+1.096X3-0.356X4+0.284X5-3.161X6+0.565X7-3.883X8-26.717X9-1.096X10-160.913X11-13.164X12+380.179X13+52.561X14+76.201X15+134.173X16+11.243X17+69.368X18-34.754X19+3.668X20-142.142X21-4005.181X22-664.484X23+3.441
F13=-0.129X1-0.284X2-0.881X3-0.178X4-0.852X5-6.2X6+1.138X7+0.005X8+51.045X9+0.335X10+21.467X11-0.343X12-1.323X13+32.949X14-71.372X15+11.172X16+3.683X17-76.715X18+2.016X19+180.351X20-156.647X21-1148.028X22+264.453X23+19.223
F14=0.172X1+0.247X2-0.096X3+2.279X4+0.763X5+2.537X6-16.731X7+5.567X8-33.832X9-5.58X10-68.753X11-4.472X12+244.379X13-109.24X14-166.04X15-162.059X16+10.271X17+125.185X18-19.609X19+98.565X20+107.302X21+3090.702X22+1386.216X23-31.034
F15=0.297X1+0.42X2-0.584X3+1.712X4+0.514X5-1.647X6-4.37X7-5.255X8-22.224X9+13.195X10+39.976X11+12.38X12-17.661X13-7.259X14+445.893X15+56.371X16-21.351X17-14.384X18-73.177X19+5.709X20+691.479X21-1317.43X22-101.087X23-29.894
F16=0.222X1+0.119X2+0.737X3+1.946X4-0.366X5-0.744X6-8.885X7-4.436X8+15.704X9+6.564X10-127.562X11-11.813X12-60.213X13-26.78X14+450.072X15-121.086X16+18.853X17+207.622X18+90.621X19+39.786X20+77.822X21-396.644X22+700.916X23-24.072
F17=-0.186X1-0.084X2-0.586X3-0.984X4+0.155X5-1.49X6+10.751X7-3.176X8-30.355X9+10.455X10+139.124X11-1.368X12+152.237X13+75.898X14-96.072X15-201.967X16+5.898X17+62.329X18-18.722X19-38.364X20-271.459X21-1101.622X22+1313.381X23+9.716
F18=0.51X1+0.11X2-0.258X3+0.442X4+1.146X5-1.288X6+13.381X7+2.629X8-94.323X9+0.317X10+42.203X11-10.375X12+49.959X13+72.684X14+132.622X15-3.616X16-23.371X17-219.481X18+33.73X19-22.302X20-163.399X21+1162.658X22-97.907X23-34.317
F19=0.609X1+0.608X2+1.058X3+1.24X4-0.33X5-0.605X6+17.483X7-6.498X8+22.124X9+6.305X10+99.083X11+2.021X12-93.031X13-58.243X14-181.554X15+2.958X16+8.375X17+77.766X18-7.43X19+33.915X20-279.637X21+852.295X22-251.507X23-57.948
F20=0.378X1+0.205X2+0.207X3-0.628X4+0.837X5-0.622X6+6.064X7+0.222X8-52.396X9+8.217X10+13.307X11-15.451X12+150.942X13+24.719X14-308.208X15+68.887X16-20.844X17-40.765X18+69.994X19+8.296X20+137.474X21-597.944X22-534.342X23-27.342
式中X1-X23分别代表V、Ca、K、Al、O、Ti、Mn、Mo、Ni、P、Mg、Cu、Pb、Si、S、Na、Cl、Zn、Ba、Cr、Fe、Sr、Zr的含量。
二十维Fisher判别模型中的各品牌组质心的坐标为:津布巴混合粉铁矿(-13.74,4.041,-4.937,2.913,2.048,7.485,-1.912,0.948,4.704,-1.615,-0.169,1.088,-0.779,-0.669,0.785,0.819,0.275,0.323,-0.03,0.005)、巴西铁矿石精粉(-12.064,2.005,-0.822,-2.521,-0.81,3.068,-4.306,1.365,-4.193,-1.141,-0.824,0.535,0.078,-0.734,-1.457,-0.318,0.396,0.378,0.053,-0.009)、皮尔巴拉混合块(-12.949,1.908,-3.727,1.266,0.791,-0.457,-1.373,-1.672,-0.851,1.372,1.062,-0.543,0.267,0.257,0.176,-0.027,-0.155,0.224,-0.103,-0.033)、纽曼混合块铁矿(-12.899,2.385,-2.854,0.572,1.044,0.35,-2.78,-0.914,-0.663,0.424,0.945,0.488,0.199,0.13,-0.416,0.491,-0.376,-0.142,0.083,0.016)、国王粉铁矿(-12.533,0.497