Process and plant for the production of a continuously cast metallic product

Abstract

The invention relates to a method for producing a continuously cast metallic product, in which metallic melt is produced and provided in a sub-process, in which a metal strand is cast in a subsequent sub-process, and in which the cast metal strand is subjected to a further process in a subsequent sub-process, wherein the sub-processes are calculated and / or controlled or regulated by respective sub-process models.To operate such a process with improved product quality and production volume, the sub-process models are linked to a higher-level overall process optimization system. This overall process optimization system interacts with the sub-process models and determines whether an optimized target value for a production parameter can be used in production. If so, the system initiates the production of the continuously cast metallic product with the optimized target value. Furthermore, the invention relates to a plant for the production of a continuously cast metallic product.

Description

The invention relates to a method for producing a continuously cast metallic product, in which metallic melt is produced and provided in a part process, in which a metal strand is cast in a subsequent part process, and in which the cast metal strand is subjected to a further process in a further subsequent part process, wherein the part processes are calculated and / or controlled by respective part process models. The invention further relates to a plant for producing a continuously cast metallic product.The steel production in a corresponding plant (e.g. Nexus plant or CSP plant) as well as the hot processing in a slab or billet plant consists of several process steps. Separate process models are provided for each of the individual process steps, which process models carry out the calculation and the control and / or regulation of the corresponding sub-process. The calculation results and results of measurements are passed on from a sub-process to the subsequent sub-process and taken into account there. The overall process can comprise the following sub-processes:a converter process calculated and controlled by a converter model,a pan process calculated and controlled from a pan model,a distributor process calculated and controlled by a distributor model,a casting process calculated and controlled from a casting model,a furnace process calculated and controlled from a furnace model,a pre-road process calculated and controlled by a pre-road model,a pre-band cooler process calculated and controlled by a pre-band cooler model,a hot rolling mill process calculated and controlled from a hot rolling mill model,a cooling process calculated and controlled by a cooling model,a coiling process calculated and controlled from a coiling model,a cold rolling mill process calculated and controlled from a cold rolling mill model,a galvanizing process calculated and controlled from a galvanizing model.Each individual thread model controls or regulates the corresponding thread according to separate specifications and in this case has at most knowledge of the respectively preceding thread. A higher-order optimization of the entire production process does not take place.WO 2022 / 223297 A1 describes a process optimizer which provides only a connection between the casting machine and the rolling mill. This is intended to achieve an optimum strand thickness for achieving maximum overall production.From EP 2 346 625 B2 it is known to optimize the residual melt in the distributor.The object of the invention is to configure a process of the generic type and a corresponding plant such that it becomes possible overall to achieve an improved product quality of the metallic product. Furthermore, the production quantity should be optimally adaptable. Finally, the production costs should also be minimized.The solution of this object by the invention is characterized according to the method in that, in the case of the procedure described at the beginning, it is provided that the thread models are connected to a superordinate overall process optimization system, wherein a) the overall process optimization system directs a request (requested thread model) to a thread model in order to obtain from it an optimized target variable for a production parameter for the thread controlled or regulated by this thread model, b) the requested thread model determines a possible optimized target variable for the thread monitored by this, and returns this to the overall process optimization system, c) the overall process optimization system directs the request with the returned optimized target variable to at least a first thread model upstream or downstream of the requested thread model, whether an operation of the sub-process controlled or regulated by this sub-process model is possible with the optimized target variable, and the overall process optimization system with the fed-back optimized target variable directs the request to at least one second sub-process model upstream or downstream of the requested sub-process model, whether an operation of the sub-process controlled or regulated by this sub-process model is possible with the optimized target variable, d) in the case that the requests according to step c) are answered positively by the first and second upstream or downstream sub-process models: Prompt the production of the continuously cast metallic product with the optimized target variable.The first thread model is preferably a thread model downstream of the requested thread model, the second thread model is preferably a thread model upstream of the requested thread model.The causing of the production according to step d above) preferably comprises the steps:confirming the optimized target size to the requested thread model,changing the sub-process on the basis of the optimized target size and confirming it to the overall process optimization system,specifying the optimized target variable to the first and second sub-process models by the overall process optimization system.Each thread model preferably comprises:an online model which calculates and / or controls or regulates the sub-process,an offline model for calculating at least one production parameter; andan optimizer which determines optimum values for production parameters calculated by the offline model.In this case, it is preferably provided that the offline model calculates the subprocess at a speed which corresponds to at least 10 times the real time, preferably at least 50 times the real time.The thread process model is preferably assigned a material model or preferably comprises such a material model. In this case, it is preferably provided that a single material model is assigned to all the thread process models.The