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    含氟連鑄保護渣黏度檢測與預測模型

    趙忠宇 趙俊學 譚澤馨 屈波樵 崔雅茹

    趙忠宇, 趙俊學, 譚澤馨, 屈波樵, 崔雅茹. 含氟連鑄保護渣黏度檢測與預測模型[J]. 工程科學學報, 2021, 43(4): 529-536. doi: 10.13374/j.issn2095-9389.2020.05.03.002
    引用本文: 趙忠宇, 趙俊學, 譚澤馨, 屈波樵, 崔雅茹. 含氟連鑄保護渣黏度檢測與預測模型[J]. 工程科學學報, 2021, 43(4): 529-536. doi: 10.13374/j.issn2095-9389.2020.05.03.002
    ZHAO Zhong-yu, ZHAO Jun-xue, TAN Ze-xin, QU Bo-qiao, CUI Ya-ru. Viscosity detection and the estimation model of fluorine-containing mold flux for continuous casting[J]. Chinese Journal of Engineering, 2021, 43(4): 529-536. doi: 10.13374/j.issn2095-9389.2020.05.03.002
    Citation: ZHAO Zhong-yu, ZHAO Jun-xue, TAN Ze-xin, QU Bo-qiao, CUI Ya-ru. Viscosity detection and the estimation model of fluorine-containing mold flux for continuous casting[J]. Chinese Journal of Engineering, 2021, 43(4): 529-536. doi: 10.13374/j.issn2095-9389.2020.05.03.002

    含氟連鑄保護渣黏度檢測與預測模型

    doi: 10.13374/j.issn2095-9389.2020.05.03.002
    基金項目: 國家自然科學基金資助項目(51674185,51674186)
    詳細信息
      通訊作者:

      E-mail:zhaojunxue1962@126.com

    • 中圖分類號: F407.3

    Viscosity detection and the estimation model of fluorine-containing mold flux for continuous casting

    More Information
    • 摘要: 采用旋轉柱體法對不同類型的含氟連鑄保護渣黏度進行檢測,并基于Arrhenius方程通過非線性回歸分析建立了新的黏度預測模型,分析了組分變化對黏度的影響。結合模型計算和實驗檢測,建立了CaF2?Na2O?Al2O3?CaO?SiO2?MgO渣系的等黏度圖。結果表明,與傳統的含氟連鑄保護渣黏度預測模型相比,該模型計算的偏差在10%以內,當渣中w(CaF2)超過20%時,偏差逐漸增大,主要由于氟化物揮發造成爐渣成分變化,最終黏度實測值與爐渣初始成分不符,造成模型無法對黏度有效預測。此外,研究發現,CaF2的增加能顯著降低爐渣黏度,而Al2O3和Na2O對黏度的影響受CaF2含量的限制。當w(CaF2)>17%,爐渣黏度隨Al2O3含量增加而減小,當w(CaF2)<17%,Al2O3的增加使爐渣黏度顯著增大;當w(CaF2)>11.5%,爐渣黏度隨Na2O含量增加顯著下降,當w(CaF2)<11.5%,Na2O含量變化對黏度的影響并不明顯。此外,該等黏度圖表明低黏度區w(CaF2)接近14%。通過調整等黏度圖中各組分比例,可以改善保護渣的黏度和流動性,供鋼鐵工業應用。

       

    • 圖  1  黏度?溫度線性分析

      Figure  1.  Linear analysis of lnη to 1/T

      圖  2  黏度參數擬合數值變化。(a)lnA;(b)B

      Figure  2.  Viscosity parameter fitting value: (a) lnA; (b) B

      圖  3  含氟保護渣黏度預測值與檢測值對比。(a)Riboud模型;(b)Iida模型;(c)Mills模型;(d)本研究模型

      Figure  3.  Estimated and measured viscosities of fluorine-containing mold flux: (a) Riboud model; (b) Iida model; (c) Mills model; (d) studied model

      圖  4  CaF2?CaO?SiO2三元渣黏度預測值與檢測值對比。(a)Mills模型;(b)本研究模型

      Figure  4.  Estimated and measured viscosities of CaF2?CaO?SiO2: (a) Mills model; (b) studied model