,-4.675,3.654,-0.135,-5.287,1.685,-2.999,-0.898,-1.014,-0.534,0.829,-0.079,-1.907,0.463,-0.566,-0.796,-0.004,-0.029,0.01)、皮尔巴拉混合粉(-12.984,2.086,-4.459,2.448,1.324,2.452,-1.199,-0.704,1.796,0.022,0.33,-0.509,0.23,-0.151,0.196,-0.364,0.229,-0.081,0.131,-0.004)、澳大利亚球团矿(165.394,79.118,-3.584,2.763,3.639,-0.23,-0.996,0.966,-1.315,-0.745,-0.003,-0.147,-0.031,-0.002,0.109,0.004,-0.011,-0.019,-0.004,-0.000009175)、杨迪粉铁矿(-11.702,1.767,-5.162,3.143,-0.382,-7.174,2.306,-2.686,-0.077,-1.819,-0.282,0.196,-0.562,0.073,-0.229,0.273,0.367,-0.153,-0.003,-0.046)、哈杨粉铁矿(-11.906,1.278,-4.557,1.875,0.001,-5.839,1.09,-3.249,-1.037,-0.702,-0.787,-0.477,0.063,0.687,0.212,0.056,0.149,0.237,0.038,0.072)、纽曼混合粉铁矿(-12.761,3.012,-4.069,0.77,0.271,3.789,-1.739,0.525,3.576,-2.575,-1.626,-0.254,-0.316,0.956,-0.405,-0.297,-0.567,-0.041,-0.076,-0.01)、南非铁矿石精粉(85.285,-76.585,-2.265,-1.14,0.265,0.017,-0.023,-0.033,0.331,-0.05,0.069,0.018,-0.003,0.001,-0.022,0.002,-0.002,0.007,0.001,0.00003174)、澳大利亚铁矿石精粉(-10.88,2.65,8.809,-12.993,-3.43,-6.47,-6.007,3.98,0.477,-0.725,-0.065,-0.371,-0.019,-0.161,0.375,0.042,0.034,-0.049,-0.01,0.00006576)、弗特斯克混合粉(-11.728,0.615,-4.602,6.385,-2.414,-0.8,3.188,5.057,-0.562,1.769,0.486,-0.207,-0.953,0.041,-0.119,-0.101,-0.062,0.006,0.008,0.011)、卡拉加斯铁矿石(-10.983,-2.358,-1.665,1.208,0.649,4.443,-3.304,-1.598,-3.57,4.173,-2.215,-0.002,-0.195,-0.053,0.376,0.146,0.168,-0.224,-0.07,-0.001)、哈萨克斯坦球团矿(29.034、22.503、2.421、-11.711、-15.447、1.488、4.282、-4.022、4.645、3.528、-0.124、0.541、0.163、-0.055、-0.385、-0.026、0.03、0.06、0.014、0.001)、哈萨克斯坦粉铁矿(4.705,-2.123,53.032,16.516,-3.587,1.059,-0.883,-1.324,-0.045,-0.735,0.134,-0.041,0.018,0.052,0.01,-0.002,0.006,0.006,-0.002,0.00009545)、昆巴标准粉(-11.268,3.332,13.031,-10.701,9.927,0.94,6.159,0.211,1.066,0.745,-0.412,-1.013,0.064,-0.521,-0.35,0.275,-0.08,0.011,-0.005,0.002)、超特粉铁矿(-11.207,-0.385,-5.802,7.727,-2.864,-1.666,5.102,5.92,-0.283,-0.366,-1.314,0.522,2.21,0.14,0.26,0.319,-0.043,0.044,0.037,-0.023)、麦克粉铁矿(-13.259,2.805,-4.929,3.418,1.387,0.621,-0.363,0.584,2.171,-0.77,1.172,0.316,0.755,-0.371,-0.464,-0.221,0.531,-0.265,-0.318,0.051)、昆巴标准块(-11.037,3.029,8.749,-8.766,6.34,0.21,2.456,0.413,-1.23,0.429,0.471,2.019,-0.118,0.911,0.365,-0.505,0.103,0.008,0.018,-0.005)、印度球团矿(-12.274,-0.026,-1.88,-8.696,-8.259,10.016,4.745,-0.796,-5.937,-4.612,0.798,-0.632,-0.103,0.037,0.474,0.104,-0.027,0.121,-0.022,0.005)。
测试结果如下表3所示,模型判别准确率分别为96.2%、93.1%、95.0%。
表3品牌判别模型具体判别结果
实施例3-4
本实施例中的样品检测、缺失值处理、多变量异常数据检验同实施例1.1-1.422或实施例2.1-2.422。
表4样品信息
如表4所示,16类品牌铁矿石359个样品使用波长色散X射线荧光光谱无标样分析方法测量数据,分别使用全部检出元素含量(Fe、O、Si、Ca、Al、Mn、Tb、Ti、Mg、P、Na、Cr、K、Sr、S、Zr、Zn、V、Cu、Gd、Ba、Cl、Ni、Co、Mo、Pb,26个)和与部分检出元素含量(Fe、Al、Si、Cu、P、Mn、Ti、Ca、Mg、S,10个)作为输入变量,建立判别分析模型,结果如表5所示。使用10输入变量建立的判别分析模型在建模样品验证与交叉验证中的准确率均低于26输入变量建立的判别分析模型,测试样品验证中两个模型的准确率相同。