requested thread process model according to step a above) is preferably a continuous casting model for continuous casting.However, the upstream part-process model is preferably a converter model for the converter process and / or a pan model for the provision of melt and / or a distributor model for the distribution of the melt.The downstream part-process model is preferably a furnace model for the operation of a furnace. It can also be a rolling model for rolling the metallic product, in particular a rough rolling model for rolling in a rough rolling mill and / or a hot rolling mill model for hot rolling the metallic product and / or a cold rolling mill model for cold rolling the metallic product.Furthermore, the downstream part-process model can also be a model for a pre-strip cooler and / or a cooling model and / or a coiling model. Finally, it is also possible for the downstream part-process model to be a model for a galvanizing plant.By using the overall process optimization system, ordered customer requirements of the metallic product and in particular of the desired metal strip can also be dealt with in an improved manner. Therefore, a development of the invention provides that the request of the overall process optimization system to a thread process model according to step a above) takes place as a function of a request for the metallic product to be produced, in particular for the metal strip, wherein the request relates in particular to the geometry of the metal strip and / or the quality of the metal strip.The proposed method can also be carried out in particular using scrap containing copper, wherein it is then preferably provided that the overall process optimization system optimizes the alloy to be produced and / or the cooling, in particular the intensive cooling, of the metal strip to be produced as a function of the strip thickness to be produced.The plant for producing a continuously cast metallic product, preferably for carrying out the method described above, comprising a subprocess in which metallic melt is produced and provided, a subsequent subprocess in which a metal strand is cast, and a further subsequent subprocess in which the cast metal strand is subjected to a further process, wherein the subprocesses have respective subprocess models which are designed for the calculation and / or control or regulation of the subprocess, and wherein a superordinate overall process optimization system is arranged which is connected to the subprocess models, characterized in that a) the overall process optimization system is designed to direct a request to a subprocess model (requested subprocess model), in order to obtain from this a target value optimized for a production parameter for the subprocess controlled or regulated by this subprocess model, b) wherein the requested subprocess model is designed to determine a possible optimized target value for the subprocess monitored by this and to feed it back to the overall process optimization system, c) wherein the overall process optimization system is designed to direct with the fed back optimized target value at least to a first subprocess model upstream or downstream of the requested subprocess model the request as to whether with the optimized target value operation of the subprocess controlled or regulated by this subprocess model is possible, and the overall process optimization system is designed to direct with the fed back optimized target value at least to a second subprocess model, In order to direct the query to the pre- or post-stored thread model whether the operation of the thread controlled or regulated by this thread model is possible with the optimized target variable, d), wherein the overall process optimization system is designed to cause the production of the continuously cast metallic product with the optimized target variable to take place in the event that the queries according to step c) are answered positively by the pre-stored and post-stored thread model.Each thread model preferably comprises:an online model which is designed to calculate and / or control or regulate the sub-process,an offline model, which is configured to calculate at least one production parameter, andan optimizer configured to determine optimum values for the production parameters calculated by the offline model.The thread process model is preferably assigned a material model or comprises such a model. Again, preferably only a single material model is assigned to all the thread process models.The requested thread process model is preferably a continuous casting model for continuous casting.The upstream part-process model is preferably a converter model for the converter process and / or a pan model for the provision of melt and / or a distributor model for the distribution of the melt.The downstream part-process model is preferably a furnace model for the operation of a furnace, a rolling model for rolling the metallic product, in particular a preliminary train model for rolling in a preliminary train and / or a hot rolling mill model for hot rolling the metallic product and / or a cold rolling mill model for cold rolling the metallic product, a model for a pre-strip cooler, a cooling model, a coiling model and / or a model for a galvanising installation.The invention also comprises a computer program for carrying out the method described above.The proposed concept thus provides that a superordinate process chain optimizer monitors, controls and optimizes the individual sub-processes in order to optimize the product quality, the production quantity and the product costs.Each sub-process has a separate model for calculating, regulating and / or controlling the respective current process (online model). A material model is assigned to this. This material model is preferably used for all the sub-processes. In addition, each thread has an offline model. It is thereby possible to calculate whether, for example, a property (temperature, speed, strand geometry or microstructure property) desired by the overall optimizer can be achieved with otherwise currently present process parameters.Furthermore, each thread has its own optimizer, which notifies the overall optimizer until how far the process speed can be changed, for example.This results in some advantages which result from a continuous consideration of the process chain, in particular for the production of hot-rolled strip or cold strip:Center segregation in the metal sheet in the case of boron-alloyed steels can be reduced by casting close to liquidus.In ferritic steels, tensile crystallinity at the cold strip surface can be avoided by a high globular fraction in the primary structure of the slabs. The globular structure proportion can be influenced by less overheating during casting; this can be adjusted