Abstract:
Method for controlling and preconfiguring a steelworks or parts of a steelworks, the rolling stand or the rolling mill train being controlled and preconfigured by means of a model of the rolling stand or the rolling mill train, the model having at least one neural network whose parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, the rate at which the parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, being varied.

Description:
FIELD OF THE INVENTION 
     The present invention relates to a method for controlling and preconfiguring a steelworks or parts of a steelworks. In this context, the term parts of a steelworks is intended to mean rolling mill trains, rolling stands, continuous or strip casting systems and units for heat treatment or cooling. 
     The present invention also relates to a method for controlling and/or preconfiguring a rolling stand or a rolling mill train for rolling a strip, the rolling stand or the rolling mill train being controlled and/or preconfigured by means of a model of the rolling stand or the rolling mill train, the model having at least one neural network whose parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip. 
     BACKGROUND INFORMATION 
     In order to control and preconfigure rolling stands or a rolling mill train for rolling a strip, models may be used which have at least one neural network whose parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip. Model-assisted control or preconfiguration of this type is in particular possible for applications as described in DE 41 31 765, EP 0 534 221, U.S. Pat. No. 5,513,097, DE 44 16 317, U.S. Pat. No. 5,600,758, DE 43 38 608, DE 43 38 615, DE 195 22 494, DE 196 25 442, DE 196 41 432, DE 196 41 431, DE 196 42 918, DE 196 42 919, DE 196 42 921. If they are adapted on-line, neural networks for these applications are adapted at constant adaptation rates. This means that, on the basis of each strip which is rolled, the error function for this strip is calculated. The leave of this error function is then determined and, with a view to a gradient optimization, a procedure is adopted whereby the error function is reduced by the chosen adaptation rate. It has been shown that, using on-line adaptation, the term on-line adaptation being intended to mean the adaptation of a neural network on the basis of a strip which is rolled, the quality of rolled steel is significantly improved. Difficulties are, however, found in terms of reliability problems pertaining to the convergence during the adaptation. If, because of deficient adaptation to malfunctioning, incorrect control or deficient preconditioning arise, this may lead to large losses for the application on account of inferior rolled steel or damage to the rolling mill train. Furthermore, because of the high investment costs for a rolling mill train, downtimes are very expensive. This being the case, the adaptation of neural networks for the control or preconfiguration of rolling stands or rolling mill trains is problematic. 
     SUMMARY 
     An object of the present invention is to provide a method for making the control or preconfiguration of a steelworks or parts of a steelworks more reliable. It is furthermore desirable to improve the accuracy of the model values determined by means of a neural network. 
     The object is achieved according to the invention by providing a method in which the rate at which the parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, is varied. It is in this way possible, for example, to distinguish whether the neural network has already properly mastered the function to be approximated at the corresponding point, whether the data point belongs to an infrequent event, that is to say to steel which is rarely rolled, or whether, because of a measuring error or an error in the subsequent calculation, the data point to be trained is in fact completely unusable. This leads to much more robust adaptation. In an advantageous embodiment of the present invention, the rate at which the parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, is varied as a function of the information density, in particular the training data pertaining to strips of the same or a similar type. 
     The information density D is in this case an (abstract) measure of how much information is present at a given point in the input space (typically, how many strips of the same or a similar quality have already been rolled). An illustrative embodiment for a definition of the information density is          D        (     x   n     )       =       ∑     k   =   1     sizenet              b   k          (     x   n     )              D   k          (     x   n     )                                  
     D(X n ) is the estimate of the information density for point xn, after treating all the patterns x 1  to x n−1 . b k (x n ) is the activity of the k-th neuron in the hidden plane or the hidden planes of the neural network on application of the pattern x n . D k (x n ) is the estimate of the local information density at the site of the k-th neuron, after processing all patterns x 1  to x n−1 . sizenet corresponds to the number of neurons in the hidden plane or the hidden planes of the neural network. b k  is calculated from              b   k          (     x   n     )       =     exp        (       -     1   2              (     x   -   μ     )     T            ∑     -   1            (     x   -   μ     )         )                                    with                 x     =     [           x   1               x   2             ⋮             x   n           ]                 μ   =       [           μ                 1               μ                 2             ⋮             μ                 n           ]                   and                   ∑     -   1            =     [           1   2         0       …       0             σ   1                                                 0         1   2                     ⋮                         σ   2                                     ⋮                   ⋰                       0       …                     1   2                                                   σ   n           ]                                    
     μi being the expected value and σ 2   i  the variance of x i . 
     D k (x n ) is calculated as:            D   k          (     x   n     )       =         I   k          (     x   n     )         I        (     x   n     )                                
     I k(x   n ) is the information accumulated locally over the entire history of all patterns x n  to x n−1  at the k-th neuron of the hidden plane or of the hidden planes of the neural network, I(x n ) is the information similarly acquired overall in the network. I k (x n ) is calculated as            I   k          (     x   n     )       =       ∑       x   ′     =     {       x   1                   …                   x     n   -   1         }                  b   k          (     x   ′     )            f        (       E        (     x   ′     )       ,     η        (     x   ′     )         )                                  
     f is a function of the prognosis error E(x′) (see below) and the learning rate η(x′). It takes into account that, for the patterns learned in the past only with a low learning rate, there is only a small amount of information. In the simplest case, it would be possible to set 
     