两个模型均有5个未知样品判别错误。26输入变量模型中:9个皮尔巴拉混合块中有1个误判为纽曼混合块铁矿,3个超特粉铁矿中有1个误判为弗特斯克混合粉,7个弗特斯克混合粉中有3个误判为皮尔巴拉混合粉。10输入变量模型中:5个纽曼混合粉铁矿中有1个误判为皮尔巴拉混合粉,6个杨迪粉铁矿中有1个误判为哈杨粉铁矿,7个弗特斯克混合粉中有3误判为皮尔巴拉混合粉。皮尔巴拉混合块/粉与纽曼混合块铁矿/粉均位于西澳皮尔巴拉矿区,矿山位置接近,成矿原因相同;杨迪粉铁矿与哈杨粉铁矿均来自西澳皮尔巴拉地区的杨迪矿山;皮尔巴拉混合粉主要由布鲁克曼2、布鲁克曼4、霍普唐斯4、汤姆普利斯、帕拉伯杜共5个地区的铁矿石混合而成,其中布鲁克曼2、布鲁克曼4、汤姆普利斯矿区与弗特斯克混合粉产区所罗门枢纽十分接近;在钢厂的实际生产过程中,超特粉铁矿可与弗特斯克混合粉相互替代,两种品牌铁矿石在元素含量方面差异比较接近。经过分析发现发生判别错误的情况主要因为这些品牌样品之间差异较小,增加输入变量可增加判别准确率。
表5判别模型具体判别结果
实施例5.1-5.1471
本系列实施例为基于元素含量定量分析方法鉴别铁矿石品牌的方法。
1、样品收集
根据GB/T 10322.1-2014《铁矿石取样和制样方法》,从我国主要铁矿石进口口岸采集并制备来自澳大利亚、南非、巴西3个原产国的16个品牌1471批次进口铁矿石化学分析样品,样品容量大、种类丰富,具有一定的独立性、代表性,基本包含了海关口岸日常检测中的主要铁矿石类别。16个铁矿石品牌为:津布巴混合粉铁矿、皮尔巴拉混合块、皮尔巴拉混合粉、纽曼混合块铁矿、纽曼混合粉铁矿、杨迪粉铁矿、哈杨粉铁矿、澳大利亚铁矿石精粉、国王粉铁矿、弗特斯克混合粉、麦克粉铁矿、超特粉铁矿、昆巴标准粉、昆巴标准块、南非铁矿石精粉、卡拉加斯铁矿石。样品信息如表6所示。
表6样品信息
2、样品检测
针对来自澳大利亚、南非、巴西3个原产国的16个品牌1471批次进口铁矿石化学分析样品,参考及采用GB/T 6730.62-2005《铁矿石钙、硅、镁、钛、磷、锰、铝和钡含量的测定波长色散X射线荧光光谱法》测定铁矿石中钙、镁、硅、铝、钛、磷、锰、铜的含量,采用GB/T6730.5-2007《铁矿石全铁含量的测定三氯化钛还原法》测定铁矿石中全铁的含量,采用GB/T 6730.61-2005《铁矿石碳和硫含量的测定高频燃烧红外吸收法》测定铁矿石中硫的含量。
3、缺失值处理
部分定量分析数据中Cu含量未检出,使用0进行缺失值处理。部分定量分析数据中S含量未进行检测,使用该品牌样品的平均值进行缺失值处理。
4、多变量异常数据检验
本实施例使用Pirouette多元数据分析软件基于剩余方差的F检验进行异常数据的剔除。通过与F0.01检验临界值表比对,无异常数据。
5、逐步判别分析
铁矿石品牌的判别模型中,对钙、镁、硅、铝、钛、磷、锰、铜、铁、硫10个变量经过逐步判别分析,选取元素变量时,将F-score值>3.84保留在了模型中,Mn含量被剔除,Fe、Al、Si、Cu、P、Ti、Ca、Mg、S的元素含量用于建立铁矿石品牌判别模型。
6、建立九维Fisher判别模型
本实施例选用共计1471个铁矿石样品用于建立Fisher判别模型,建模过程中选取1108个样品作为训练集,363个样品作为验证集检验模型的准确性。
采用Fe、Al、Si、Cu、P、Ti、Ca、Mg、S的共9个元素含量,建立九维Fisher判别模型和相应的组质心处的坐标。
九维Fisher判别模型的9组判别函数为:
F1=-0.141X1-2.026X2-1.667X3+37.182X4+13.058X5+29.733X6+5.865X7+45.805X8-3.605X9+5.864
F2=3.801X1+3.457X2+4.088X3-5.378X4+37.252X5-4.948X6+0.77X7+0.052X8+36.704X9-248.587
F3=0.297X1+0.024X2+4.666X3-15.711X4-85.076X5-8.798X6+0.873X7+11.986X8-16.159X9-21.18
F4=0.249X1+6.715X2+3.81X3-24.212X4+47.508X5+7.362X6-0.79X7-2.194X8+41.336X9-34.085
F5=0.396X1+0.768X2-0.304X3+108.72X4-61.619X5+33.1X6+2.343X7-25.031X8+69.08X9-20.631
F6=-0.251X1-5.098X2+1.456X3-37.041X4+60.3X5+9.783X6+8.868X7-14.049X8+79.13X9+11.807
F7=0.097X1-0.352X2+1.195X3+64.192X4+35.604X5+14.205X6+4.483X7-11.092X8-175.299X9-8.031
F8=-0.153X1+1.719X2-1.303X3-96.561X4-17.174X5-17.788X6+16.88X7+6.077X8+15.726X9+10.836
F9=-0.107X1+0.293X2+0.33X3+497.005X4+7.154X5-18.482X6+2.855X7-1.072X8+18.587X9+5.359
式中X1-X9分别代表Fe、Al、Si、Cu、P、Ti、Ca、Mg、S的含量。