in the steel mill. The use of stirrers in strand guidance can increase the globular structure fraction; too low a globeitic structure fraction can be increased by the use of a stirrer even in the event of higher superheating.An increase in the casting and rolling speed is possible. By means of a higher casting and rolling speed, the ordering temperature of the melts in the steel mill can be reduced. Too high temperatures of the melt lead to a reduction in the casting speed because of the risk of breakthroughs in the strand shell.Accordingly, the proposed superordinate optimization offers substantial advantages.In contrast to previously known solutions, the proposed procedure allows a connection to be established to all involved sub-processes and thus ensures overall optimized production. Not only when a predefined actual value deviates from the setpoint value can be reacted; rather, an optimized new production plan can be created and run by requesting a request for a plurality of, preferably all, other sub-processes and taking into account the result of the response (difference from the above-mentioned EP 2 346 625 B2).In addition, the expected properties of the slabs are transferred prematurely to the subsequent sub-processes (aggregates). As a result, for example, the furnace inlet temperature to be expected can be used for the premature control of the furnace temperatures.Exemplary embodiments of the invention are shown in the drawing. The following are shown: FIG. 1 schematically shows a process chain for the production of a steel strip, FIG. 2 schematically shows the process chain, details being given for a part process, FIG. 3 schematically shows the flow chart for the processing of a request by a process chain optimizer, FIG. 4 ashows schematically the procedure during a current production of a metal strip, FIG. 4 bshows schematically the procedure in the optimization of the end product, FIG. 5 ashows an optimization process concerning the material composition of the metal strip to be produced, wherein the copper equivalent is plotted against the strip thickness in the diagram shown, FIG. 5 bshows in the representation according to FIG. 5 athe influence of the cooling on the permitted copper equivalent, and FIG. 6 schematically shows a flow chart for optimizing the process in the casting machine.FIG. 1 outlines the process chain for the production of a stable strip in a casting-rolling plant. Liquid steel is first provided and distributed in a ladle, for which purpose a ladle model and a distributor model are used. The liquid steel is then cast in a continuous casting facility, which is monitored by a casting model.Furthermore, the heating of the cast slab is carried out in a first furnace and rolling in a preliminary train with subsequent heating of the metallic material in a second furnace. Respective models are also used for this purpose, which monitor the corresponding operation.Hot rolling, cooling and coiling are then carried out with subsequent calcining and galvanizing, which is in turn monitored by appropriate models.It can be seen in FIG. 1 that all models for monitoring and controlling or regulating the individual process steps are connected to a central optimization system (shown below the plant in FIG. 1 ), in which the optimization of the process chain takes place with early forwarding of the expected process values to other models in the sense of the above procedure.FIG. 2 schematically indicates for one of the sub-processes of the process chain, namely for a "process i" preceded and followed by other sub-processes, which individual elements are used for which purposes.Each process step ( 1- n) is assigned an online process model for the current process for calculating and regulating / controlling the current process i. Calculation values (e.g., calculated temperatures) are returned to the process chain optimizer. Furthermore, a material model for calculating the material values for the current analysis is assigned.For a future or process to be optimized, a process step optimizer, an offline process model and a material model are assigned. The process step optimizer creates the data set for calculation for the offline process model and can optimize a target size. For this purpose, the offline process model can be called up multiple times. For the current data set of the process step optimizer, the material model is called up with the future analysis. With this material data and the process values of the process step optimizer, a fast calculation (e.g. the temperatures) takes place. The material model is used to calculate the material values for future analysis.FIG. 3 schematically outlines the flow chart for the processing of a request by a process chain optimizer, as corresponds to an exemplary embodiment of the method described above. Here, the communication in a change request by the process chain optimizer (PKO) is illustrated using an example of a request to the continuous casting process.The process starts (at numeral "1") with a request from the process chain optimizer PKO to the continuous casting model SGM concerning a new desired target size (for example, a higher casting speed is desired).At numeral "2", the continuous casting model SGM is requested to the continuous casting computer / database SGR for limit values for the target variable and for possible secondary conditions (for example, it is possible to request which maximum casting speed is permitted for the current material at the current strand thickness).The sending back of the current limit values from the continuous casting computer / database SGR to the continuous casting model SGM for the current process conditions takes place at numeral "3" (for example, a currently maximum possible casting speed is transmitted).The continuous casting model SGM optimizes the target variable (for example by calculating the maximum possible casting speed, so that the solidification through remains in the supported continuous guide at the current process conditions such as material and casting temperature), as is indicated by the numeral "4".At numeral "5", the response of the continuous casting model SGM to the target size is made to the process chain optimizer PKO.The process chain optimizer PKO asks at numeral "6" the subsequent processes whether the target variable found is advantageous or possible (for example, it is necessary to request whether the furnace can heat the slabs produced fast enough). If this is the case, the process chain optimizer PKO asks the preceding processes at numeral "7" whether they can meet the new requirements (i.e. whether the steelwork can produce the ladle connection, for example.If this is also the case, the process chain-optimized PKO confirms the new target variable to the continuous casting model