高爐爐況的重要參數(shù)

一、問題的重述高爐煉鐵是現(xiàn)代鋼鐵生產(chǎn)的重要環(huán)節(jié)，且是個(gè)復(fù)雜的高溫物理化學(xué)過程，精確掌握爐內(nèi)的溫度分布上不可能，所以一般要通過預(yù)報(bào)高爐爐溫（鐵水硅含量）來間接地反映爐內(nèi)的溫度變化，判斷高爐爐缸熱狀態(tài)，并以此來調(diào)控高爐行程、能量消耗及生鐵質(zhì)量。事實(shí)上，影響鐵水硅含量（即爐溫）的因素很多，大體上分為兩大類：狀態(tài)參數(shù)和控制參數(shù)。狀態(tài)參數(shù)包括料速、透氣性指數(shù)、風(fēng)口狀況、鐵水與爐渣成分等；控制參數(shù)包括入爐原料的性質(zhì)（成分、比重、配料比等）、裝料方式、風(fēng)量、風(fēng)溫、富氧量等，各個(gè)因素之間也存在交互影響。其中幾個(gè)重要的影響參數(shù)為：（1） 料速是判斷高爐爐況的一個(gè)重要參數(shù)；（2） 透氣性指數(shù)是判斷爐溫與爐況順行的一個(gè)重要參數(shù)；（3） 鐵量差指的是理論出鐵量與實(shí)際出鐵量之差；（4） 風(fēng)溫對高爐冶煉過程的影響，主要是直接影響到爐缸溫度，并間接的影響高爐高度方向上溫度分布的變化，以及影響到爐頂溫度水平；（5） 風(fēng)量引起的爐料下降速度和初渣中FeO的含量的增減，以及煤氣流分布的變化，都會影響到煤氣能的利用程度和爐況順行情況?，F(xiàn)在要求我們根據(jù)表中給出的近期某高爐的生產(chǎn)數(shù)據(jù)，試建立鐵水硅含量與各影響參數(shù)的數(shù)學(xué)預(yù)測模型。二、問題的分析高爐鐵水硅含量的高低反映了高爐冶煉過程的熱狀態(tài)及燃燒比。維持穩(wěn)定且較低的鐵水硅含量是爐況穩(wěn)定并產(chǎn)生較低燃燒比的直接保證。對于本問題中鐵水硅含量的預(yù)報(bào)有很多方法，如傳統(tǒng)的ARMA模型，但是由于高爐生產(chǎn)過程的復(fù)雜性，尤其在不斷提高噴煤量之后，爐況的波動(dòng)更加劇烈和復(fù)雜，采用ARMA模型已經(jīng)很難準(zhǔn)確的描述鐵水硅含量的預(yù)測模型。然而最近提出的神經(jīng)網(wǎng)絡(luò)模型能夠以實(shí)驗(yàn)數(shù)據(jù)為基礎(chǔ)，經(jīng)過有限次迭代，就可以獲得一個(gè)反映實(shí)驗(yàn)數(shù)據(jù)內(nèi)在規(guī)律性的參數(shù)組，尤其是對于參數(shù)眾多的，規(guī)律性不明顯的生產(chǎn)過程能發(fā)揮其獨(dú)特性，此方法正好解決本文中參數(shù)眾多且無規(guī)律的問題，所以本文采用神經(jīng)網(wǎng)絡(luò)的方法對鐵水硅含量進(jìn)行預(yù)報(bào)。為了使得我們建立的BP神經(jīng)網(wǎng)絡(luò)模型更具有說服力，同時(shí)建立了一個(gè)多元線性回歸模型與之進(jìn)行對比。三、模型的假設(shè)和符號說明（一） 模型假設(shè)1、 鐵硅量與原料混合時(shí)間有關(guān)，與起始時(shí)間無關(guān)；2、 用料全部都倒進(jìn)高爐內(nèi)，在反應(yīng)開始前無殘留；3、 原始各變量相互不獨(dú)立，具有相關(guān)性。（二） 符號說明%：第i個(gè)主成分，第j個(gè)變量的權(quán)數(shù)P0:為回歸常數(shù)房：多元線性回歸系數(shù)(i=1,2,-p)X〔j:第i個(gè)主成分的第j個(gè)變量值F:第i個(gè)主成分四、模型的建立及求解(一)模型一：多元線性回歸模型模型一的建立多元線性回歸模型的一般形式y(tǒng)—P+gx+gx+?,?+&x+80 11 22 pp式中，P0為回歸常數(shù)，七(i=1,2,—p)稱為回歸系數(shù)，y稱為被解釋變量，TOC\o"1-5"\h\z即因變量；而x，x，…，x是p個(gè)可以精確測量并可控制的一般變量，稱為解釋1 2 p變量，即自變量。