Abstract:
A process for controlling a metallurgical plant for producing steel or aluminum, in particular a rolling mill, steel or aluminum having specific material properties that depend on the structure of the steel or aluminum being produced in the metallurgical plant from input materials, and the material properties of the steel or aluminum being a function of operating parameters with which the plant is operated, the operating parameters being determined by a structure optimizer as a function of the desired material properties of the steel or aluminum.

Description:
PRESENT FIELD OF THE INVENTION 
     The invention relates to a process and a device for for producing steel or aluminum, in particular for controlling a rolling mill for producing a rolled strip of steel or aluminum having specific material properties that depend on the structure of the steel or aluminum being produced from input materials. The material properties of the steel or aluminum are a function of operating parameters with which the plant is operated. The present invention also relates to the associated device for implementing the process. 
     BACKGROUND INFORMATION 
     The appropriate operating parameters are normally set by an operator of the metallurgical plant in such a way that the material properties of the steel or aluminum correspond to desired, predefined material properties. For this purpose, the operator usually has recourse to empirical knowledge which is stored, for example, in table form. 
     SUMMARY 
     The object of an present invention is to provide a process and a device for implementing the process which make it possible to produce steel or aluminum whose material properties correspond more precisely to the material properties desired in advance. 
     According to the present invention, the objective is achieved by providing a process according to and a device in which, the operating parameters are determined by a structure optimizer as a function of the desired material properties of the steel or aluminum. In so doing, material properties such as yield point, proof stress, tensile strength, elongation at fracture, hardness, transition temperature, anisotropy and consolidation index of the steel or aluminum are particularly advantageously considered. The process of the present invention permits operating parameters of a metallurgical plant to be set in such a way that the steel or aluminum produced has the desired material properties. 
     In an advantageous refinement of the present invention, the structure optimizer has a structure observer which predicts the material properties of a steel or aluminum produced in a metallurgical plant as a function of its operating parameters. A structure observer of this type advantageously has a neural network. 
     In a further advantageous refinement of the present invention, the structure optimizer determines at least one of the variables: yield point, proof stress, tensile strength, elongation at fracture, hardness, transition temperature, anisotropy and consolidation index of the steel or aluminum as a function of the temperature, the degree of deformation or the relative deformation of the steel, the deformation speed and the alloying components of the steel. 
     In another advantageous refinement of the process according to the invention, the structure observer determines at least one of the variables: yield point, proof stress, tensile strength, elongation at fracture, hardness, transition temperature, anisotropy and consolidation index of the steel to be examined as a function of the individual alloying components in the steel. In this case, it has been shown to be particularly advantageous to determine at least one of the variables: yield point, proof stress, tensile strength, elongation at fracture, hardness and transition temperature as a function of the carbon portion, of the silicon portion, of the manganese portion, of the phosphorus portion, of the sulphur portion, of the cobalt portion, of the aluminum portion, of the chromium portion, of the molybdenum portion, of the nickel portion, of the vanadium portion, of the copper portion, of the tin portion, of the calcium portion, of the titanium portion, of the boron portion, of the niobium portion, of the arsenic portion, of the tungsten portion and of the nitrogen portion. 
     In a simple advantageous refinement of the present invention, the structure observer determines at least one of the variables yield point, proof stress, tensile strength, elongation at fracture, hardness, transition temperature, anisotropy and consolidation index of the steel to be examined as a function of the portion of carbon in the steel or of the carbon equivalent or of the useful and/or pollutant portions. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 shows the change in the structure of steel during rolling; 
     FIG. 2 shows the integration of a structure optimizer into the control of a rolling train; 
     FIG. 3 shows a structure observer; 
     FIG. 4 shows an alternative embodiment of a structure observer; 
     FIG. 5 shows a further alternative embodiment of a structure observer; 
     FIG. 6 shows the use of genetic algorithms in a structure optimizer. 