九维Fisher判别模型中的各品牌组质心的坐标为:津布巴混合粉铁矿(-3.68,3.382,-4.525,7.245,-1.86,-0.289,1.153,-0.357,0.27)、皮尔巴拉混合块(-2.522,1.313,-3.494,-3.05,-0.251,0.347,-0.302,-0.029,-0.025)、皮尔巴拉混合粉(-2.201,1.313,-3.676,2.304,-0.678,0.074,0.006,0.085,0.014)、纽曼混合块铁矿(-2.783,4.496,-1.858,-2.153,-0.277,0.580,-0.161,-0.107,-0.027)、纽曼混合粉铁矿(-1.451,5.482,-0.482,2.723,-0.886,-1.609,0.5,0.245,-0.139)、杨迪粉铁矿(-3.582,-15.16,5.795,-0.649,-0.105,0.553,0.547,-0.107,0.244)、哈杨粉铁矿(-2.441,-13.014,1.734,-3.094,-0.4,-0.821,0.153,0.456,-0.091)、澳大利亚铁矿石精粉(4.172,15.551,18.347,-1.537,-3.274,-0.512,-0.813,-0.279,0.061)、国王粉铁矿(-3.351,-12.551,2.657,0.985,0.491,0.735,0.939,-1.101,-0.328)、弗特斯克混合粉(-3.772,-8.168,2.451,5.086,2.137,-0.015,-1.496,0.023,0.013)、麦克粉铁矿(-3.769,0.427,-0.181,3.75,-0.122,0.132,-0.353,-0.582,0.118)、超特粉铁矿(-4.128,-11.961,4.262,7.55,2.738,-1.127,-1.172,-0.593,-0.316)、昆巴标准粉(-3.219,10.222,5.062,1.567,2.472,0.876,0.402,1.293,0.065)、昆巴标准块(-4.581,11.283,6.096,0.143,2.412,0.437,0.903,-0.364,-0.169)、南非铁矿石精粉(151.238,-1.833,-0.92,0.417,0.406,0.174,0.082,0,-0.008)、卡拉加斯铁矿石(-0.605,8.763,-4.11,-5.676,3.935,-2.216,0.077,-0.64,0.317)。
具体判别结果如表7所示。
表7判别模型具体判别结果
Claims (10)
1.一种铁矿石的原产国的鉴别方法,其特征在于,其步骤包括:
S1.取至少3个原产国,每个原产国至少12个批次的铁矿石中元素含量的数据,依次进行缺失值处理和多变量异常数据检验后,建立Fisher判别模型;
S2.将待测样品铁矿石的元素含量代到步骤S1的Fisher判别模型中,确定待测样品铁矿石的原产国;
其中,所述步骤S1和步骤S2的元素含量单位均为质量百分含量。
2.如权利要求1所述的铁矿石的原产国的鉴别方法,其特征在于,原产国数为3~5个,批次数为12~298;
和/或,所述元素含量的检测方法为波长色散X射线荧光光谱无标样分析方法,或者为元素含量定量分析方法;所述元素含量定量分析方法较佳地为波长色散X射线荧光光谱定量分析方法、三氯化钛还原法和/或高频燃烧红外吸收法。
3.如权利要求1所述的铁矿石的原产国的鉴别方法,其特征在于,当所述元素含量因无法检出而导致缺失时,所述缺失值处理采用以下几种方式的一种:(1)用0替代缺失值;(2)用检测限替代缺失值;(3)用检测限替代缺失值,并增加一组逻辑变量,若元素含量能够检出,则标记为1,若元素含量无法检出,则标记为0;(4)删除存在缺失值的元素含量数据;
或者,当所述元素含量因未进行检测而导致缺失时,所述缺失值处理采用同一原产国的样品中所述元素含量的平均值替代。
4.如权利要求1所述的铁矿石的原产国的鉴别方法,其特征在于,所述多变量异常数据检验为库克距离判断、马哈拉诺比斯距离判断或基于剩余方差的F检验法,较佳地为基于剩余方差的F检验法;更佳地,所述多变量异常数据检验选用Pirouette多元数据分析软件的基于剩余方差的F检验进行;
和/或,所述多变量异常数据检验后,先进行逐步判别分析,后建立Fisher判别模型;较佳地,所述逐步判别分析选取的F值为3.84。
5.如权利要求1所述的铁矿石的原产国的鉴别方法,其特征在于,采用波长色散X射线荧光光谱无标样分析方法时,所述Fisher判别模型为至少四维以上的Fisher判别模型;
较佳地,所述Fisher判别模型为四维Fisher判别模型,所述四维Fisher判别模型的变量元素为Fe、O、Si、Ca、Al、Mn、Ti、Mg、P、Na、Cr、K、Sr、S、Zn、V、Cu、Ba、Ni、Mo和Pb;
其中,所述四维Fisher判别模型的4组判别函数为:
F1=-0.029X1-0.121X2-0.166X3+13.393X4+1.016X5+1.117X6-8.709X7-3.014X8-35.081X9+5.715X10-6.516X11-9.674X12+49.536X13-53.125X14+158.478X15+36.648X16+68.739X17-15.032X18-291.388X19+560.344X20-920.863X21+9.604
F2=0.266X1-0.115X2+0.036X3+6.576X4+0.652X5-2.365X6+13.54X7-7.79X8-46.172X9-11.156X10-49.525X11+31.216X12+262.112X13+2.559X14+13.094X15-23.33X16+10.458X17+27.165X18+168.438X19-2190.239X20+317.394X21-13.546
F3=0.02X1+0.843X2+0.089X3-4.128X4-0.751X5-0.438X6-9.018X7+6.14X8+37.322X9+8.864X10-81.072X11+15.107X12+135.869X13+40.742X14-25.678X15+31.234X16+8.987X17+10.862X18-161.474X19+426.269X20-90.978X21-28.975
F4=0.051X1+0.622X2+0.6X3+5.972X4-0.109X5-2.297X6-4.482X7+1.302X8-16.684X9-1.351X10+118.16X11+3.65X12+18.161X13+49.477X14-54.647X15-13.305X16-176.834X17-33.23X18+509.022X19-837.642X20+980.466X21-30.568
式中,X1-X21分别代表Ca、K、O、V、Mg、Sr、Na、Zn、Al、Ti、Ni、Pb、P、Cr、Cu、Mo、Mn、S、Ba、Fe、Si的含量;