SGM at numeral "8".At numeral "9", the new target variable is sent from the continuous casting model SGM to the continuous casting computer SGR for the purpose of changing the plant control (for example, in this connection, the display of the new casting speed in "level 1 HMI" is carried out for the operator of the plant).If the operator agrees with the new target variable, the operator confirms the transfer from the continuous casting computer SGR to the continuous casting model SGM according to the numeral "10".Finally, at numeral "11", the confirmation of the new target size is made from the continuous casting model SGM to the process chain optimizer PKO.FIGS. 4 a / 4 b show by way of example how the final product can be optimized: the final product of a combined casting and rolling mill is the finished coil. For this purpose, there are customer requirements, for example for the strand thickness, the strand width, the material and for mechanical properties (such as tensile strength, yield strength and hardness). Consequently, for example, the strand geometry can only be changed to the extent that a predefined order is produced; accordingly, the strand width and the strand thickness cannot be changed arbitrarily.In order to achieve the specifications, the following process data must be matched to one another: the mass flow from the steel mill, the casting speed, the average temperature after the continuous casting plant (caster), the average temperature after the furnace or on the rolling mill and the rolling force.FIG. 4 a shows, first of all, the sequence of a current production by way of example.New ladles are constantly being produced in the steel mill, so that the casting machine (caster) can cast a strand width of 2,000 mm at a casting speed of 5 m / min (and for example a strand thickness of 120 mm) without stopping casting.The Caster online model computes the sump length and average temperature at the furnace entrance and sends these values to the process chain optimizer.The process chain optimizer sends the slab geometry and furnace inlet temperature to the furnace online model.The oven online model controls the oven such that with the current values of the caster, the oven exit temperature is greater than or equal to a minimum temperature for the rolling mill and sends the oven exit temperature to the process chain optimizer.The process chain optimizer sends the slab geometry and furnace exit temperature to the mill online model.From the furnace data, the mill online model calculates the required rolling forces and adjusts the stands to produce a 2 mm thick strip.If now, as shown in FIG. 4 b, less raw steel can be produced by problems in the steel mill, the casting speed must be reduced in the caster in order to ensure the ladle connection. As a result, the average temperature of the slabs falls after the caster and, depending on the length and output of the furnace, also the average temperature at the entry into the rolling mill. In order to be able to carry out the same decreases with the same width, a higher rolling force is required at a lower average temperature. If the maximum permissible rolling force is now exceeded, the decrease in the rolling mill must be reduced or the strand width must already be reduced in the box. For this purpose, it must be queried during production planning whether orders with a smaller strand thickness or strand width are also present for the current material.In this respect, FIG. 4 b shows numerals (1 to 10) for which the following is noted:According to number 1, a message is made from the steelwork optimizer to the process chain optimizer: 20% less steel available.According to number 2, the request from the process chain optimizer to the caster optimizer takes place: Which casting speed is still possible for the smaller amount of steel? The Caster optimizer calculates: With the same strand geometry, the ladle connection is ensured with a reduction in the casting speed from 5 to 4 m / min. The Caster offline model calculates the new oven inlet temperature. The Caster optimizer sends these values to the process chain optimizer.Referring to numeral 3, the process chain optimizer sends the new oven inlet temperature to the oven optimizer. This switch activates all burners in order to achieve a sufficiently high furnace outlet temperature. The oven offline model calculates the new oven exit temperature. The return is to the process chain optimizer.Referring to numeral 4, the process train optimizer sends the new furnace outlet temperature to the rolling mill optimizer: the rolling force is too high. Production of 2.5 mm thick tapes would be possible. Feedback is provided to process chain optimizers.According to numeral 5, the process chain optimizer inquires about the production plan: If there are orders for a strip thickness of 2.5 mm? Answer: No, but there are orders for a narrower bandwidth (1,500 instead of 2,000 mm).According to numeral 6, the process chain optimizer calculates that the mass flow of 2,000 mm strand width at a casting speed of 4 m / min corresponds to the same mass flow as 1,500 mm at 5.33 m / min.According to numeral 7, the process chain optimizer requests the Caster optimizer: if the sump tip is still in the plant at a casting speed of 5.33 m / min? Answer: No, but still at 5.2 m / min. The return is made to the process chain optimizer along with the new oven run-in temperature.According to numeral 8, the process chain optimizer requests the furnace optimizer: Is the furnace inlet temperature high enough? Answer: Yes; the feedback is made.According to number 9, the request from the process chain optimizer to the rolling mill optimizer is made: If the required rolling force can be applied: Answer: Yes; feedback has been made.According to numeral 10, after all the sub-processes can cope with their sub-task, the process chain optimizer sends the message to the Caster online model: decrease the strand width to 1,500 mm and increase the casting speed to 5.2 m / min.The sequence explained can be carried out fully automatically or, in another embodiment, must also be set by the Caster operator.FIGS. 5 aand 5 b illustrate further possible embodiments of the method.The composition of the analysis in the steel mill can also be optimized thereafter. In the so-called condition, the last possibility of an analysis change, some alloying elements can be added or reduced if necessary. Each material has a possible minimum and a possible maximum proportion for each alloying element. Within these limits, a change can take place.In this connection, the copper content in the material is also important:Especially when secondary scrap is used, the copper content of the melt produced is high. Depending on the amount of decrease in hot rolling, there are an upper and lower