Patent ID: 11898212
Assignee: ZHEJIANG UNIVERSITY
Field: Materials, metallurgy (Chemistry)
Classification: CPC C  F  G | IPC C  F  G

Claim 0:
1. A method for monitoring blast furnace state based on multi-modes fusion, comprising:
step (1) pre-calculation of sub-modes of blast furnace, comprising:
step (1.1) acquiring blast furnace historical parameter variable data and constructing a data set;
step (1.2) filling in missing values in the data set by a moving average method;
step (1.3) detecting abnormal values in the data set by a boxplot method and directly eliminating the detected abnormal values;
step (1.4) obtaining blast furnace state indication variable data and preprocessing, comprising filling the missing values and eliminating the abnormal values;
step (1.5) calculating a grey relation degree value between each blast furnace parameter variable data and blast furnace state indication variable data, and selecting top N blast furnace parameter variables with the highest correlation as characteristic variables; wherein a calculation formula of the grey relation degree is as follows:, γ
   ⁡
   (
   
    
     x
     0
    
    ,
    
     x
     i
    
   
   )
  
  =
  
   
    1
    n
   
   ⁢
   
    
     ∑
     
      p
      =
      1
     
     n
    
    
     
      ξ
      i
     
     (
     
      t
      p
     
     ), where

 
  
   
    ξ
    i
   
   (
   
    t
    p
   
   )
  
  =
  
   
    
     Δ
     min
    
    +
    
     ρ
     ⁢
     
      Δ
      max
     
    
   
   
    
     
      ❘
      "\[LeftBracketingBar]"
     
     
      
       
        x
        0
       
       (
       
        t
        p
       
       )
      
      -
      
       
        x
        i
       
       (
       
        t
        p
       
       )
      
     
     
      ❘
      "\[RightBracketingBar]"
     
    
    +
    
     ρΔ
     max
    
   
  
 

Δmin=(|x0(tp)−xi(tp)|)

Δmax=(|x0(tp)−xi(tp)|), where x0 is the blast furnace state indication variable data of n samples, x; is each blast furnace parameter variable of the n samples, i=1, 2, . . . , m, m is a total number of blast furnace parameter variables, ξ is a grey relation coefficient between the two variables, tp is a p-th moment, p=1, 2, . . . , n, n is a total number of samples contained in each parameter variable, ρ is a resolution coefficient, Δmin is a two level minimum difference, Δmax is a two level maximum difference, x0(tp) is a blast furnace state indication variable value at tp and xi(tp) is a blast furnace parameter value at tp;
step (1.6) selecting characteristic variable data calculated in step (1.5) from the blast furnace historical parameter variable data set to form a historical characteristic variable data set;
step (1.7) applying a mean shift clustering algorithm in the historical characteristic variable data set to obtain a plurality of cluster centers; and
step (1.8) calculating the Euclidean distance between the samples in the historical characteristic variable data set and each cluster center, selecting a sample point with a smallest Euclidean distance from each cluster center as the sub-modes of the blast furnace, respectively, and constructing a sub-mode characteristic variable data set by all selected sample point data, storing the blast furnace state indication variable data at a corresponding time of each sub-mode, and constructing a sub-mode indication variable data set;
step (2) blast furnace sub-mode fusion, comprising:
step (2.1) obtaining real-time parameter variable data of the blast furnace, and selecting the data corresponding to the N characteristic variables calculated in step (1.5) as input variables;
step (2.2) calculating the Euclidean distance between input variable data and the characteristic variable data of each sub-mode, and constructing an Euclidean distance matrix D=(d1, d2, . . . , du, . . . , dq), where du is the Euclidean distance between the input variable data and the characteristic variable data of a u-th sub-mode, and u=1, 2, . . . , q, q is a number of types of the sub-modes obtained in step (1.8);
step (2.3) calculating a weight on the basis of an exponential function and a sub-mode contribution rate, comprising:
step (2.3.1) scaling the Euclidean distance calculated in step (2.2) based on the exponential function and taking a reciprocal, wherein a calculation formula is as follows:, d
   u_t
  
  =
  
   1
   
    e
    
     r
     ×
     
      d
      u, where du_t is a transformed Euclidean distance between the input variable data and the characteristic variable data of the u-th sub-mode, and r is a scaling coefficient;
step (2.3.2) arranging the sub-modes in an order according to corresponding du_t from big to small, and selecting first L sub-modes whose total contribution rate is greater than a set threshold; wherein the calculation formula of the contribution rate conu of each sub-mode is as follows:, c
   ⁢
   o
   ⁢
   
    n
    u
   
  
  =
  
   
    d
    u_t
   
   
    
     
      ∑
       
     
     
      u
      =
      1
     
     q
    
    ⁢
    
     d
     u_t
    
   
  
 

step (2.3.3) normalizing the du_t of the selected sub-mode to obtain a corresponding weight wu, the corresponding weight of the unselected sub-mode being 0, and obtaining a weight matrix W of the sub-modes;
step (3) monitoring of blast furnace state, comprising:
reading the indication variable data corresponding to each sub-mode from the sub-mode indication variable data set, and performing weighted summation; wherein the calculation formula is as follows:, y
    z
   
   ˆ
  
  =
  
   
    
     ∑
      
    
    
     u
     =
     1
    
    q
   
   ⁢
   
    w
    u
   
   ×
   
    y
    z
    u, where ŷz is an estimated value of a z-th blast furnace state indication variable, z=1, 2, . . . , v, v is a number of types of the blast furnace state indication variables, and yzu is a z-th blast furnace state indication variable value corresponding to the u-th sub-mode; and
obtaining an estimated value of the corresponding real-time state indication variable of the blast furnace after sub-mode fusion, which realizes real-time state monitoring of the blast furnace.