其中,所述四维Fisher判别模型中的各原产国组质心的坐标为:澳大利亚(-1.313,-2.088,0.229,0.311)、巴西(-0.507,-0.853,-3.589,-3.449)、南非(-1.715,9.877,1.244,-0.145)、哈萨克斯坦(16.519,-1.012,3.204,-0.66)、印度(9.5,5.368,-9.678,2.778)。
6.一种铁矿石的品牌的鉴别方法,其特征在于,其步骤包括:
S1.取至少16个品牌,每个品牌至少11个批次的铁矿石中元素含量的数据,依次进行缺失值处理和多变量异常数据检验后,建立Fisher判别模型;
S2.将待测样品铁矿石的元素含量代到步骤S1的Fisher判别模型中,确定待测样品铁矿石的品牌;
其中,所述步骤S1和步骤S2的元素含量单位均为质量百分含量。
7.如权利要求6所述的铁矿石的品牌的鉴别方法,其特征在于,品牌数为16~21个,批次数为11~308;
和/或,所述元素含量的检测方法为波长色散X射线荧光光谱无标样分析方法,或者为元素含量定量分析方法;所述元素含量定量分析方法较佳地为波长色散X射线荧光光谱定量分析方法、三氯化钛还原法和/或高频燃烧红外吸收法;
和/或,当所述元素含量因无法检出而导致缺失时,所述缺失值处理采用以下几种方式的一种:(1)用0替代缺失值;(2)用检测限替代缺失值;(3)用检测限替代缺失值,并增加一组逻辑变量,若元素含量能够检出,则标记为1,若元素含量无法检出,则标记为0;(4)删除存在缺失值的元素含量数据;或者,当所述元素含量因未进行检测而导致缺失时,所述缺失值处理采用同一品牌的样品中所述元素含量的平均值替代。
8.如权利要求6所述的铁矿石的品牌的鉴别方法,其特征在于,所述多变量异常数据检验为库克距离判断、马哈拉诺比斯距离判断或基于剩余方差的F检验法,较佳地为基于剩余方差的F检验法;更佳地,所述多变量异常数据检验选用Pirouette多元数据分析软件的基于剩余方差的F检验进行;
和/或,所述多变量异常数据检验后,先进行逐步判别分析,后建立Fisher判别模型;较佳地,所述逐步判别分析选取的F值为3.84。
9.如权利要求6所述的铁矿石的品牌的鉴别方法,其特征在于,采用波长色散X射线荧光光谱无标样分析方法时,所述Fisher判别模型为至少二十维以上的Fisher判别模型;
较佳地,所述Fisher判别模型为二十维Fisher判别模型,所述二十维Fisher判别模型的变量元素为V、Ca、K、Al、O、Ti、Mn、Mo、Ni、P、Mg、Cu、Pb、Si、S、Na、Cl、Zn、Ba、Cr、Fe、Sr和Zr;
其中,所述二十维Fisher判别模型的20组判别函数为:
F1=-0.063X1-0.101X2+0.042X3-0.211X4-0.393X5+1.274X6+12.681X7+2.43X8-2.359X9+1.25X10-84.836X11+1.62X12+81.752X13-61.727X14-66.185X15-7.208X16+320.464X17+149.594X18+15.808X19+34.49X20+1341.336X21+2212.518X22-714.712X23-13.105
F2=-0.083X1+0.132X2+0.274X3-0.966X4+1.162X5-0.418X6-62.659X7-0.961X8+8.828X9-2.241X10+81.887X11+10.502X12-97.849X13+31.64X14+333.138X15+1.229X16+283.852X17-173.621X18-0.366X19-15.513X20+462.515X21+116.469X22+36.171X23-1.25
F3=0.315X1+0.334X2+0.64X3-3.535X4-0.409X5+0.758X6-
14.496X7+0.578X8+16.922X9+2.247X10-2.182X11+48.737X12+76.039X13-44.933X14+110.534X15+63.579X16-0.155X17+142.501X18+73.302X19-9.263X20+237.108X21+7905.493X22-1784.531X23-19.163
F4=-0.473X1+0.694X2-0.121X3-2.552X4-0.961X5+5.697X6+2.682X7-7.921X8+49.775X9-1.376X10+5.835X11-28.097X12-102.866X13+20.6X14+44.635X15+71.872X16+21.298X17+147.882X18-9.479X19+58.986X20+229.354X21+2013.315X22-1323.799X23+9.025
F5=0.108X1+0.175X2-0.295X3-8.833X4-0.507X5-5.243X6+4.46X7-4.335X8+89.273X9-12.712X10-36.739X11+32.377X12+58.251X13+45.737X14-59.931X15-94.447X16-5.173X17+45.387X18+71.978X19-13.194X20+365.185X21-473.43X22+402.527X23-16.388
F6=0.259X1-1.18X2-0.117X3+5.973X4+4.182X5+2.551X6-1.029X7-8.209X8+94.513X9-10.296X10-16.609X11-6.28X12-130.495X13+26.263X14-21.244X15+78.535X16+11.012X17+62.304X18+3.182X19-8.022X20+53.495X21-3081.716X22-193.861X23+20.074
F7=-0.237X1+0.885X2-0.241X3+12.495X4+0.004X5+1.33X6+17.339X7-9.261X8-107.635X9-2.546X10+33.419X11+22.89X12+138.002X13-17.655X14+5.997X15+31.913X16+3.242X17-70.375X18+19.414X19+27.802X20-3.866X21-779.248X22+147.537X23-13.975