limit for the copper equivalent. With greater decreases, the stretching increases, as a result of which the "hot-shortness cracks" occurring after casting become harmless. However, with smaller decreases (correspondingly large band thickness), this can lead to a reduction in quality.The copper equivalent can be determined by, for example: (Cu: copper; Sn: tin; Sb: antimony; Ni: nickel)Thus, the copper equivalent can be reduced by supplying nickel. If the strip thickness to be rolled next is now known to the steel mill, the strip quality can already be improved in the steel mill by the supply of nickel. On the other hand, the expensive alloying element nickel can be saved if thinner strips are to be produced next.FIG. 5 ashows the profile of the copper equivalence over the strip thickness of the metal strip to be produced, wherein the influence of the alloying element nickel is illustrated.Since the process chain optimizer knows a strip thickness to be produced in the future, the nickel alloy can be optimized in the steel mill.By intensive cooling in the casting machine, grain refining takes place, whereby a higher copper equivalent is permitted.FIG. 5 bschematically illustrates the influence of intensive cooling on the allowed copper equivalent.On the other hand, active intensive cooling reduces the strand temperature in the caster. The process chain optimizer can then decide beforehand whether it is more favorable for a strip thickness to be produced to alloy more nickel or to switch on intensive cooling and to start up the burner output in the furnace. In the case of a ladle already produced, only an existing intensive cooling can be added at a high copper content or an order with a smaller strip thickness (corresponding to a higher decrease in the rolling mill) can be produced.FIG. 6 schematically illustrates, by way of example, the optimization of a strand thickness. In the figure, numerals 1 to 14 are indicated, to which reference is made below.According to number 1, the caster currently produces a strand thickness H=65 mm at a casting speed of 4.5 m / min.According to Number 2, a greater decrease would be advantageous in the rolling mill, for which reason the rolling mill optimizer requests the process chain optimizer whether a strand thickness of 70 mm can also be produced in the caster under the other current process values (casting speed, casting temperature, water quantities, water temperature... ).According to numeral 3, the process chain optimizer directs the request to the Caster optimizer to determine whether H=70 mm is also possible under the current process conditions.According to numeral 4, the Caster optimizer queries the Caster database for the maximum possible sump length.According to numeral 5, a maximum sump length of 8,100 mm is stored in the Caster database for the current material. In addition, the Caster optimizer receives the information that a sump length between 8.050 and 8.100 mm is also to be considered optimum. This interval is intended to have the effect that an optimum strand thickness with two decimal points, preferably with only one decimal point, is output as the optimum value.Referring to numeral 6, the Caster optimizer invokes the fast Caster offline model for the first time to calculate the sump length for a 70 mm line thickness with otherwise current process data.According to numeral 7, the fast Caster offline model calculates the developing sump length in a few seconds and sends it back to the Caster optimizer, e.g., 8.050 mm<=first sump length<=8.100 mm.The Caster optimizer sends the process chain optimizer that a strand thickness of H=70 mm can be cast.The process chain optimizer sends to the caster online model that a strand thickness of 70 mm is to be used in the current casting operation.Referring to numeral 8, if the first calculated sump length is too long, the Caster optimizer optimizes the strand thickness. In this case, it can proceed, for example, as follows: the actual thickness of 65 mm is possible, 70 mm is not possible.According to numeral 9, a new second calculation is made with a thickness of 67.5 mm.According to numeral 10, it is established that a second calculated sump length of 8,030 mm is too short. The Caster optimizer can determine further strand thicknesses using the interval-box method or better mathematical methods (Segand method, Muller method).According to numeral 11, if the Caster optimizer found a sump length between 8.050 and 8.100 mm (e.g., for a thickness of 69.2 mm), it returns to the process chain optimizer that a strand thickness of 70 mm is not possible, but already 69.2 mm.According to numeral 12, the process chain optimizer inquires the rolling mill optimizer whether a strand thickness of 69.2 mm would also be advantageous and should be produced.According to numeral 13, the mill optimizer checks whether this thickness is advantageous and sends the result back to the process chain optimizer.According to numeral 14, it may be first that no advantage can be achieved and thus the process chain optimizer does nothing more and waits for a next request. However, it may also be that a strand thickness of 69.2 mm provides advantages: in this case, the process chain optimizer sends to the caster online model that the strand thickness is to be increased to 69.2 mm.The new strand thickness can now be changed fully automatically or the Caster operator is indicated that a larger strand thickness provides advantages in the rolling mill; it then manually changes the thickness.The new strand thickness can be achieved by dynamically changed LCR and / or soft reduction adjustment.In this case, it is also possible to vary the casting speed or the water cooling in order to save energy: by varying the amounts of cooling water and / or the casting speed, the optimizer of the casting machine can place the sump length at the permitted end of the supported strand guide. This takes place as a function of the otherwise present current process parameters (overheating, strand thickness, water temperature, mold values etc.). At a maximum sump length, the string energy increases; the reheat unit requires less energy and thus also produces less CO 2- emissions. A higher casting speed reduces the transport time of the strand head between a scissors and the inlet into the reheating unit and thus reduces the energy loss due to the thermal radiation. The optimization of the casting speed can also be effected in an overall optimization together with the strand thickness.Since the process models can exchange data before the casting machine and from a possible downstream furnace by the higher-order process optimizer, the furnace inlet temperature optimized with the faster, second process model can be sent to the furnace at an early stage. This enables a targeted, predictive