Founded in 1987, Bimonthly
Supervisor:Jiangxi University Of Science And Technology
Sponsored by:Jiangxi University Of Science And Technology
Jiangxi Nonferrous Metals Society
ISSN:1674-9669
CN:36-1311/TF
CODEN YJKYA9
LI Ming-zhou, HUANG Jin-di, TONG Chang-ren, ZHANG Wen-hai. A composition soft-sensing model of FeO-Fe2O3-SiO2 ternary slag system based on two-temperature two-density method[J]. Nonferrous Metals Science and Engineering, 2016, 7(5): 37-41. DOI: 10.13264/j.cnki.ysjskx.2016.05.007
Citation: LI Ming-zhou, HUANG Jin-di, TONG Chang-ren, ZHANG Wen-hai. A composition soft-sensing model of FeO-Fe2O3-SiO2 ternary slag system based on two-temperature two-density method[J]. Nonferrous Metals Science and Engineering, 2016, 7(5): 37-41. DOI: 10.13264/j.cnki.ysjskx.2016.05.007

A composition soft-sensing model of FeO-Fe2O3-SiO2 ternary slag system based on two-temperature two-density method

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  • Received Date: January 12, 2016
  • Published Date: October 30, 2016
  • According to the characteristics of composition strong coupling with physical properties of smelting slag, the temperature and density of slag system were selected as the secondary variables of soft sensor, and the regression algorithm with Taylor series three times expansion was used, then a composition soft-sensing model of the ternary slag system was established based on two-temperature two-density method. Practical experimental results of slag system of FeO-Fe2O3-SiO2 show that regression calculation has high accuracy, and the average relative error (absolute value) of density is only 0.412%. Multi-group soft measurements have achieved superior predictive hit rate, and the model is suitable for compositions of the slag system with wide range. Analysis of the density-temperature characteristic curves drawn show that the model can well take account of the inherent law of density, composition and temperature of the ternary slag system. The research serves as a nice soft-sensing model for predicting compositions of the ternary slag system.
  • [1]
    ERIC R. Slag properties and design issues pertinent to matte smelting electric furnaces[J]. Journal of the South African Institute of Mining and Metallurgy, 2004, 104(9): 499-510. http://www.saimm.co.za/Conferences/MoltenSlags2004/531-Eric.pdf
    [2]
    MILLS K. The estimation of slag properties[J]. Southern African Pyrometallurgy, 2011, 7(3): 35-42. http://www.pyrometallurgy.co.za/KenMills/KenMills.pdf
    [3]
    ERIC R H, HEJJA A A, STANGE W. Liquidus temperature and electrical conductivities of synthetic ferromanganese slags[J]. Minerals Engineering, 1991, 4(12): 1315-1332. doi: 10.1016/0892-6875(91)90175-U
    [4]
    白晨光, 裴鹤年.熔渣表面粘度测定的基础研究[J].重庆大学学报:自然科学版, 1993, 16(2): 84-87. http://www.cnki.com.cn/Article/CJFDTOTAL-FIVE199302014.htm
    [5]
    朱祖泽, 贺家齐.现代铜冶金学[M].北京:科学出版社, 2003.
    [6]
    何焕华, 蔡乔方.中国镍钴冶金[M].北京:冶金工业出版社, 2000.
    [7]
    REUTER M A, ERIC R H, HEJJA A A. Modelling of liquidus temperature and electrical conductivity of manganese smelting slags by the use of neural nets[J]. Sensors & Modeling in Materials Processing, 1997: 35.
    [8]
    王雅静, 翟玉春, 田彦文, 等.铝酸钠溶液表面张力的化工数学模型[J].有色金属, 2004, 56(3): 60-62. http://www.cnki.com.cn/Article/CJFDTOTAL-YOUS200403018.htm
    [9]
    刘飞飞, 刘辉辉, 李俊荣.基于RBF神经网络的WO3浸出率软测量建模[J].有色金属科学与工程, 2013, 4(5): 117-121. http://ysjskx.paperopen.com/oa/DArticle.aspx?type=view&id=2013050023
    [10]
    殷铭宏.基于泡沫视觉特征的浮选精矿品位的软测量[J].有色冶金设计与研究, 2013, 34(6): 52-54. http://www.cnki.com.cn/Article/CJFDTOTAL-YSYJ201306017.htm
    [11]
    童长仁, 李明周, 吴金财, 等.基于BP网络逆映射的铝酸钠溶液软测量模型[J].中国有色金属学报, 2008, 18(5): 917-922. http://www.cnki.com.cn/Article/CJFDTOTAL-ZYXZ200805027.htm
    [12]
    TIAN H X, MAO Z Z, WANG A N. Hybrid modeling for soft sensing of molten steel temperature in LF[J]. Journal of Iron and Steel Research, International, 2009, 16(4): 1-6. doi: 10.1016/S1006-706X(09)60051-0
    [13]
    GUI W H, YANG C H, CHEN X F, et al. Modeling and optimization problems and challenges arising in nonferrous metallurgical processes[J]. Acta Automatica Sinica, 2013, 39(3): 197-207. doi: 10.1016/S1874-1029(13)60022-1
    [14]
    GUI W H, WANG L Y, YANG C H, et al. Intelligent prediction model of matte grade in copper flash smelting process[J]. Transactions of Nonferrous Metals Society of China, 2007, 17(5): 1075-1081. doi: 10.1016/S1003-6326(07)60228-3
    [15]
    YUAN X, GE Z, SONG Z. Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes[J]. Industrial & Engineering Chemistry Research, 2014, 53(35): 13736-13749. doi: 10.1021/ie4041252
    [16]
    李凌.软测量过程中的若干建模方法研究[J].沈阳化工学院学报, 2006, 20(2): 118-120. http://www.cnki.com.cn/Article/CJFDTOTAL-SYHY200602009.htm
    [17]
    张明健.基于三温三电导法的铝酸钠溶液在线实时测量[D].阜新:辽宁工程技术大学, 2004. http://cdmd.cnki.com.cn/Article/CDMD-10147-2004044486.htm
    [18]
    李明周.铝酸钠溶液成分浓度软测量数模研究与软件开发[D].赣州:江西理工大学, 2008. http://cdmd.cnki.com.cn/Article/CDMD-10407-2010020197.htm
    [19]
    董林垚, 陈建耀, 谢丽纯, 等.基于温度和电导的地下水-海水交互作用研究[J].热带地理, 2010, 30(6): 597-602.
    [20]
    李志宏, 杜娟, 马莹, 等.铝酸钠溶液化学成分实时测量系统设计及应用[J].仪器仪表学报, 2005, 26(10): 1019-1022. http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB200510007.htm
    [21]
    KANEKO H, ARAKAWA M, FUNATSU K. Development of a new soft sensor method using independent component analysis and partial least squares[J]. AIChE Journal, 2009, 55(1): 87-98. doi: 10.1002/aic.v55:1
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