對于一個(gè)實(shí)際問題，如果我們獲得n組觀測數(shù)據(jù)(x，x，…，x；y)i1i2 ipi(i=1,2，…，n)則線性回歸模型可表示為y—P+Px+Px+,,,+Px+810111212 p1p1y=P+Px+Px+ Px+8 —1、V2 0 121 222 p2p 2 (1.1)y=P+Px+Px+,,,+Px+8【n0 1n1 2n2 pnpn由于大量的參數(shù)變量間并非相互獨(dú)立，各個(gè)因素之間也存在交互影響，因此我們采用主成分分析法，把各變量之間互相關(guān)聯(lián)的復(fù)雜關(guān)系進(jìn)行簡化分析。建立主成分函數(shù)TOC\o"1-5"\h\zFi=224ax (L2)j—1最后將F看做一個(gè)新的變量，建立多元回歸分析模型iY=P+PF+PF+…+PF (1.3)0 11 22 mm模型一的求解根據(jù)上面原理，利用SPSS軟件進(jìn)行主成分分析求解，結(jié)果如表1。表1 主成分矩陣ComponentMhtrix1―2—3456―7 8Ls.828-.095.151.057-.241-.098.033.004S-.811.094-.202-.220.289-.053-.077.022Ti.789-.195.416.024-.045-.072-.098.125R.772.141-.530.210-.001.148-.091-.067RR.745.150-.533.245-.011.186-.081-.082Sz.680.096-.521-.128.250-.077-.024.103CaO.658.105-.424.371-.263.011.254.015鐵水溫度.555-.173.369-.108.206.223-.162.018F.516-.105-.090-.372.078-.486-.051.190K2O.161.929.305-.084.047.023.051-.047FeO.163.927.307-.073.045.023.049-.044Na2O.166.926.304-.094.033.009.037-.061Si.563-.243.596-.053.252.061-.136.187P.416-.296.559-.018.116.274.071.140實(shí)際產(chǎn)量-.047.031.177.831.269-.329-.069-.053水渣量.065.092.197.761.000-.339.034.111鐵量差.107.075-.067-.544-.412鐵口工作深Ew-.216-.038.013.196.502.371.331.203混合時(shí)間-.246-.010-.147.368.156.436.232-.082Mn.327-.161.232.203-.277.376.192-.208SiO2-.364-.096.300.208-.418-.257.559.150Al2O3.413.045-.275-.223.373-.280.484.157MgO.295-.217.075-.335.345-.067.475-.283鐵口工作泥菱1 -215234-192174-038226-061763ExtractionMethod:PrincipalComponentAnalysis.a-8componentsextracted.根據(jù)表1結(jié)果，得到主成分F的表達(dá)式。iF=0.828X-0.811X+0.789X+0.772X+0.745X+0.680X+0.658X+0.555X+0.516X+0.161X+0.163X+0.166X+0.563X+0.416X-0.047X+0.065X+0.107X-0.216X-0.246X+0.327X-0.364X+0.413X+0.295X-215X117 118 119 120 121 122 123 124F=-0.095X+0.094X-0.195X+0.141X+0.150X+0.096X+0.105X-0.173X-0.105X +0.929X +0.927X +0.926X -0.243X -0.296X +0.031X +0.092X+0.075X -0.038X -0.010X -0.161X -0.096X +0.045X -0.217X +0.234XF=0.151X-0.202段+0.416X-0.530jT-0.533X221-0.521X22-0.424X號???-0.192X3 31 32 33 34 35 36 37 324Eg=0.004X81+0.022X82+…+0.763X8?耳（1.4）同時(shí)將求得的F值多元回歸分析，結(jié)果如表2：i故擬合多元線性回歸方程Y=63.183+0.003F+0.070F-0.058F-0.005F-0.025F+0.011F-0.041F1 2 3 5 6 7 8