heating of the furnace zones. Changes in the current casting process can thus already be taken into account early in other plant components.During continuous casting, a casting break with a subsequent restart should be prevented. If there are now problems in the steel mill with the ladle connection, the optimizer of the casting machine can calculate the minimum possible mass flow with the aid of the minimum possible casting speed and transfer it to the higher-order process optimizer. There, it is then decided whether or not a mold break is to be performed. In the optimization, the casting speed and strand thickness must be selected such that the calculated sump peak lies behind the LCR zone in the casting direction and that the strand surface does not become too cold in the straightening region.With current problems in the rolling mill, the current production of the casting machine can be reduced such that no stopping of casting is necessary and that the furnace can temporarily store the slabs produced. Possibly, with a greater strand thickness, more material can be temporarily stored in the furnace in the current casting process.For this purpose, a uniform temperature distribution is also sought for improving quality:After the continuous casting plant, temperature differences can be present within a slab. By knowing the tolerances allowed in the rolling mill and the temperature compensation currently possible in the furnace, it is possible to try with the casting optimizer to place these temperature differences in the allowed range by optimizing the casting speed and the amount of water sprayed from the current casting.Hereafter, the structure is calculated and optimized as follows:To calculate and optimize the mechanical properties, the structural properties present after the casting process (such as solidification structures, precipitations, for example the formation and dissolution of carbides, austenite-ferrite conversion or the grain size) can be passed on from the casting computer to the superordinate process model.In addition, it is possible to modify the process parameters of the current casting process by the casting optimizer in such a way that the microstructure properties and thus also the mechanical properties of the currently produced strip are optimized.The hot use of slabs is well known and a measure for reducing the energy input during steel production. For the casting machine, it is generally determined in advance in a production plan which material is to be actually driven with which strand width, thickness, casting speed and cooling water quantity. By means of regulations, the amounts of water to be used for the actual casting or the average temperature at the furnace inlet can be varied.These fixed specifications, for example the strand thickness, are selected in advance such that they can be processed in any case in the subsequent process steps (reheating, hot rolling mill). The current process conditions will not be discussed here. Thus, for example, the current analysis (chemical composition) could deviate from the reference analysis of the material to be cast and other material properties could thereby form, or the currently present temperature of the melt can deviate from the planned target temperature, which leads to a change in the casting speed.With a real-time calculation, it is not possible to determine the necessary changes of the current process parameters for the casting process in advance and to determine the influence on the subsequent process steps.Accordingly, in the context of the present invention, provision is also made, according to a preferred embodiment, for the control or regulation of the current casting process to integrate a second process model and an additional casting optimizer into the existing online process model.The casting optimizer is constantly exchanging data with the superordinate process optimizer, which carries out the communication for production planning and the process models (steel mill, ladles and distributors) of the upstream process steps and to the downstream process steps (reheating unit, hot rolling mill, cooling sections, etc.). The target specifications required for the current casting process are sent-as explained above-from the process models to the higher-order process optimizer, which sends them to the optimizer of the casting machine.The casting optimizer additionally communicates with the online process model running in real time and a second process model computing faster than real time. In addition, it takes into account the casting regulations, which are stored in the database depending on the type of steel. The casting optimizer receives the target specifications (such as casting speed, temperature, outlet thickness, structure and precipitation state) required for the optimization from the superordinate process optimizer from the upstream and downstream processes and from the production plan.The second process model, computing faster than real time, can predict how the impact, for example, of a different chemical composition or a changed casting temperature or casting speed of the current casting process, will affect the goals and pass the results on to the casting optimizer. The same applies to requirements from the downstream process steps (such as a higher rolling speed or a higher forming temperature). The casting optimizer determines which casting parameters of the current casting must be changed to meet the target specification. The new casting parameters are checked by the second process model, which calculates more quickly than real time. If the target specification cannot be reached, the best possible solution is determined and forwarded from the casting optimizer to the higher-order process optimizer. Only after acceptance of the higher-order process optimizer are the casting parameters passed to the current casting process.The second process model, which calculates more quickly than real time, is capable of performing optimizations independently as well.The optimizer contains models with optimization strategies which set the current casting process such that the required target specifications are complied with in the subsequent process steps.Possible optimization strategies are, for example, zero-point search, the gradient method, linear and integer optimization, discrete optimization, nonlinear optimization and data mining.The following should be noted with regard to the possibilities of optimizing the casting process:In all optimizations, attention must be paid primarily to a safe casting operation: the sump tip must lie within the supported strand guide in order to prevent a waling (see in this regard the checked sump length in FIG. 6 ). The strand shell thicknesses in the upper plant part must be thick enough to prevent