CN116384159A - A method and system for continuous casting process temperature simulation and macrostructure prediction - Google Patents

Abstract

The invention belongs to the technical field of continuous casting of ferrous metallurgy, in particular to a method and a system for continuous casting process temperature simulation and macroscopic tissue prediction, which are characterized in that parameters are obtained and stored in a MongoDB database, wherein the parameters comprise steel parameters, equipment parameters, process parameters and model parameters; setting a model according to the parameters stored in the MongoDB database, and starting a temperature simulation sub-model to perform temperature simulation calculation; after the temperature simulation calculation is completed, starting a macroscopic tissue prediction sub-model to perform macroscopic tissue prediction calculation; and storing the calculation result into a MongoDB database, and displaying the result data and the cloud image on an HMI interface. The calculation result provides effective data result support for temperature control calibration, macroscopic tissue control and casting machine working capacity development in the continuous casting process, analysis means and multi-sample data are provided, and the method has a great application prospect.

Description

A method and system for continuous casting process temperature simulation and macrostructure prediction

technical field

The invention relates to the technical field of iron and steel metallurgical continuous casting, in particular to a method and system for continuous casting process temperature simulation and macrostructure prediction.

Background technique

During the continuous casting production process, the molten steel is in a high-temperature state for a long time, and many complex physical phenomena such as heat transfer, solidification, and thermal deformation continue to occur in the continuous casting machine. The above physical phenomena, temperature distribution, slab shell thickness and uniformity in the machine will directly affect the shape, characteristics and proportion of the macroscopic structure of the final slab. Therefore, accurate and meticulous temperature control is the basis and basis for continuous casting stable production. The essential. The macrostructure mainly includes equiaxed crystals and columnar crystals. Steel types such as oriented silicon steel and electrical steel are expected to obtain a larger proportion of columnar crystal regions in order to improve magnetic permeability, wear resistance, and corrosion resistance. Steel types such as bearing steel and high-strength steel In order to improve hardness, strength, and plasticity, it is hoped to obtain a larger proportion of equiaxed crystal regions. Different steel types need to formulate different continuous casting processes. Metallurgists often rely on laboratory experiments and factory tests, and use trial and error methods to carry out a large number of repeated verification work in order to obtain a specific macrostructure, but this will inevitably lead to Excessive investment and great waste of capital, human resources and other resources.

Contents of the invention

In order to solve the problems existing in the prior art, the main purpose of the present invention is to propose a method and system for continuous casting process temperature simulation and macrostructure prediction.

In order to solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

A method for continuous casting process temperature simulation and macrostructure prediction, comprising the following steps:

S1. Obtain parameters and store the parameters in the MongoDB database, the parameters include steel parameters, equipment parameters, process parameters, and model parameters; calculate the physical parameters of the steel under the current conditions according to the steel parameters;

S2. Perform model setting according to the parameters stored in the MongoDB database, start the temperature simulation sub-model for temperature simulation; after the temperature simulation is completed, start the macro-tissue prediction sub-model for macro-tissue prediction;

S3. Store the temperature simulation and macroscopic structure prediction results in the MongoDB database to complete a temperature simulation and macroscopic structure prediction.

As a preferred solution of the method for continuous casting process temperature simulation and macrostructure prediction according to the present invention, wherein: after the step S3, it also includes,

S4. According to the temperature simulation and macroscopic structure prediction results stored in the MongoDB database, carry out the temperature control calibration of the slab, the macroscopic structure control, and the development of the working capacity of the casting machine.

As a preferred solution of the method for continuous casting process temperature simulation and macrostructure prediction according to the present invention, wherein: in the step S1, the steel grade parameters include steel grade, composition, and temperature information, which are derived from continuous casting The data measured and recorded by the refining furnace with the same furnace number.

As a preferred solution of the method for continuous casting process temperature simulation and macrostructure prediction in the present invention, wherein: in the step S1, the physical parameters of the steel include: thermal conductivity, density, specific heat capacity, stress-strain, phase Change parameters.

As a preferred solution of the method for continuous casting process temperature simulation and macrostructure prediction according to the present invention, wherein: in the step S1, the Oracle database has stored equipment parameters, process parameters, and model parameters in advance, and the equipment is obtained from the Oracle database. parameters, process parameters, model parameters.

As a preferred solution of the method for continuous casting process temperature simulation and macrostructure prediction according to the present invention, wherein: in the step S1, when there is a deviation in the parameters obtained from the Oracle database, the HMI interface can be manually input Online input, modification and viewing, etc.