(1.5)表2 回歸系數(shù)CoefficientsaModelUnstandardized Coefficients Standardized_Coefficients tSig.BStd.ErrorBeta1 (Constant)63.1837.5808.335.000Y1.003.008.155.389.698Y2.070.0491.1421.430.155Y3-.058.011-3.630-5.206.000Y5-.005.004-.519-1.244.215Y6-.025.011-2.858-2.261.025Y7.011.015.374.755.451Y8-.041.013-1.574-3.241.001a.DependentVariable:爐溫指數(shù)模型一的檢驗(yàn)要看回歸效果如何，對回歸方程進(jìn)行顯著性檢驗(yàn)，即看自變量F,Fl,Fm從整體上對隨機(jī)變量j是否有顯著的影響。為此提出原假設(shè)H:。=。=。=0 (1.6)如果H被接受，則表示隨機(jī)變量j與F,F，…，F(xiàn)之間的關(guān)系由線性回歸模型0 1 2表示不合適。為了建立對H0進(jìn)行檢驗(yàn)的F統(tǒng)計(jì)量，利用總離差平方和的分解式=X(J-J)2+X(j—j)2=X(J-J)2+X(j—j)2i iii=1 i-1(1.7)(1.8)(1.9)i=1簡寫為SST=SSR+SSE構(gòu)造F檢驗(yàn)統(tǒng)計(jì)量如下SSR/mSSE/(n-m-1)在正態(tài)假設(shè)下，當(dāng)原假設(shè)H0:P1=P2=...P以=0成立時(shí)，F(xiàn)服從自由度為(m,n-m-1)的F分布。于是，可以利用F統(tǒng)計(jì)量對回歸方程的總體顯著性進(jìn)行檢驗(yàn)。給定的顯著性水平a(取a=0.05),查F分布表，得臨界值q(m,n-m-1).當(dāng)F>Fa(m,n-m-1)時(shí)，拒絕原假設(shè)H0，認(rèn)為在顯著性水平a下，j對

F,F…F有顯著的線性；反之，當(dāng)F<F(m,n-m-1),則認(rèn)為回歸方程不顯著。1 2m a表3 方差分析表方差來源自由度平方和均方F值尸值回歸mSSRSSR/mSSR/mP(F>F值)=P值殘差n—m—1SSESSE/(n—m—1)總和n—1SSTSSE/(n—m—1)表4ANOVAbSumofdfFSin1 Regressionoquaies10.7237IV，eanK_/quare1.53217.382Dig..000aResidualTotal13.308 24031 151 158—.088Predictors:(Constant),Y8,Y2,Y1,Y7,Y5,Y3,Y6DependentVariable:爐溫指數(shù)Sig為顯著性水平檢驗(yàn)，Sig<0.05表示變量回歸性顯著。由表4可看出變量通過了顯著性水平檢驗(yàn)，但表2卻反映，雖然變量通過了顯著性水平檢驗(yàn)，但某些變量，即F1、F2、F5、F7顯著性水平較弱，(F1、F2、F5、F7分別對應(yīng)表五中的Y1、Y2、Y5、Y7)。將預(yù)測值和實(shí)際值匯在散點(diǎn)圖上可直觀的反映擬合效果，散點(diǎn)構(gòu)成的直線基本傾斜向右上方，但離散程度過大，故方程的擬合效果不盡理想。此方法不適合用于此類的預(yù)測問題。2.70TOC\o"1-5"\h\z00Y1.80001.50 2.00 1.50 2.00 2.50 3.00 3.50爐溫指數(shù)爐溫指數(shù)散點(diǎn)圖（二）模型二：BP神經(jīng)網(wǎng)絡(luò)模型模型結(jié)構(gòu)的確定BP神經(jīng)網(wǎng)絡(luò)模型可以擬合任意一個(gè)非線性映射，由輸入層、隱藏層和輸出層三部分組成。其基本的結(jié)構(gòu)如圖2所示。