Data-driven Modeling and Predictive Algorithm for Complex Blast Furnace Ironmaking Process

Control of blast furnace ironmaking process means to control the temperature and compositions of blast furnace hot metal within speciflc bounds. For the silicon content in blast furnace hot metal as a targeted indicator of in-furnace thermal state, a data-driven based predictive model is constructed in this paper utilizing the information of multivariate time series measured from the blast flnance system. Through an example, it is indicated that the constructed data-driven based predictive model has a good performance in predicting the silicon content in hot metal, with 83.23% percentage of target hitting and 0.07260 of the root mean square error of prediction when the size of predicted sample set is 167. These criteria are better than those achieved by data-driven predictive model based on univariate time series, implying that the constructed data-driven predictive model based on multivariate time series can act as an important tool to serve the production process.

[1]  Gao,et al.  Chaotic Identification and Prediction of Silicon Content in Hot Metal , 2005 .

[2]  Chuanhou Gao,et al.  Assessing the Predictability for Blast Furnace System through Nonlinear Time Series Analysis , 2008 .

[3]  Hideki Kawai,et al.  Two Dimensional Cold Model Study on Unstable Solid Descending Motion and Control in Blast Furnace Operation with Low Reducing Agent Rate , 2005 .

[4]  Liu Xiang-guan.  Application of SVM to prediction of silicon content in hot metal , 2005 .

[5]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[6]  Jian Chen,et al.  A predictive system for blast furnaces by integrating a neural network with qualitative analysis , 2001 .

[7]  Liu Xue-yi.  Application of Bayesian Network to Predicting Silicon Content in Hot Metal , 2005 .

[8]  A. Mees,et al.  Dynamics from multivariate time series , 1998 .

[9]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[10]  T. Bhattacharya.  Prediction of Silicon Content in Blast Furnace Hot Metal Using Partial Least Squares (PLS) , 2005 .

[11]  Nong Ye,et al.  Recent Developments in Chaotic Time Series Analysis , 2003, Int. J. Bifurc. Chaos.

[12]  Hiroshi Nogami,et al.  Multi-dimensional transient mathematical simulator of blast furnace process based on multi-fluid and kinetic theories , 2005, Comput. Chem. Eng..

[13]  Frank Pettersson,et al.  Nonlinear Prediction of the Hot Metal Silicon Content in the Blast Furnace , 2007 .

[14]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[15]  Farmer,et al.  Predicting chaotic time series. , 1987, Physical review letters.

[16]  Xue-feng Yang,et al.  Numerical Simulation of Innovative Operation of Blast Furnace Based on Multi-Fluid Model , 2006 .

[17]  Abdul Rahman Mohamed,et al.  Neural networks for the identification and control of blast furnace hot metal quality , 2000 .

[18]  S Boccaletti,et al.  Reconstructing embedding spaces of coupled dynamical systems from multivariate data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Gao Chuan-hou,et al.  CHAOTIC IDENTIFICATION OF BF IRONMAKING PROCESS I. The Calculation of Saturated Correlative Dimension , 2004 .

[20]  H. Saxén.  Short-Term Prediction of Silicon Content in Pig Iron , 1994 .

[21]  Jiming Chen,et al.  A chaos‐based iterated multistep predictor for blast furnace ironmaking process , 2009 .

[22]  F. Obeso,et al.  Hot metal temperature prediction in blast furnace using advanced model based on fuzzy logic tools , 2007 .