    
    
     DETAILED DESCRIPTION 
     FIG. 1 shows the change in the structure of steel during rolling. The steel runs into the rolling train with a structure according to block  1 . After passing through the first roll stand, grains stretched by rolling have been formed in accordance with block  2 . In this state, so-called recovery occurs, during which dislocations and hence stress within individual grains of the structure are reduced. Due to recrystallization, new, low-dislocation grains are formed, starting from the grain boundaries, as indicated by block  3 . The recrystallization is designated as dynamic recrystallization or as static recrystallization depending on whether new grains are formed while the material is still in the roll stand or only thereafter. Subsequently, following the recrystallization and depending on the temperature, grain growth occurs, larger grains growing at the expense of smaller grains, as in block  4 . Iteration loop  6  sketches the use of a plurality of roll stands in a rolling train, or the repeated passing of rolling stock through a reversing stand. The operation illustrated in blocks  2 ,  3  and  4  is repeated in principle during each rolling, however always starting from the microstructure following the preceding rolling operation. After rolling and subsequent cooling have been completed, a microstructure according to block  5  has been formed. This microstructure has specific material properties, such as specific values for yield point, proof stress, tensile strength, elongation at fracture, hardness, anisotropy and consolidation index. Starting from values defined in advance for yield point, proof stress, tensile strength, elongation at fracture, hardness, transition temperature, anisotropy and/or consolidation index of the metal, in particular steel or aluminum, a rolling train (and/or a continuous casting installation) is adjusted in such a way that, at the end, a microstructure having the desired values for yield point, proof stress, tensile strength, elongation at fracture, hardness, transition temperature, anisotropy and/or consolidation index is established. This is carried out with the aid of a structure optimizer, as illustrated in FIG.  2 . 
     In FIG. 2, reference numeral  15  designates a rolled strip in a rolling train  16 , the intention being for the material or working properties of the rolled strip following rolling to correspond to setpoint values  11  for the material or working properties. Control elements  17  are provided for influencing the rolling train. Furthermore, measuring instruments  18  are provided for measuring specific states of the rolling train. The operating parameters of rolling train  16 , which are set using control elements  17 , are ascertained by a structure optimizer  20 . Structure optimizer  20  has a structure observer  25  which ascertains the material or working properties to be expected of rolled strip  15  as a function of a standard roll-pass plan  10 , chemical analysis values  12  of rolled strip  15  and settings ascertained for rolling train  16  by an advance calculation  24 . Such a structure observer  25  is explained in more detail in FIGS. 3,  4  and  5 . Setpoint values  11  for the material or working properties are compared in a comparator  21  to the values ascertained by structure observer  25  for the material or working properties. If setpoint values  11  for the material or working properties and the values ascertained by structure observer  25  for the material and working properties do not agree sufficiently precisely, then path  26  is followed. In accordance with a selected optimization criterion, the operating parameters, in this case input temperature T ein , output temperature T aus  and the degrees of reduction φ i  of the individual roll stands are changed in a weighted variation  22 . The result of this weighted variation  22  is new setpoint values  23  for temperature T ein  of rolled strip  15  at the entry into rolling train  16 , for temperature T aus  of rolled strip  15  at the exit from rolling train  16 , as well as the degrees of reduction pi of the individual roll stands of rolling train  16 . On the basis of these setpoint values  23 , new settings for rolling train  16  are ascertained in an advance calculation  24 . This cycle is run through until values  25  ascertained by the structure observer have the desired degree of agreement with setpoint values  11  for the material or working properties. In this case, path  27  is followed, which sets control elements  17  according to the values ascertained in advance calculation  24 . Furthermore, an adaptation  13  of advance calculation  24  is provided, by which models on which advance calculation  24  is based are adapted as a function of measured values from measuring instruments  18  and a post-calculation  14 . In an advantageous, alternative refinement, provision is made for the input variables of structure observer  25  to be the operating parameters, i.e., T ein , T aus  and φ i  in the present case, instead of the settings calculated in advance calculation  24  for rolling train  16 . 