圖2BP神經(jīng)網(wǎng)絡(luò)層次圖（1） 輸入層的確定神經(jīng)網(wǎng)絡(luò)的輸入層起緩沖存儲器的作用，其向量的數(shù)目相當(dāng)于所研究問題的獨(dú)立變量數(shù)目。為了有效地進(jìn)行鐵水硅含量的預(yù)報(bào)，本模型結(jié)合題目本身所給的數(shù)據(jù)，同時(shí)考慮到不同參數(shù)對鐵水硅含量的時(shí)間滯后性，對其作了精心的篩選，共選擇9個(gè)參數(shù)作為網(wǎng)絡(luò)模型的輸入結(jié)點(diǎn)，如表5所示。（2） 隱含層的確定隱含層神經(jīng)元代表網(wǎng)絡(luò)輸入與輸出之間的非線性程度，對模型的訓(xùn)練速度和預(yù)報(bào)能力有著重要影響，神經(jīng)元數(shù)太少影響網(wǎng)絡(luò)在輸入層提取有價(jià)值的特征，網(wǎng)絡(luò)可能訓(xùn)練不出來或網(wǎng)絡(luò)不“強(qiáng)壯”，容錯(cuò)性差。但神經(jīng)元個(gè)數(shù)太多又使學(xué)習(xí)時(shí)間過長，誤差也不一定最佳。所以沒有統(tǒng)一的理論依據(jù)，本文中我們根據(jù)Kolmogorov定理，確定隱含層神經(jīng)元個(gè)數(shù)為19。（3） 輸出層的確定輸出層神經(jīng)元的個(gè)數(shù)取決于系統(tǒng)對網(wǎng)絡(luò)功能的要求，本模型要實(shí)現(xiàn)鐵水硅含量的預(yù)測，故輸出變量為鐵水硅含量，即輸出層神經(jīng)元的個(gè)數(shù)為1。因此在本文中，我們建立的神經(jīng)網(wǎng)絡(luò)模型結(jié)構(gòu)為：9-19-1。樣本集的確定為完成對目標(biāo)函數(shù)的逼近，在網(wǎng)絡(luò)的構(gòu)建訓(xùn)練和檢測及結(jié)果評價(jià)的整個(gè)過程，首先要為網(wǎng)絡(luò)提供一組適當(dāng)數(shù)量的可靠樣本。本文中給出了160組數(shù)據(jù)，由于數(shù)據(jù)太多，我們選取了部分?jǐn)?shù)據(jù)，如表4所示。選定其中的1~100組作為訓(xùn)練樣本，101?160組作為測試樣本。數(shù)據(jù)處理數(shù)據(jù)處理的目的是為神經(jīng)網(wǎng)絡(luò)的推理提供較為準(zhǔn)確的參數(shù)。一般方法包括：時(shí)序化處理、歸一化處理。序號RRRCaO渣中SSSi鐵水溫度LsFeO10.9981.02235.5810.0430.52151223.256020.9981.02235.6210.0360.62151527.778030.9841.00834.8510.0320.62151631.25041.0361.05936.881.020.0390.61152126.154051.0311.05536.581.020.0260.92152039.231061.0331.05736.671.020.0410.56151324.878071.0351.05936.591.020.0280.94152236.429081.0361.06135.8410.0310.81152232.258091.0411.06436.781.020.0290.75152335.1720.55101.0251.0536.641.020.030.581515340111.0381.06335.7210.0340.53151129.4120121.0441.06836.021.010.0340.56151129.7060131.0211.04536.60.990.0270.66151036.6670141.0271.05136.240.990.0370.65151626.7570151.0291.05336.140.980.0430.56151022.7910表5 鐵水硅含量影響因子數(shù)據(jù)表（1） 時(shí)序化處理：由于給出的數(shù)據(jù)中只有每鐵次的值，因此需要將鐵次的值轉(zhuǎn)化成對應(yīng)小時(shí)或序號的值，作為樣本的中的輸入?yún)?shù)。（2） 歸一化處理為避免由于輸入變量單位不同、絕對值相差很大對神經(jīng)網(wǎng)絡(luò)模型的影響，需要對輸入輸出參數(shù)進(jìn)行歸一化處理。