As a preferred solution of the method for continuous casting process temperature simulation and macrostructure prediction in the present invention, wherein: in the step S1, after the parameters are acquired, the parameters will be displayed on the HMI interface.

As a preferred solution of the method for continuous casting process temperature simulation and macrostructure prediction according to the present invention, wherein: in the step S2, the size and algorithm of the model are set according to the equipment parameters; the initial conditions of the model are set according to the process parameters , boundary conditions, etc.; set the model monitor, iteration step size, iteration steps, etc. according to the model parameters.

As a preferred solution of the method for continuous casting process temperature simulation and macrostructure prediction in the present invention, wherein: in the step S2, the relevant information of the model calculation progress will be fed back to the model service manager in real time, and then fed back to the HMI interface, the HMI will display the progress of the calculation in real time.

As a preferred solution of the method for temperature simulation and macrostructure prediction of a continuous casting process according to the present invention, wherein: in the step S3, the temperature simulation results include: temperature distribution nephogram, solidified liquid core nephogram, solidified slab shell growth curve , temperature change curve, etc.; macroscopic tissue prediction results include: macroscopic tissue ratio, macroscopic tissue topography, etc.

In order to solve the above technical problems, according to another aspect of the present invention, the present invention provides the following technical solutions:

A system for continuous casting process temperature simulation and macrostructure prediction that implements the method for continuous casting process temperature simulation and macrostructure prediction.

An information data processing terminal for realizing the above method of continuous casting process temperature simulation and macrostructure prediction.

A computer-readable storage medium, including instructions, which, when run on a computer, cause the computer to execute the above method for continuous casting process temperature simulation and macrostructure prediction.

The beneficial effects of the present invention are as follows:

The present invention proposes a method and system for continuous casting process temperature simulation and macroscopic structure prediction, by obtaining parameters and storing the parameters in the MongoDB database, the parameters include steel type parameters, equipment parameters, process parameters, and model parameters; The parameters of the MongoDB database are set for the model, and the temperature simulation sub-model is started for temperature simulation calculation; after the temperature simulation calculation is completed, the macro-tissue prediction sub-model is started for macro-tissue prediction calculation; the calculation results are stored in the MongoDB database, and the results are displayed on the HMI interface Data and cloud map. The method and system have a positive effect and significance on the development of the continuous casting process towards digitization, visualization and intelligence, and realize the connection of information such as molten steel temperature and composition between the continuous casting process and the refining process. Temperature control calibration, macroscopic tissue control, and casting machine working ability development provide support for effective data results, provide analysis methods and multi-sample data, and have great application prospects.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained according to the structures shown in these drawings without creative effort.

Fig. 1 is a schematic flow chart of the method of the present invention.

The realization of the purpose of the present invention, functional characteristics and advantages will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Detailed ways

A clear and complete description will be made below in conjunction with the technical solutions in the embodiments. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

The invention proposes a method and system for continuous casting process temperature simulation and macroscopic structure prediction, which has positive effects and significance on the development of continuous casting process towards digitization, visualization and intelligence, and realizes the temperature of molten steel between the continuous casting process and the refining process The connection of information such as composition, composition, etc. provides effective data and result support for temperature control calibration and macroscopic tissue control in the continuous casting process through accurate online calculations, and provides analytical means and solutions for the development of casting machine working capabilities and other applications through accurate offline calculations. Multi-sample data. The HMI interface uses screens to display the cloud map of temperature distribution at different positions of the casting machine, the cloud map of solidified liquid core, the growth curve of solidified slab shell, the temperature change curve, the macroscopic structure diagram, and the macroscopic structure ratio (columnar crystal and equiaxed crystal ratio) wait.

According to one aspect of the present invention, the present invention provides following technical scheme:

As shown in Figure 1, a method for continuous casting process temperature simulation and macrostructure prediction includes the following steps:

S1. Obtain parameters and store the parameters in the MongoDB database, the parameters include steel parameters, equipment parameters, process parameters, and model parameters; calculate the physical parameters of the steel under the current conditions according to the steel parameters;

S2. Perform model setting according to the parameters stored in the MongoDB database, start the temperature simulation sub-model for temperature simulation; after the temperature simulation is completed, start the macro-tissue prediction sub-model for macro-tissue prediction;

S3. Store the temperature simulation and macroscopic structure prediction results in the MongoDB database to complete a temperature simulation and macroscopic structure prediction.

Preferably, after the step S3, it also includes,

S4. According to the temperature simulation and macroscopic structure prediction results stored in the MongoDB database, carry out the temperature control calibration of the slab, the macroscopic structure control, and the development of the working capacity of the casting machine.