      圖  5  CaF2,Na2O和Al2O3成分變化對保護渣黏度影響

      Figure  5.  Effect of CaF2, Na2O, and Al2O3 on the viscosity of mold flux

      圖  6  CaF2?Na2O?Al2O3?CaO?SiO2?MgO等黏度圖(M=0.9, w(MgO)=6.5%,1500 ℃)

      Figure  6.  CaF2–Na2O–Al2O3–CaO–SiO2–MgO iso-viscosity diagram (M=0.9, w(MgO)=6.5%, 1500 °C)

      表  1  含氟保護渣成分范圍(質量分數)

      Table  1.   Composition of fluorine-containing mold flux %

      CaF2MNa2OAl2O3MgO
      4–200.6–1.23–122–120–12
      下載: 導出CSV

      表  2  連鑄保護渣組成(質量分數)

      Table  2.   Components of mold fluxes for continuous casting %

      SlagCaOSiO2Al2O3CaF2Na2OMgOMSlagCaOSiO2Al2O3CaF2Na2OMgOM
      C124.622.311.21911.711.21.1C2433.737.47.212.43.16.20.9
      C234.030.910.517.84.72.11.1C2527.630.77.6138.1130.9
      C335.132.011.27.811.72.21.1C2633.837.57.6138.100.9
      C435.131.910.57.34.710.51.1C2730.734.17.6138.16.50.9
      C532.829.84.51911.72.21.1M123.438.36.212.918.80.50.6
      C632.929.94.217.84.710.51.1M217.540.05.214.521.51.30.4
      C733.930.94.57.811.711.21.1M320.134.44.814.326.400.6
      C842.838.94.27.34.72.11.1M419.436.618.017.28.800.5
      C923.032.911.21911.72.20.7M532.631.25.38.022.901.0
      C1023.333.210.517.84.710.50.7M622.542.210.410.713.01.30.5
      C1123.934.211.27.811.711.20.7M721.533.23.715.425.70.50.6
      C1231.044.410.57.34.72.10.7M822.738.66.313.518.900.6
      C1322.131.54.51911.711.20.7M922.335.08.916.517.300.6
      C1429.341.94.217.84.72.10.7M1020.434.04.317.923.400.6
      C1530.443.44.57.811.72.20.7M1113.832.03.520.227.13.50.4
      C1630.243.14.27.34.710.50.7P139390138.41.61.0
      C1735.329.57.6138.16.51.2P239390133.66.41.0
      C1824.340.57.6138.16.50.6P341410133.61.61.0
      C1928.131.313138.16.50.9P43939078.46.41.0
      C2033.336.92.2138.16.50.9P54242078.41.61.0
      C2126.629.67.621.68.16.50.9P64242073.66.41.0
      C2234.838.77.64.38.16.50.9P74444073.61.61.0
      C2327.530.58.013.613.66.80.9P830420138.46.40.7
      P93448133.61.60.7S133.553.013.50.6
      P10344115640.85S232.947.819.30.7
      P113541101040.85S333.146.920.00.7
      P12394510600.85S430.837.831.40.8
      P13354110680.85S527.030.442.60.9
      P14374310640.85S633.039.627.40.8
      P15423810641.1S733.238.628.20.9
      下載: 導出CSV

      表  3  黏度參數分析

      Table  3.   Viscosity controlling parameters

      SlaglnABSlaglnABSlaglnAB
      C1?12.116616750.76C10?11.166315523.76C19?11.641916899.20
      C2?11.152115331.25C11?11.406916399.51C20?10.573614696.67
      C3?11.482716323.80C12?11.904519019.28C21?9.2606311473.14
      C4?11.233816106.31C13?10.711413670.59C22?11.338818048.45
      C5?10.966613904.19C14?11.189615932.56C23?10.481413697.65
      C6?11.180614859.89C15?11.439117000.89C24?17.236225991.79
      C7?11.352215328.48C16?11.131216491.46C25?10.581214320.87
      C8?11.363516421.96C17?14.961021103.44C26?13.068218316.60
      C9?10.999214722.03C18?12.897219711.71C27?10.782813602.93
      下載: 導出CSV
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    • 收稿日期:  2020-05-03
    • 刊出日期:  2021-04-26

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