variations in the casting level. The surface temperatures must not be too low in the bending and straightening range to prevent surface cracks. Only when this is fulfilled can the casting process be further optimized.For this purpose, a variation of the strand thickness is possible for increasing production, as can be seen from the following explanations:Since, during hot use in a slab, CSP, Nexus or billet installation, the casting machine is generally the bottleneck in overall production, the material flow of the casting machine is of decisive importance.The mass flow (V*B*H*p) is limited by the maximum possible sump length. A constant sump length also corresponds to a constant K factor, as defined below:In this case, H is the strand thickness, B is the strand width, V is the casting speed and K is the K factor.A smaller strand thickness combined with a higher casting speed can lead to a higher production than a larger strand thickness at a constant sump length.Depending on the chemical composition present and the rolling forces required thereby in the rolling mill for thickness reduction, the higher-order process optimizer can thus calculate a desired strand thickness and thus optimize the overall production. This desired optimum strand thickness is sent to the optimizer of the casting machine. There it is decided whether it can be used for the casting process:In the positive case: use of the optimized strand thickness for the current casting.In the negative case: determination of a strand thickness that is the best possible for the current casting process and return to the higher-order process optimizer.The new strand thickness can be achieved by dynamically changed LCR and / or soft reduction adjustment.The following should be noted in the calculation and optimization of the mass flow:The mass flow is calculated (as explained above) from the product of strand width, strand height, casting speed and density. Since the density is a function of the temperature, the mass flow at the end of the casting machine can change on the basis of the current temperature control with the same strand cross section and the same casting speed. The mass flow must now be optimized for the current casting in such a way that the subsequent flow of ladle, distributor and immersion pipe is ensured. Likewise, the mass flow in the casting machine may need to be reduced in order to permit connection to the next ladle and not to generate a casting break.On account of problems in the subsequent rolling mill (for example due to a temporarily reduced rolling force), there may be specifications for reducing the current strand cross section.The following applies to the calculation and optimization of the energy flow:The energy flow is calculated from the mass flow multiplied by the enthalpy in J / s.With the mass flow MP, the enthalpy of the castle H Caster, the enthalpy of the furnace H Furnace and the efficiency of the furnace η Furnace the new furnace load is calculated as:This means that, for example, at a higher casting speed or greater thickness and the same temperature, the mass flow (MP) and thus the load on the subsequent furnace increases. Depending on the efficiency of the furnace, specifications can therefore be made for maintaining a maximum energy flow to the casting machine, before which the current process parameters of the current casting must be changed.Alternatively, the casting optimizer can also be used in production plants in which the cast steel is not processed further directly in a rolling mill.Depending on the current process parameters and the desired product quality, the slabs produced can pass through a cooling bed, a heating hood and / or a pit or can subsequently be stacked. The casting optimizer can optimize the process parameters of the current casting for the desired driving mode.

Claims

Method for producing a continuously cast metallic product, in which metallic melt is produced and provided in a thread process, in which a metal strand is cast in a subsequent thread process and in which the cast metal strand is subjected to a further process in a further subsequent thread process, wherein the thread processes are calculated and / or controlled by respective thread process models and wherein the thread process models are connected to a higher-order overall process optimization system, wherein a) the overall process optimization system directs a request to a thread process model (requested thread process model) in order to obtain from it an optimized target variable for a production parameter for the thread process controlled by this thread process model, b) the requested thread model determines a possible optimized target variable for the thread monitored by the same and returns the same to the overall process optimization system, c) the overall process optimization system uses the returned optimized target variable to direct the request to at least one first thread model upstream or downstream of the requested thread model, whether operation of the thread controlled or regulated by this thread model is possible with the optimized target variable, and the overall process optimization system uses the returned optimized target variable to direct the request to at least one second thread model upstream or downstream of the requested thread model, whether operation of the thread controlled or regulated by this thread model is possible with the optimized target variable, d) in the case of this, the queries according to step c) being answered positively by the first and the second upstream or downstream subprocess models: causing the production of the continuously cast metallic product with the optimized target size.Method according to Claim 1, characterized in that the first thread model is a thread model downstream of the requested thread model and the second thread model is a thread model upstream of the requested thread model.Method according to claim 1 or 2, characterized in that the initiation of the production according to step d) of claim 1 comprises the steps: - confirmation of the optimized target variable to the requested thread model, - changing the thread on the basis of the optimized target variable and confirmation thereof to the overall process optimization system, - specification of the optimized target variable to the first and second thread model by the overall process optimization system.Method according to one of Claims 1 to 3, characterized in that each part-process model comprises: - an online model which calculates and / or controls or regulates the part-process, - an offline model for calculating at least one production parameter, and - an optimizer which determines optimum values for production parameters calculated by the offline model.Method according to claim 4, characterized in that