       
           f =1∀( x′∈x   1    . . . x   n−1 ) 
       
     
     For I(x n ):          I        (     x   n     )       =         ∑     k   =   1     sizenet            I   k          (     x   n     )         =       ∑       x   ′     =     {       x   1                   …                   x     n   -   1         }              f        (       E        (     x   ′     )       ,     η        (     x   ′     )         )                                  
     Since, for all x′ε{x 1  . . . x n−1 ], then            ∑     k   =   1     sizenet            b   k          (     x   ′     )         =   1                          
     In a further particularly advantageous embodiment of the present invention, the rate at which the parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, is varied as a function of the expected error, in particular the average error over the entire adaptation phase or the average error over a long time interval during the adaptation. 
     The expected error F is, for example, the average error over the entire history at the point x n  in space. It may, for example, be of the following form:          F        (     x   n     )       =       ∑     k   =   1     sizenet              b   k          (     x   n     )              F   k          (     x   n     )                                  
     F k (x n ) being the local expected error for the n-th pattern at the k-th neuron of the hidden plane of a neural network. If F k (x n ) is given as            F   k          (     x   n     )       =         ∑       x   ′     =     {       x   1                   …                   x     n   -   1         }                          b   k          (     x   ′     )            E        (     x   ′     )            f   (     E        (     x   ′     )                   I   k          (     x   n     )                                             
     Through multiplication of the error E(x′) with b k (x′), the numerator contains a measure of the local error. This error is divided by the local information density. 
     A further approach for calculating the expected error is for the calculation to be carried out in the form of local statistics, in which not only the average of the local error but also its variance are taken into account. 
     In a further advantageous refinement of the invention, the rate at which the parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, is varied as a function of the current error during the adaptation, i.e., the current error between the conditions in the rolling stand and/or in the rolling mill train, in particular the properties of the strip, determined by means of the neural network and the actual conditions. 
     The current error E is, for example, the Euclidean or other distance between the network prediction, i.e., the value determined by means of the neural network, and the actual value. The Euclidean distance, which is advantageously used as the current error E, is defined as 
     
       
           E =( y   n ( x   n   ,w )− t   n ( x   n )) 2   
       
     
     x n  being the input variable or the input variables of the network, y n (x,w) being the output variable, for example the rolling force, of the neural network for a pattern x n  as a function of the network weights w, and t n (x) the actual value corresponding to y n (x n ,w). n corresponds to the chronological sequence of the training patterns. 
     A case distinction is drawn according to the invention for at least one of the three variables information density, expected error and current error. In this case, in a particularly advantageous embodiment of the present invention, distinction is made between a normal case (well-trained network), unusual case (typically a very infrequently rolled steel, for example coin steel), aberrant (for example due to failure of a measuring sensor) and an unstable process (for a very similar type of steel, the target value in the past fluctuate considerably). The degree of the adaptation of the network is chosen in accordance with this error distinction, as shown by Table 1. In this case, ↑ indicates high, ↓ indicates low (possibly equal to zero) and → indicates medium. 
     