F8=0.133X1-0.405X2+1.142X3-5.801X4+0.669X5+7.614X6+7.494X7+3.116X8-17.998X9+0.024X10+58.323X11+6.647X12-86.098X13+67.68X14+82.742X15-124.629X16-30.193X17-150.62X18-13.873X19+91.953X20+197.849X21+316.906X22+645.565X23-5.737
F9=-0.118X1+0.666X2+0.162X3-3.057X4+1.71X5-3.628X6-7.012X7+8.576X8+38.102X9+10.308X10+15.782X11+13.503X12-18.877X13+6.272X14-52.801X15-50.711X16+25.108X17-44.043X18+4.047X19-22.067X20-187.925X21+1087.285X22+451.136X23-22.626
F10=0.17X1-0.023X2-0.902X3-1.778X4-1.257X5+9.724X6-2.135X7-2.973X8+77.872X9+9.571X10-156.977X11+6.929X12+27.111X13+38.703X14-118X15+23.827X16+23.706X17+120.225X18+12.035X19-13.022X20-318.435X21-1597.18X22-778.946X23+0.774
F11=0.262X1+0.255X2+0.656X3+4.495X4-1.675X5+2.201X6-7.671X7-1.168X8+73.007X9-1.284X10+36.638X11-4.172X12+129.771X13+186.729X14-160.162X15-44.892X16+1.742X17-144.907X18-30.262X19-41.62X20+315.988X21-4500.335X22+589.115X23-21.928
F12=0.213X1-0.188X2+1.096X3-0.356X4+0.284X5-3.161X6+0.565X7-3.883X8-26.717X9-1.096X10-160.913X11-13.164X12+380.179X13+52.561X14+76.201X15+134.173X16+11.243X17+69.368X18-34.754X19+3.668X20-142.142X21-4005.181X22-664.484X23+3.441
F13=-0.129X1-0.284X2-0.881X3-0.178X4-0.852X5-6.2X6+1.138X7+0.005X8+51.045X9+0.335X10+21.467X11-0.343X12-1.323X13+32.949X14-71.372X15+11.172X16+3.683X17-76.715X18+2.016X19+180.351X20-156.647X21-1148.028X22+264.453X23+19.223
F14=0.172X1+0.247X2-0.096X3+2.279X4+0.763X5+2.537X6-16.731X7+5.567X8-33.832X9-5.58X10-68.753X11-4.472X12+244.379X13-109.24X14-166.04X15-162.059X16+10.271X17+125.185X18-19.609X19+98.565X20+107.302X21+3090.702X22+1386.216X23-31.034
F15=0.297X1+0.42X2-0.584X3+1.712X4+0.514X5-1.647X6-4.37X7-5.255X8-22.224X9+13.195X10+39.976X11+12.38X12-17.661X13-7.259X14+445.893X15+56.371X16-21.351X17-14.384X18-73.177X19+5.709X20+691.479X21-1317.43X22-101.087X23-29.894
F16=0.222X1+0.119X2+0.737X3+1.946X4-0.366X5-0.744X6-8.885X7-4.436X8+15.704X9+6.564X10-127.562X11-11.813X12-60.213X13-26.78X14+450.072X15-121.086X16+18.853X17+207.622X18+90.621X19+39.786X20+77.822X21-396.644X22+700.916X23-24.072
F17=-0.186X1-0.084X2-0.586X3-0.984X4+0.155X5-1.49X6+10.751X7-3.176X8-30.355X9+10.455X10+139.124X11-1.368X12+152.237X13+75.898X14-96.072X15-201.967X16+5.898X17+62.329X18-18.722X19-38.364X20-271.459X21-1101.622X22+1313.381X23+9.716
F18=0.51X1+0.11X2-0.258X3+0.442X4+1.146X5-1.288X6+13.381X7+2.629X8-94.323X9+0.317X10+42.203X11-10.375X12+49.959X13+72.684X14+132.622X15-3.616X16-23.371X17-219.481X18+33.73X19-22.302X20-163.399X21+1162.658X22-97.907X23-34.317
F19=0.609X1+0.608X2+1.058X3+1.24X4-0.33X5-0.605X6+17.483X7-6.498X8+22.124X9+6.305X10+99.083X11+2.021X12-93.031X13-58.243X14-181.554X15+2.958X16+8.375X17+77.766X18-7.43X19+33.915X20-279.637X21+852.295X22-251.507X23-57.948