本模型的BP網(wǎng)絡(luò)采用Sigmoid函數(shù)作為激發(fā)函數(shù)，即各節(jié)點(diǎn)的輸入輸出值應(yīng)在［0,1］之間。因此，要對每一參數(shù)進(jìn)行相應(yīng)的轉(zhuǎn)換，在不失其變化規(guī)律的前提下，把參數(shù)值都轉(zhuǎn)換到［0,1］上。對于輸入層的參數(shù)值采用如下式的線性轉(zhuǎn)換方式。（.）二xact（P,）-xmin（i）x0-9PP—xmax（i）x1.1-Xmin（i）x0.9 （2.1）式中X（p,.）——樣本p中參數(shù)i的樣本值；xact（p,i）——樣本P中參數(shù)i的實(shí)際值；Xmin（i）——樣本集中參數(shù)i的最小值；xmax（i）——樣本集中參數(shù)i的最大值。按上述方法得到的歸一化數(shù)據(jù)如表6。（僅為每個(gè)樣本輸出層的數(shù)據(jù)，完整數(shù)據(jù)見附件）

表6 處理后輸出層數(shù)據(jù)表序號數(shù)據(jù)序號數(shù)據(jù)序號數(shù)據(jù)序號數(shù)據(jù)序號數(shù)據(jù)序號數(shù)據(jù)10.310.610.910.1210.1510.20.320.620.920.1220.1520.30.330.630.930.1230.1530.40.41247340.640.940.1240.1540.50.350.650.950.1250.1550.60.360.660.960.1260.1560.70.370.670.970.1270.1570.2246280.380.680.980.1280.1580.90.390.690.990.1290.1590.100.400.700.50041000.1300.110.410.710.1010.1310.120.420.720.1020.1320.130.430.32454730.1030.1330.140.440.740.600321040.1340.150.450.60032750.512391050.1350.160.460.51239760.1060.1360.170.470.770.1070.1370.180.480.780.1080.1380.60032190.490.790.1090.1390.200.500.800.1100.1400.210.510.810.1110.1410.220.520.820.1120.1420.230.530.830.1130.1430.240.540.840.1140.1440.250.70024550.850.1150.512391450.260.21263560.860.1160.1460.270.570.870.1170.1470.280.580.880.1180.1480.290.590.890.1190.1490.22462300.600.900.1200.1500.網(wǎng)絡(luò)學(xué)習(xí)學(xué)習(xí)參數(shù)的確定學(xué)習(xí)速率門和沖量系數(shù)a是兩個(gè)學(xué)習(xí)時(shí)可供選擇的參數(shù)，二者大小的選取直接影響網(wǎng)絡(luò)的收斂穩(wěn)定性和學(xué)習(xí)效率，合理選擇n和a，可避免或減少系統(tǒng)誤差

的振蕩。經(jīng)過多次訓(xùn)練，我們選取=0.14。BP網(wǎng)絡(luò)學(xué)習(xí)的步驟：BP網(wǎng)絡(luò)學(xué)習(xí)的目的就是要獲得最終的權(quán)值矩陣。歸一化后的數(shù)據(jù)即可作為可靠性樣本進(jìn)行訓(xùn)練，本文中使用train函數(shù)進(jìn)行訓(xùn)練，經(jīng)過數(shù)次訓(xùn)練后得出訓(xùn)練圖3100,KcaBDLaftueuBD-OH.n.aT10-30Performanceis0.0018014,Goalis0.00210-1I100,KcaBDLaftueuBD-OH.n.aT10-30Performanceis0.0018014,Goalis0.00210-1I。-25 1015Epochs15StopTraining圖3訓(xùn)練圖從圖3中可知經(jīng)過15步訓(xùn)練就達(dá)到了性能指標(biāo)要求。