Preferably, in the step S1, the steel grade parameters include steel grade, composition, and temperature information, which are derived from the data measured and recorded by the refining furnace with the same furnace number as the continuous casting.

Preferably, in the step S1, the physical parameters of the steel type include: thermal conductivity, density, specific heat capacity, stress-strain, and phase transformation parameters.

Preferably, in the step S1, the Oracle database has previously stored equipment parameters, process parameters, and model parameters, and the equipment parameters, process parameters, and model parameters are obtained from the Oracle database.

Preferably, in the step S1, when the parameters obtained from the Oracle database deviate, online input, modification and viewing can be performed on the HMI interface through manual input.

Preferably, in the step S1, after the parameters are acquired, the parameters will be displayed on the HMI interface.

Preferably, in the step S2, the size, algorithm, etc. of the model are set according to the equipment parameters; the initial conditions, boundary conditions, etc. of the model are set according to the process parameters; the monitor, iteration step size, number of iteration steps, etc. of the model are set according to the model parameters . Among them, "iteration step size × number of iteration steps = simulation time", "simulation time = metallurgical length of continuous casting machine/casting speed".

Preferably, in the step S2, the relevant information of the model calculation progress will be fed back to the model service manager in real time, and then fed back to the HMI interface, and the HMI will display the calculation progress in real time.

Preferably, in the step S3, the temperature simulation results include: temperature distribution cloud map, solidified liquid core cloud map, solidified billet shell growth curve, temperature change curve, etc.; macroscopic structure prediction results include: macroscopic structure ratio, macroscopic structure topography, etc. .

According to another aspect of the present invention, the present invention provides the following technical solutions:

A system for continuous casting process temperature simulation and macrostructure prediction that implements the method for continuous casting process temperature simulation and macrostructure prediction.

An information data processing terminal for realizing the above method of continuous casting process temperature simulation and macrostructure prediction.

A computer-readable storage medium, including instructions, which, when run on a computer, cause the computer to execute the above method for continuous casting process temperature simulation and macrostructure prediction.

The technical solutions of the present invention will be further described below in conjunction with specific embodiments.

Example 1

A method for continuous casting process temperature simulation and macrostructure prediction, comprising the following steps:

S1. Obtain parameters and store the parameters in the MongoDB database. The parameters include steel parameters, equipment parameters, process parameters, and model parameters; steel parameters include steel grades (spring steel 55SiCr), composition (the composition of spring steel 55SiCr is C 0.55wt%, Si 1.45wt%, Mn 0.7wt%, Cr 0.7wt%, V 0.7wt%, Ni 0.35wt%, Cu 0.25wt%), temperature information, which comes from refining with the same furnace number as continuous casting The data measured and recorded by the furnace; the physical parameters of the steel type under the current conditions are calculated according to the steel type parameters; the Oracle database stores the equipment parameters, process parameters, and model parameters in advance, and the equipment parameters, process parameters, and model parameters are obtained from the Oracle database; There is no deviation in the parameters obtained by the Oracle database; the above parameters are displayed on the HMI interface;

S2. Perform model setting according to the parameters stored in the MongoDB database, start the temperature simulation sub-model for temperature simulation; after the temperature simulation is completed, start the macro-tissue prediction sub-model for macro-tissue prediction;

1) Submit the calculation task on the HMI interface, send the calculation task signal to the model service manager, and the model service manager starts the calculation model;

2) The MongoDB database assigns device parameters to the written APDL command flow, inserts parameter information into the calculation model, and sets the size and algorithm of the model;

3) The MongoDB database assigns the process parameters to the APDL command flow written, inserts the parameter information into the calculation model, and sets the initial conditions and boundary conditions of the model;

4) The MongoDB database assigns the model parameters to the APDL command flow written, inserts the parameter information into the calculation model, and sets the model monitor, iteration step size, number of iteration steps, etc.; where "iteration step size × number of iteration steps = simulation time" , "Simulation time = metallurgical length of continuous casting machine / casting speed". The metallurgical length of the continuous casting machine is 25 m, the casting speed is 0.6 m/min, and the simulation time is 2500 s.

5) When all the parameters are implanted into the model, the model service manager starts the temperature simulation sub-model for calculation, and when the model calculation time meets the simulation time, the temperature simulation sub-model calculation is completed;

6) After the temperature simulation calculation is completed, the model service manager stops the calculation of the temperature simulation sub-model, starts the calculation of the macro-tissue prediction sub-model, and when the model calculation time meets the simulation time, the macro-tissue prediction sub-model calculation is completed;

7) The relevant information of the model calculation progress will be fed back to the model service manager in real time, and then fed back to the HMI interface, and the HMI will display the progress of the simulation calculation in real time;

S3. Store the temperature simulation and macroscopic tissue prediction results in the MongoDB database;

1) On the HMI interface, you can view the simulation and prediction result data. The distance between the solidification end point of the slab and the meniscus is 17.39 m; The return temperature at the junction of the third zone is 62.3°C, and the return temperature at the junction of the third zone and the fourth zone in the second cooling section is 147.2°C; the proportion of equiaxed crystals is 47.50%, and the proportion of columnar crystals is 35.60%;

2) Store the calculation results in the MongoDB database.