F20=0.378X1+0.205X2+0.207X3-0.628X4+0.837X5-0.622X6+6.064X7+0.222X8-52.396X9+8.217X10+13.307X11-15.451X12+150.942X13+24.719X14-308.208X15+68.887X16-20.844X17-40.765X18+69.994X19+8.296X20+137.474X21-597.944X22-534.342X23-27.342
式中,X1-X23分别代表V、Ca、K、Al、O、Ti、Mn、Mo、Ni、P、Mg、Cu、Pb、Si、S、Na、Cl、Zn、Ba、Cr、Fe、Sr、Zr的含量;
其中,所述二十维Fisher判别模型中的各品牌组质心的坐标为:津布巴混合粉铁矿(-13.74,4.041,-4.937,2.913,2.048,7.485,-1.912,0.948,4.704,-1.615,-0.169,1.088,-0.779,-0.669,0.785,0.819,0.275,0.323,-0.03,0.005)、巴西铁矿石精粉(-12.064,2.005,-0.822,-2.521,-0.81,3.068,-4.306,1.365,-4.193,-1.141,-0.824,0.535,0.078,-0.734,-1.457,-0.318,0.396,0.378,0.053,-0.009)、皮尔巴拉混合块(-12.949,1.908,-3.727,1.266,0.791,-0.457,-1.373,-1.672,-0.851,1.372,1.062,-0.543,0.267,0.257,0.176,-0.027,-0.155,0.224,-0.103,-0.033)、纽曼混合块铁矿(-12.899,2.385,-2.854,0.572,1.044,0.35,-2.78,-0.914,-0.663,0.424,0.945,0.488,0.199,0.13,-0.416,0.491,-0.376,-0.142,0.083,0.016)、国王粉铁矿(-12.533,0.497,-4.675,3.654,-0.135,-5.287,1.685,-2.999,-0.898,-1.014,-0.534,0.829,-0.079,-1.907,0.463,-0.566,-0.796,-0.004,-0.029,0.01)、皮尔巴拉混合粉(-12.984,2.086,-4.459,2.448,1.324,2.452,-1.199,-0.704,1.796,0.022,0.33,-0.509,0.23,-0.151,0.196,-0.364,0.229,-0.081,0.131,-0.004)、澳大利亚球团矿(165.394,79.118,-3.584,2.763,3.639,-0.23,-0.996,0.966,-1.315,-0.745,-0.003,-0.147,-0.031,-0.002,0.109,0.004,-0.011,-0.019,-0.004,-0.000009175)、杨迪粉铁矿(-11.702,1.767,-5.162,3.143,-0.382,-7.174,2.306,-2.686,-0.077,-1.819,-0.282,0.196,-0.562,0.073,-0.229,0.273,0.367,-0.153,-0.003,-0.046)、哈杨粉铁矿(-11.906,1.278,-4.557,1.875,0.001,-5.839,1.09,-3.249,-1.037,-0.702,-0.787,-0.477,0.063,0.687,0.212,0.056,0.149,0.237,0.038,0.072)、纽曼混合粉铁矿(-12.761,3.012,-4.069,0.77,0.271,3.789,-1.739,0.525,3.576,-2.575,-1.626,-0.254,-0.316,0.956,-0.405,-0.297,-0.567,-0.041,-0.076,-0.01)、南非铁矿石精粉(85.285,-76.585,-2.265,-1.14,0.265,0.017,-0.023,-0.033,0.331,-0.05,0.069,0.018,-0.003,0.001,-0.022,0.002,-0.002,0.007,0.001,0.00003174)、澳大利亚铁矿石精粉(-10.88,2.65,8.809,-12.993,-3.43,-6.47,-6.007,3.98,0.477,-0.725,-0.065,-0.371,-0.019,-0.161,0.375,0.042,0.034,-0.049,-0.01,0.00006576)、弗特斯克混合粉(-11.728,0.615,-4.602,6.385,-2.414,-0.8,3.188,5.057,-0.562,1.769,0.486,-0.207,-0.953,0.041,-0.119,-0.101,-0.062,0.006,0.008,0.011)、卡拉加斯铁矿石(-10.983,-2.358,-1.665,1.208,0.649,4.443,-3.304,-1.598,-3.57,4.173,-2.215,-0.002,-0.195,-0.053,0.376,0.146,0.168,-0.224,-0.07,-0.001)、哈萨克斯坦球团矿(29.034、22.503、2.421、-11.711、-15.447、1.488、4.282、-4.022、4.645、3.528、-0.124、0.541、0.163、-0.055、-0.385、-0.026、0.03、0.06、0.014、0.001)、哈萨克斯坦粉铁矿(4.705,-2.123,53.032,16.516,-3.587,1.059,-0.883,-1.324,-0.045,-0.735,0.134,-0.041,0.018,0.052,0.01,-0.002,0.006,0.006,-0.002,0.00009545)、昆巴标准粉(-11.268,3.332,13.031,-10.701,9.927,0.94,6.159,0.211,1.066,0.745,-0.412,-1.013,0.064,-0.521,-0.35,0.275,-0.