the offline model calculates the sub-process at a speed corresponding to at least 10 times the real time, preferably at least 50 times the real time.Method according to either of Claims 4 and 5, characterized in that a material model is assigned to the thread process model or this model comprises a material model.Method according to Claim 6, characterized in that a single material model is assigned to all the thread process models.Method according to any one of claims 1 to 7, characterized in that the requested thread model according to step a) of claim 1 is a continuous casting model for continuous casting.Method according to one of Claims 1 to 8, characterized in that the upstream part-process model is a converter model for the converter process and / or a pan model for the provision of melt and / or a distributor model for the distribution of the melt.Method according to one of Claims 1 to 9, characterized in that the downstream part-process model is a furnace model for the operation of a furnace.Method according to one of Claims 1 to 10, characterized in that the downstream part-process model is a rolling model for rolling the metallic product, in particular a preliminary rolling mill model for rolling in a preliminary rolling mill and / or a hot rolling mill model for hot rolling the metallic product and / or a cold rolling mill model for cold rolling the metallic product.Method according to one of Claims 1 to 11, characterized in that the downstream part-process model is a model for a pre-strip cooler and / or a cooling model and / or a coiling model.Method according to one of Claims 1 to 12, characterized in that the downstream part-process model is a model for a galvanising installation.Method according to one of Claims 1 to 13, characterized in that the request of the overall process optimization system to a part-process model according to step a) of Claim 1 takes place as a function of a request for the metallic product to be produced, in particular for the metal strip, wherein the request relates in particular to the geometry of the metal strip and / or to the quality of the metal strip.Method according to one of Claims 1 to 14, characterized in that the process is carried out using scrap containing copper, wherein the overall process optimization system optimizes the alloy to be produced and / or the cooling, in particular the intensive cooling, of the metal strip to be produced as a function of the strip thickness to be produced.Plant for producing a continuously cast metallic product, in particular for carrying out the method according to one of Claims 1 to 15, comprising a part-process in which metallic melt is produced and provided, a subsequent part-process in which a metal strand is cast, and a further subsequent part-process in which the cast metal strand is subjected to a further process, wherein the part-processes have respective part-process models which are designed for the calculation and / or control or regulation of the part-process, wherein a superordinate overall-process optimization system is arranged which is connected to the part-process models, wherein a) the overall-process optimization system is designed to direct a request to a part-process model (requested part-process model), in order to obtain from this a target variable optimized for a production parameter for the subprocess controlled or regulated by this subprocess model, b) the requested subprocess model is designed to determine a possible optimized target variable for the subprocess monitored by this, and to feed it back to the overall process optimization system, c) the overall process optimization system is designed to direct the request with the fed back optimized target variable to at least one first subprocess model upstream or downstream of the requested subprocess model, whether operation of the subprocess controlled or regulated by this subprocess model is possible with the optimized target variable, and the overall process optimization system is designed to direct the fed back optimized target variable to at least one second subprocess model, In order to direct the request to the pre- or post-stored thread model in front of the requested thread model whether the optimized target variable can be used to operate the thread controlled or regulated by this thread model, d) the overall process optimization system is designed to cause the production of the continuously cast metallic product with the optimized target variable to take place in the event that the requests according to step c) are answered positively by the pre-stored and post-stored thread model.Plant according to claim 16, characterised in that each part-process model comprises: - an online model which is designed to calculate and / or control or regulate the part-process, - an offline model which is designed to calculate at least one production parameter, and - an optimizer which is designed to determine optimum values for the production parameters calculated by the offline model.Plant according to Claim 17, characterized in that a material model is assigned to the thread process model or said model comprises a material model.Plant according to Claim 18, characterized in that a single material model is assigned to all the part-process models.Plant according to one of Claims 16 to 19, characterized in that the requested part-process model is a continuous casting model for continuous casting.Plant according to one of Claims 16 to 20, characterized in that the upstream part-process model is a converter model for the converter process and / or a pan model for the provision of melt and / or a distributor model for the distribution of the melt.Plant according to one of Claims 16 to 21, characterized in that the downstream part-process model is a furnace model for the operation of a furnace.Plant according to one of Claims 16 to 22, characterized in that the downstream part-process model is a rolling model for rolling the metallic product, in particular a preliminary rolling mill model for rolling in a preliminary rolling mill and / or a hot rolling mill model for hot rolling the metallic product and / or a cold rolling mill model for cold rolling the metallic product.Plant according to one of Claims 16 to 23, characterized in that the downstream part-process model is a model for a pre-strip cooler and / or a cooling model and / or a coiling model.Plant according to one of Claims 16 to 24, characterized in that the downstream part-process model is a model for a galvanising plant.A computer program comprising program instructions for performing the thread models and for communication with the overall process higher-order optimization system when performing the method according to any one of claims 1 to 15.

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