Application of the method of continuous casting process temperature simulation and macrostructure prediction in this embodiment:

The above-mentioned temperature simulation and macrostructure prediction are realized by online calculation, and the above-mentioned temperature simulation and macrostructure prediction results are used for billet temperature control calibration and macrostructure control: the calculation results show that the proportion of equiaxed grains is 47.50%, which is in line with that of spring steel 55SiCr macro structure ratio requirements. However, the recovery temperature at the junction of the third zone and the fourth zone of the second cooling section is 147.2°C, and the standard for the return temperature criterion of the second cooling section is generally required to be less than 100°C. The return temperature at the junction exceeds the standard, and the risk of defects such as cracks increases. Therefore, it is necessary to modify the cooling water volume in the third and fourth zones of the secondary cooling section to improve the excessive return temperature phenomenon. After reducing the cooling water volume of the third zone of the secondary cooling section from the original 16.9L/min to 16.4 L/min, and increasing the cooling water volume of the fourth zone of the secondary cooling section from the original 31.0 L/min to 32.4 L/min, the secondary cooling section The return temperature at the junction of the third zone and the fourth zone becomes 93.2°C, which is in line with the standard for the return temperature criterion of the second cooling section. In the continuous casting process, the modified parameters of the secondary cooling section can be directly used for production.

The above-mentioned temperature simulation and macrostructure prediction are realized by offline calculation, and the above-mentioned temperature simulation and macrostructure prediction results are used for the development of the working capacity of the casting machine: the calculation results show that the distance between the solidification end point of the slab and the meniscus is 17.39 m, but the continuous The metallurgical length of the casting machine is 25m, which means that the steel type does not match the maximum working capacity of the continuous casting machine. Therefore, design multiple sets of casting speed and superheat condition process schemes, carry out off-line calculation of continuous casting process, and predict the process of continuous casting production. According to the data and cloud images of the simulation results of multiple schemes, it is found that the equiaxed crystal ratio is 57.64%, meeting the quality requirements, the return temperature at the junction of each zone in the secondary cooling section is less than 100°C, which meets the standard of the return temperature criterion in the secondary cooling section, and the distance between the solidification end point of the slab and the meniscus has increased from 17.39 m to 22.07 m m, effectively developed the working capacity of the continuous casting machine and continuous casting production efficiency. The optimal process parameter combination obtained by off-line calculation can be directly used in the continuous casting production of spring steel 55SiCr.

Example 2

A method for continuous casting process temperature simulation and macrostructure prediction, comprising the following steps:

S1. Acquire parameters and store them in the MongoDB database. The parameters include steel type parameters, equipment parameters, process parameters, and model parameters; steel type parameters include steel grade (bridge cable steel SWRS87B), composition (bridge cable steel SWRS87B) The composition is C 0.87wt%, Si 0.3wt%, Mn 0.75wt%, Cr 0.28wt%, P 0.015wt%, S 0.01wt%, V 0.75wt%, Al 0.22wt%, Ni 0.02wt%), temperature information , which is derived from the data measured and recorded by the refining furnace with the same furnace number as the continuous casting; the physical parameters of the steel type under the current conditions are calculated according to the steel type parameters; the Oracle database has stored equipment parameters, process parameters, and model parameters in advance. The database obtains equipment parameters, process parameters, and model parameters; the crystallizer cooling water volume in the process parameters exported from the Oracle database is 3050 L/min, but the cooling water volume temporarily used during on-site production is 3150 L/min, so in the HMI interface, through Manually input the cooling water volume from the original 3050 L/min to 3150 L/min online; the above parameters are displayed on the HMI interface;

S2. Perform model setting according to the parameters stored in the MongoDB database, start the temperature simulation sub-model to perform temperature simulation; after the temperature simulation calculation is completed, start the macro-tissue prediction sub-model to perform macro-tissue prediction;

1) Submit the calculation task on the HMI interface, send the calculation task signal to the model service manager, and the model service manager starts the calculation model;

2) The MongoDB database assigns device parameters to the written APDL command flow, inserts parameter information into the calculation model, and sets the size and algorithm of the model;

3) The MongoDB database assigns the process parameters to the APDL command flow written, inserts the parameter information into the calculation model, and sets the initial conditions and boundary conditions of the model;

4) The MongoDB database assigns the model parameters to the APDL command flow written, inserts the parameter information into the calculation model, and sets the model monitor, iteration step size, number of iteration steps, etc.; where "iteration step size × number of iteration steps = simulation time" , "Simulation time = metallurgical length of continuous casting machine / casting speed". The metallurgical length of the continuous casting machine is 25 m, the casting speed is 0.65 m/min, and the simulation time is 2307.69 s.