最終爐溫指數(shù)擬合如圖4圖4 爐溫指數(shù)擬合圖圖4 爐溫指數(shù)擬合圖0 10 20 30 40 50 60圖4中的誤差情況如圖50 10 20 30 40 50 600.080.060.040.020-0.02-0.04-0.06-0.08-0.1-0.12StnpTraining.圖5誤差情況圖由圖5中的數(shù)據(jù)可以看出，模型二的誤差值最大時(shí)也僅僅為0.1,其余誤差大多集中在0附近，因此模型二作為一個(gè)預(yù)測模型有比較準(zhǔn)確的命中率。五、模型的評價(jià)與改進(jìn)模型評價(jià)多元線性回歸模型在選取變量時(shí)先進(jìn)行了主成分分析，保證七、烏、、F8這8個(gè)變量相互獨(dú)立，滿足多元線性回歸的要求?？啥嘣€性回歸模型最終結(jié)果擬合度不高，對該問題的預(yù)測效果不佳。本文其后運(yùn)用了BP神經(jīng)網(wǎng)絡(luò)模型進(jìn)行預(yù)測，BP神經(jīng)網(wǎng)絡(luò)具有模擬多變量而不需要對輸入變量做復(fù)雜的相關(guān)假定的能力。它不依靠專家經(jīng)驗(yàn)，只利用觀察到的數(shù)據(jù)，可以從訓(xùn)練過程中通過學(xué)習(xí)來抽取和逼近隱含的輸入/輸出非線性關(guān)系。符合本題對模型的要求。從模型的預(yù)測結(jié)果也可以看出本模型具有很高的命中率。模型改進(jìn)模型一線性擬合效果不佳，可利用最小二乘法進(jìn)行曲線方程擬合，對各種曲線模型的擬合度進(jìn)行比較，選擇擬合效果最好的模型。本文中采用的BP神經(jīng)網(wǎng)絡(luò)模型可以用RBF網(wǎng)絡(luò)來代替用來對模型的改進(jìn)。RBP網(wǎng)絡(luò)與BP網(wǎng)絡(luò)相比最大的不同在于，隱層的裝換函數(shù)是局部響應(yīng)的高斯函數(shù)，徑向基網(wǎng)絡(luò)所需要的訓(xùn)練時(shí)間比BP網(wǎng)絡(luò)要少。（3）模型二也可用BP神經(jīng)網(wǎng)絡(luò)模型與時(shí)差方法相結(jié)合的方法對本模型的改進(jìn)。參考文獻(xiàn)薛薇，spss統(tǒng)計(jì)分析方法及應(yīng)用（第二版），電子工業(yè)出版社，2009年；何曉群，多元統(tǒng)計(jì)分析（第二版），中國人民大學(xué)出版社，2008年；何曉群，應(yīng)用回歸分析（第二版），中國人民大學(xué)出版社，2007年；馬莉，MATLAB數(shù)學(xué)實(shí)驗(yàn)與建模，清華大學(xué)出版社，2010年；/p-.html/p-.html/view/9d127ddb35eefd3419.html/viewthread-5767.html附錄一：數(shù)據(jù)歸一化處理程序代碼functionY=guiyihua(y,m,n)%y為要處理的數(shù)據(jù)矩陣%m為y矩陣行數(shù)%n為y矩陣列數(shù)%Y為返回處理后的矩陣Y=zeros(m,n);forj=1:nmin=10000;max=-10000;fori=1:mify(i,j)<minmin=y(i,j);%求列最小值endify(i,j)>maxmax=y(i,j); %求列最大值endendfork=1:mY(k,j)=(y(k,j)-min*0.9)/(max*1.1-min*0.9);%歸一化函數(shù)endend附件二：主成分相關(guān)公式ComponentMaflixuomponenrC—57Ls.828-.095.151.057-.241-.098.033.004S-.811.094-.202-.220.289-.053-.077.022Ti.789-.195.416.024-.045-.072-.098.125R.772.141-.530.210-.001.148-.091-.067RR.745.150-.533.245-.011.186-.081-.082Sz.680.096-.521-.128.250-.077-.024.103CaO.658.105-.424.371-.263.011.254.015鐵水溫度.555-.173.369-.108.206.223-.162