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Case distinction for the adaptation rate 
               
             
          
           
               
                   
                   
                   
                   
                 Degree of 
               
               
                   
                   
                   
                   
                 adaptation/ 
               
               
                   
                 Information 
                 Expected 
                 Current 
                 adaptation 
               
               
                   
                 density 
                 error 
                 error 
                 rate 
               
               
                   
                   
               
             
          
           
               
                 Well-trained 
                   
                   
                   
                   
               
               
                 network 
               
               
                 (normal) 
               
               
                 Well- 
                   
                  or  
                   
                   
               
               
                 generalized 
               
               
                 unusual case 
               
               
                 Poorly 
                   
                  or  
                   
                   
               
               
                 generalized 
               
               
                 unusual case 
               
               
                 Aberrant 
                   
                   
                   
                   
               
               
                 Unstable 
                   
                   
                  or  
                 Termination 
               
               
                 process 
                   
                   
                   
                 alarm 
               
               
                   
               
             
          
         
       
     
     If the information density is high, the expected error is low and the actual error is low, then a well-trained network is assumed and the adaptation rate is kept at a medium value. If the information density and the current error are small, then it is assumed that the neural network, in the case of an infrequent kind of steel, i.e., an unusual case, achieves good generalization. The adaptation rate is kept at a medium value. If, however, the current error is high with a small information density, then the adaptation rate is increased. A combination in which the information density and the current error are high, but the expected error is small, is interpreted as aberrant and the adaptation rate is accordingly reduced, or no adaptation takes place. If both the information density and the expected error are high, this is assessed as an indication of an unstable adaptation process. The adaptation is terminated. 
     The method according to the present invention is maybe used in conjunction with the applications described in DE 41 31 765, EP 0 534 221, U.S. Pat. No. 5,513,097, DE 44 16 317, U.S. Pat. No. 5,600,758, DE 43 38 608, DE 43 38 615, DE 195 22 494, DE 196 25 442 DE 196 41 432, DE 196 41 431, DE 196 42 918, DE 196 92 919, DE 196 42 921. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 shows the method according to the present invention 
     FIG. 2 shows a block circuit diagram for the process control in a preliminary and a final mill train in accordance with the method according to the present invention. 
    