08,0.011,-0.005,0.002)、超特粉铁矿(-11.207,-0.385,-5.802,7.727,-2.864,-1.666,5.102,5.92,-0.283,-0.366,-1.314,0.522,2.21,0.14,0.26,0.319,-0.043,0.044,0.037,-0.023)、麦克粉铁矿(-13.259,2.805,-4.929,3.418,1.387,0.621,-0.363,0.584,2.171,-0.77,1.172,0.316,0.755,-0.371,-0.464,-0.221,0.531,-0.265,-0.318,0.051)、昆巴标准块(-11.037,3.029,8.749,-8.766,6.34,0.21,2.456,0.413,-1.23,0.429,0.471,2.019,-0.118,0.911,0.365,-0.505,0.103,0.008,0.018,-0.005)、印度球团矿(-12.274,-0.026,-1.88,-8.696,-8.259,10.016,4.745,-0.796,-5.937,-4.612,0.798,-0.632,-0.103,0.037,0.474,0.104,-0.027,0.121,-0.022,0.005)。
10.如权利要求6所述的铁矿石的品牌的鉴别方法,其特征在于,采用元素含量定量分析方法时,所述Fisher判别模型为至少九维以上的Fisher判别模型;
较佳地,所述Fisher判别模型为九维Fisher判别模型,所述九维Fisher判别模型的变量元素为Fe、Al、Si、Cu、P、Ti、Ca、Mg和S;
其中,所述九维Fisher判别模型的9组判别函数为:
F1=-0.141X1-2.026X2-1.667X3+37.182X4+13.058X5+29.733X6+5.865X7+45.805X8-3.605X9+5.864
F2=3.801X1+3.457X2+4.088X3-5.378X4+37.252X5-4.948X6+0.77X7+0.052X8+36.704X9-248.587
F3=0.297X1+0.024X2+4.666X3-15.711X4-85.076X5-8.798X6+0.873X7+11.986X8-16.159X9-21.18
F4=0.249X1+6.715X2+3.81X3-24.212X4+47.508X5+7.362X6-0.79X7-2.194X8+41.336X9-34.085
F5=0.396X1+0.768X2-0.304X3+108.72X4-61.619X5+33.1X6+2.343X7-25.031X8+69.08X9-20.631
F6=-0.251X1-5.098X2+1.456X3-37.041X4+60.3X5+9.783X6+8.868X7-14.049X8+79.13X9+11.807
F7=0.097X1-0.352X2+1.195X3+64.192X4+35.604X5+14.205X6+4.483X7-11.092X8-175.299X9-8.031
F8=-0.153X1+1.719X2-1.303X3-96.561X4-17.174X5-17.788X6+16.88X7+6.077X8+15.726X9+10.836
F9=-0.107X1+0.293X2+0.33X3+497.005X4+7.154X5-18.482X6+2.855X7-1.072X8+18.587X9+5.359
式中,X1-X9分别代表Fe、Al、Si、Cu、P、Ti、Ca、Mg、S的含量;
所述九维Fisher判别模型中的各品牌组质心的坐标为:津布巴混合粉铁矿(-3.68,3.382,-4.525,7.245,-1.86,-0.289,1.153,-0.357,0.27)、皮尔巴拉混合块(-2.522,1.313,-3.494,-3.05,-0.251,0.347,-0.302,-0.029,-0.025)、皮尔巴拉混合粉(-2.201,1.313,-3.676,2.304,-0.678,0.074,0.006,0.085,0.014)、纽曼混合块铁矿(-2.783,4.496,-1.858,-2.153,-0.277,0.580,-0.161,-0.107,-0.027)、纽曼混合粉铁矿(-1.451,5.482,-0.482,2.723,-0.886,-1.609,0.5,0.245,-0.139)、杨迪粉铁矿(-3.582,-15.16,5.795,-0.649,-0.105,0.553,0.547,-0.107,0.244)、哈杨粉铁矿(-2.441,-13.014,1.734,-3.094,-0.4,-0.821,0.153,0.456,-0.091)、澳大利亚铁矿石精粉(4.172,15.551,18.347,-1.537,-3.274,-0.512,-0.813,-0.279,0.061)、国王粉铁矿(-3.351,-12.551,2.657,0.985,0.491,0.735,0.939,-1.101,-0.328)、弗特斯克混合粉(-3.772,-8.168,2.451,5.086,2.137,-0.015,-1.496,0.023,0.013)、麦克粉铁矿(-3.769,0.427,-0.181,3.75,-0.122,0.132,-0.353,-0.582,0.118)、超特粉铁矿(-4.128,-11.961,4.262,7.55,2.738,-1.127,-1.172,-0.593,-0.316)、昆巴标准粉(-3.219,10.222,5.062,1.567,2.472,0.876,0.402,1.293,0.065)、昆巴标准块(-4.581,11.283,6.096,0.143,2.412,0.437,0.903,-0.364,-0.169)、南非铁矿石精粉(151.238,-1.833,-0.92,0.417,0.406,0.174,0.082,0,-0.008)、卡拉加斯铁矿石(-0.605,8.763,-4.11,-5.676,3.935,-2.216,0.077,-0.64,0.317)。
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