.018F.516-.105-.090-.372.078-.486-.051.190K2O.161.929.305-.084.047.023.051-.047FeO.163.927.307-.073.045.023.049-.044Na2O.166.926.304-.094.033.009.037-.061Si.563-.243.596-.053.252.061-.136.187P.416-.296.559-.018.116.274.071.140實(shí)際產(chǎn)量-.047.031.177.831.269-.329-.069-.053水渣量.065.092.197.761.000-.339.034.111鐵量差.107.075-.067-.544-.412鐵口工作深度-.216-.038.013.196.502.371.331.203混合時(shí)間-.246-.010-.147.368.156.436.232-.082Mn.327-.161.232.203-.277.376.192-.208SiO2-.364-.096.300.208-.418-.257.559.150Al2O3.413.045-.275-.223.373-.280.484.157MgO.295-.217.075-.335.345-.067.475-.283鐵口丁作泥量 <215 234 -.192 174 -.03& 226 -.061 763ExtractionMethod:PrincipalComponentAnalysis.a.8componentsextracted.F1=0.828*Ls-0.811*S+0.789*Ti+0.772*R+0.745*RR+0.680*Sz+0.658*CaO+0.555*鐵水溫度+0.516*F+0.161*K2O+0.163*FeO+0.166*Na2O+0.563*Si+0.416*P-0.047^際產(chǎn)量+0.065*水渣量+0.107*鐵量差-0.216*鐵口工作深度-0.246*混合時(shí)間+0.327*Mn-0.364*SiO2+0.413*Al2O3+0.295*MgO-0.215*^口工作泥量F2=-0.095*Ls+0.094*S-0.195*Ti+0.141*R+0.150*RR+0.096*Sz+0.105*CaO-0.173*鐵水溫度-0.105*F+0.929*K2O+0.927*FeO+0.926*Na2O-0.243*Si-0.296*P+0.031*實(shí)際產(chǎn)量+0.092*水渣量+0.075*鐵量差-0.038*鐵口工作深度-0.010*混合時(shí)間-0.161*Mn-0.096*SiO2+0.045*Al2O3-0.217*MgO+0.234*<口工作泥量F3=0.151*Ls-0.202*S+0.416*Ti-0.530*R-0.533*RR-0.521*Sz-0.424*CaO+0.369*鐵水溫度-0.090*F+0.305*K2O+0.307*FeO+0.304*Na2O+0.596*Si+0.599*P+0.177*$際產(chǎn)量+0.197*水渣量-0.067*鐵量差+0.013*鐵口工作深度-0.147*混合時(shí)間+0.232*Mn+0.300*SiO2-0.275*Al2O3+0.075*MgO-0.192*<口工作泥量F4=0.057*Ls-0.220*S+0.024*Ti+0.210*R+0.245*RR-0.128*Sz+0.371*CaO-0.108*鐵水溫度-0.372*F-0.084*K2O-0.073*FeO-0.094*Na2O-0.053*Si-0.018*P+0.831*實(shí)際產(chǎn)量+0.761*水渣量-0.544*鐵量差+0.196*鐵口工作深度+0.368*混合時(shí)間+0.203*Mn+0.208*SiO2-0.223*Al2O3-0.335*MgO+0.174*<口工作泥量F5=-0.241*Ls+0.289*S-0.045*Ti-0.001*R-0.011*RR-+0.250Sz-0.263*CaO+0.206*鐵水溫度+0.078*F+0.047*K2O+0.045*FeO+0.033*Na2O+0.252*Si+0.116*P+0.269*^際產(chǎn)量+0.000*水渣量-0.472*鐵量差+0.502*鐵口工作深度+0.156*