     Provision can likewise be made to use a structure optimizer corresponding to FIG. 2 to adjust a metallurgical plant composed of a hot rolling train and a cold rolling train, a metallurgical plant composed of a continuous casting installation, a hot rolling train and a cold rolling train, a metallurgical plant composed of a continuous casting installation and a hot rolling train, or a metallurgical plant composed of a continuous casting installation, a rolling train and a cooling section. For this purpose, appropriately expanded structure observers and a suitably increased number of operating parameters should be used. The present invention is also suitable for adjusting a rail rolling track section. 
     It is particularly advantageous to optimize further parameters, such as energy consumption or roll wear, simultaneously using structure optimizer  20 . 
     FIGS. 3,  4  and  5  show advantageous embodiments for a structure observer  25  from FIG.  2 . In FIG. 3, P B  designates the operating parameters and PM the material or working properties of a steel or aluminum. Reference numeral  50  designates a neural network which ascertains material or working properties P m , such as yield point, proof stress, tensile strength, elongation at fracture, hardness, transition temperature, anisotropy and/or consolidation index as a function of operating parameters P B . A neural network is described in for example German patent application DE 197 38 943. 
     FIG. 4 shows an alternative embodiment of a structure observer. This structure observer has a grain size model  51  and an analytical material model  52 . Details of this model can be learned from for example the article “ Recrystallization and grain growth in hot rolling ”, by C. M. Sellers and J. A. Whiteman, Material Science, March/April 1979, pages 187 through 193. Grain size model  51  ascertains the ferrite grain size d a  in the case of uncrystallized or only partly crystallized austenite as a function of operating parameters P B . Material model  52  ascertains material or working properties P M  as a function of the ferrite grain size d a  in the uncrystallized or only partly crystallized austenite, as well as of operating parameters P B . Operating parameters P B , which are used as input variables for grain size model  51  and material model  52 , are not necessarily identical. It is possible for different operating parameters to be used as input variables. 
     FIG. 5 shows a structure observer corresponding to FIG. 4, analytical material model  52  being replaced by a neural network  53 . Such a neural network  53  can be designed, for example, as described in for example German patent application 197 38 943, the ferrite grain size d a  in the uncrystallized or only partly crystallized austenite being provided as an additional input variable for the neural network described in for example German patent application 197 38 943. 
     Genetic algorithms can advantageously be used for the iterative determination of optimal setting or optimal operating parameters by a structure optimizer  20  according to FIG.  2 . 
     FIG. 6 shows in simplified form the procedure in the optimization with the aid of genetic algorithms. The optimization is carried out in such a way 
     that values for the parameters to be optimized are arranged in so-called genes  40  which, in turn, are assigned to individuals  41  of a so-called population; 
     that a specific number of individuals  41  forms a socalled initial population; 
     that a few or all of the values in the genes are changed by a random value, in particular a random value from a selection of normally distributed random numbers, so that the result is a changed population  34  (step  33  in FIG.  6 ); 
     that genes belonging together are combined on so-called chromosomes, which are inherited together during recombination; 
     that the individuals with their genes, i.e., the values for the corresponding parameters, are assessed by an optimization function; and 
     that based on this assessment (step  32  in FIG.  6 ), individuals are selected for a new population, statistical preference being given to individuals that satisfy the optimization function better than other individuals; 
     that remaining individuals  31  are no longer taken into account; 
     that the optimization cycle is repeated with new population  41  until a solution considered to be optimal is reached. 
     Transferred to the iteration loop in structure optimizer  20  illustrated in FIG. 2, the step  32  in FIG. 6 is implemented in comparator  21 , and the assessment is implemented in structure observer  25  in FIG.  2 . Steps  33  and  35  in FIG. 6 are implemented in weighted variation  32  in FIG.  2 . The parameters combined into the genes correspond, for example, to operating parameters T ein , T aus  and φ i  in FIG.  2 . It is particularly advantageous to include further parameters, in particular optimization criteria such as energy consumption or roll wear, in the optimization. The genes which correspond to these parameters must be provided accordingly. The further parameters are then optimized at the same time as the operating parameters.