    
     DETAILED DESCRIPTION 
     FIG. 1 shows an outline representation of the method according to the present invention. In this case, the reference number  33  denotes a neural network, the reference number  34  denotes an adaptation algorithm and the reference number  35  denotes the determination of the adaptation rate  36  of the neural network  33 . On the basis of input variables  31 , the neural network calculates output variables  37 . In order to adapt the neural network  33 , these input variables  31  and output variables  37  are also fed to an adaptation algorithm  34  which compares the network response, i.e., the output variables  37  of the neural network  33 , with corresponding actual values  32 . On the basis of these variables, the adaptation algorithm  34  adapts the parameters of the neural network  33 . In accordance with the method according to the present invention, the adaptation rate  36  for the adaptation algorithm  34  is determined on the basis of at least one of the variables  31 ,  32 ,  37  or the internal state variables  38  of the neural network  33 . Of course, the variables  31 ,  32 ,  37  and  38  are not necessary scalars, but may also be multiple variables. Thus, for example, the input variables  31  may be variables such as the strip thickness, the strip width, the strip temperature, the alloy components of the strip, etc. An example of a possible output variable  37  of the neural network  33  is a correction value for the rolling force. 
     FIG. 2 shows a preliminary mill train  1  and a final mill train  2 , with rolling stands  3  and  4 , respectively, for rolling metal strips  5 . The preliminary mill train  1  is controlled by a control device  6  which acts on the individual rolling stands  3  and operates different actuators. The control device  6  obtains the information needed to control the rolling stands  3  both from a computer unit  7  and from a device  8  for detecting measured values. At the start of the rolling process, there are not yet any measured values for the variables needed to regulate the process. For this reason, predicted values for the variables are calculated in the computer unit  7  on the basis of modeling assumptions, and communicated to the control device  6  in order to preconfigure the preliminary mill train  1 . During the course of the process, measured values of the variables used for regulating the process are detected by the device  8  for detecting measured values and are fed to the control device  6 . 
     Via a link  9 , the control device  6  furthermore obtains information for determining a predicted value y pre  for the expected width variation of the metal strip  5  in the final mill train  2 . On the basis of this predicted value y pre , the rolling process, i.e., the compression of the metal strip  5  in the preliminary mill train  1 , is controlled in such a way that the preliminary strip width, i.e., the width of the metal strip  5  when it emerges from the preliminary mill train  1 , is equal to the desired target final strip width for the metal strip  5  when it emerges from the final mill train  2 , less the predicted width variation y pre  of the metal strip  5  in the final mill train  2 . In this way, the affect achieved when there is an accurate prediction of the width variation of the metal strip  5  in the final mill train  2 , is that the metal strip  5  has the desired target final strip width when it emerges from the final mill train  2 . 
     As in the preliminary mill train  1 , the individual rolling stands  4  are controlled in the final mill train  2  by a control device I  0  which receives the information needed for this from a computer unit  11  and a device  12  for detecting measured values. Before a metal strip  5  passing through the preliminary mill train  1  enters the final mill train  2 , predicted values of the variables needed for regulating the rolling process in the final mill train  2  are calculated in the computer unit  11  and sent to the control device  10  in order to preconfigure the final mill train  2 . Of these variables determined in advance, those which may have an influence on the width variation of the metal strip  5  in the final mill train  2  are fed as input variables x before  to a neural network  13  which, as network response, produces a computed value y NN (x before ) for the width variation, and provides this on the link  9  for calculating the predicted value y pre  in the control device  9 . The following variables, which form the input vector x before , are in particular suitable as influencing variables or input variables for the neural network  13 . 
     When the metal strip  5  passes through the final mill train  2 , the process-relevant variables including the influencing variables of the preliminary strip temperature, the preliminary strip thickness, the final strip temperature, the final strip thickness, the target final strip width, the rate at which the final strip emerges from the final mill train  2 , the strength of the material, the profile, the relative thickness reductions in the individual rolling stands  4 , the various lateral positions of the lateral displacement rolls and the tensions in the metal strip  5  between the individual rolling stands  4 , are measured by means of the device  12  for detecting measured values and are fed to the controller  10  as well as to a device  14  for after-processing. The after-processing comprises, for example, a statistical treatment of the measured influencing variables and the calculation of influencing variables which cannot be measured directly as a function of other measured variables. With these influencing variables which are measured afterwards, i.e., those which are determined substantially more accurately than the calculation beforehand in the computer unit  11 , the network parameters of the neural network  13  are adapted after the metal strip  5  has passed through the final mill train  2 . To do this, the influencing variables calculated afterwards are compiled in an input vector x after  and sent to the neural network  13 . The network response y NN (x after ) then received from the neural network  13  is fed to an adaptation algorithm  15  which is furthermore fed the actual preliminary strip width WP measured in front of the final mill train  2  at point  16 , as well as the actual final strip width WF measured after the final mill train  2  at point  17 . The actual width variation y act =WF−WP obtained in this way is compared with the network response y NN (x after ), the discrepancy between the network response y NN (x after ) and the actual width variation y act  being employed via the link  18  for adaptation of the network parameters with a view to reducing this discrepancy. Further to the calculated value y NN (x before ), the values y NN (x after ) and y act  are also provided on the link  9  and fed to the control device  6  in order to calculate the predicted value y pre  for the width variation. 
     The adaptation algorithm  15  is given a predetermined adaptation rate  22  which is determined by means of an adaptation-rate determiner  20 . Besides the values y NN (x before ),  NN (x after ), y act , further input variables in the adaptation-rate determiner  20  are x after , WP and WF as well as the internal states  23  of the neural network  13 . The adaptation-rate determiner  20  optionally outputs an alarm signal  21  if the information density and expected error are high. This alarm signal  21  is used by a higher-order system for process diagnosis. As a safety measure, the adaptation of the neural network  13  is stopped in such a case.