混合時(shí)間-0.277*Mn-0.418*SiO2+0.373*Al2O3+0.345*MgO-0.038嚶口工作泥量F6=-0.098*Ls-0.053*S-0.072*Ti+0.148*R+0.186*RR-0.077*Sz+0.011*CaO+0.223*鐵水溫度-0.486*F+0.023*K2O+0.023*FeO+0.009*Na2O+0.061*Si+0.274*P-0.329*實(shí)際產(chǎn)量-0.339*水渣量+0.163*鐵量差+0.371*鐵口工作深度+0.436*混合時(shí)間+0.376*Mn-0.257*SiO2-0.280*Al2O3-0.067*MgO-0.226^口工作泥量F7=0.033*Ls-0.077*S-0.098*Ti-0.091*R-0.081*RR-0.024*Sz+0.254*CaO-0.162*鐵水溫度-0.051*F+0.051*K2O+0.049*FeO+0.037*Na2O-0.136*Si+0.071*P-0.069*實(shí)際產(chǎn)量+0.034*水渣量+0.187*鐵量差+0.331*鐵口工作深度+0.232*混合時(shí)間+0.192*Mn+0.559*SiO2+0.484*Al2O3-0.475*MgO-0.061*鐵口工作泥量F8=0.004*Ls+0.022*S+0.125*Ti-0.067*R-0.082*RR+0.103*Sz+0.015*CaO+0.018*鐵水溫度+0.190*F-0.047*K2O-0.044*FeO-0.061*Na2O+0.187*Si+0.140*P-0.053*^際產(chǎn)量+0.111*水渣量+0.212*鐵量差+0.203*鐵口工作深度-0.082*混合時(shí)間-0.208*Mn+0.150*SiO2+0.157*Al2O3-0.283*MgO+0.763*<口工作泥量附件三：主成分及相應(yīng)回歸值F1F2F3F4F5F6F7F8Y823.15-219.46616.9339.47415.1164.21-224.5491.812.1839.68-220.8628.85340.29404.05156.38-222.384.71.92837.05-221.76608.08274.9389.16186.09-219.0298.681.85852.29-229.68606.97203.78374.54193.7-226.7686.271.71864.61-210.17640.13350.66377.61127.93-221.73117.281.6854.56-232.03592.62129.48353.78208.2-228.4488.862860.38-222.56617.15247.49376.8165.82-230.99104.591.53838.73-229.14653.49441.87471.66109.16-255.5658.891.44865.1-223.1589.53119.29329.33221.25-213.65115.691.7853-218.05644.58370.84410.13116.94-236.3789.211.86857.52-220.56603.94181.11350.72175.63-229.52111.622.04841.42-221.17642.29381.58429.9115.36-244.7778.632.02847.52-217.6628.73334.58401.36134.82-233.398.982.03824.63-221.27655.64489.56474.69100.99-245.8965.641.85838.43-227.76575.8119.46349.13231.86-217.57103.112.2834.26-211.44679.15561.1473.4953.97-248.176.811.9833.73-217.01600.27252.76368.81195.31-208.97107.742.24849.27-221.48631.2313.94399.66141.15-234.6188.841.87832.46-213.73636.14401.73421.2128.93-228.4396.612.02

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