5) When all the parameters are implanted into the model, the model service manager starts the temperature simulation sub-model for calculation, and when the model calculation time meets the simulation time, the temperature simulation sub-model calculation is completed;

6) After the temperature simulation calculation is completed, the model service manager stops the calculation of the temperature simulation sub-model, starts the calculation of the macro-tissue prediction sub-model, and when the model calculation time meets the simulation time, the macro-tissue prediction sub-model calculation is completed;

7) Information about the progress of the model calculation will be fed back to the model service manager in real time, and then fed back to the HMI interface, and the HMI will display the progress of the simulation calculation in real time.

S3. Store the temperature simulation and macroscopic tissue prediction results in the MongoDB database;

1) On the HMI interface, you can view the simulation and prediction result data. The distance between the solidification end point of the slab and the meniscus is 20.17 m; The return temperature at the junction of the third zone is 57.3°C, and the return temperature at the junction of the third zone and the fourth zone in the second cooling section is 92.2°C; the proportion of equiaxed crystals is 37.40%, and the proportion of columnar crystals is 49.50%;

2) Store the calculation results in the MongoDB database.

Application of the method of continuous casting process temperature simulation and macrostructure prediction in this embodiment:

The above-mentioned temperature simulation and macrostructure prediction are realized by online calculation, and the above-mentioned temperature simulation and macrostructure prediction results are used for the temperature control calibration and macrostructure control of the slab: the calculation results show that the proportion of equiaxed crystals is 37.40%, which is in line with the bridge cable Requirements for steel SWRS87B macrostructure ratio. At the same time, the return temperature at the junction of each zone in the second cooling section is less than 100°C, which meets the standard for the return temperature criterion of the second cooling section. During the continuous casting process, the set process parameters can continue to be used for production.

As can be seen from the foregoing embodiments, the present invention stores parameters into the MongoDB database by obtaining parameters, and the parameters include steel type parameters, equipment parameters, process parameters, and model parameters; model settings are carried out according to the parameters stored in the MongoDB database, and start The temperature simulation sub-model performs temperature simulation calculations; after the temperature simulation calculation is completed, the macro-structure prediction sub-model is started to perform macro-structure prediction calculations; the calculation results are stored in the MongoDB database, and the resulting data and cloud images are displayed on the HMI interface. The method and system have a positive effect and significance on the development of the continuous casting process towards digitization, visualization and intelligence, and realize the connection of information such as molten steel temperature and composition between the continuous casting process and the refining process. Temperature control calibration, macroscopic tissue control, and casting machine working ability development provide support for effective data results, provide analysis methods and multi-sample data, and have great application prospects.

The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Under the inventive concept of the present invention, the equivalent structural transformation made by using the content of the description of the present invention, or directly/indirectly used in other related All technical fields are included in the patent protection scope of the present invention.

Claims (10)

1. A method for continuous casting process temperature simulation and macrostructure prediction, characterized in that, comprising the steps: S1. Obtain parameters and store the parameters in the MongoDB database, the parameters include steel parameters, equipment parameters, process parameters, and model parameters; calculate the physical parameters of the steel under the current conditions according to the steel parameters; S2. Perform model setting according to the parameters stored in the MongoDB database, start the temperature simulation sub-model for temperature simulation; after the temperature simulation is completed, start the macro-tissue prediction sub-model for macro-tissue prediction; S3. Store the temperature simulation and macroscopic structure prediction results in the MongoDB database to complete a temperature simulation and macroscopic structure prediction. 2. the method for continuous casting process temperature simulation and macrostructure prediction according to claim 1, is characterized in that, also comprises after described step S3, S4. According to the temperature simulation and macroscopic structure prediction results stored in the MongoDB database, carry out the temperature control calibration of the slab, the macroscopic structure control, and the development of the working capacity of the casting machine. 3. the method for continuous casting process temperature simulation and macrostructure prediction according to claim 1, is characterized in that, in described step S1, Oracle database has stored equipment parameter, process parameter, model parameter in advance, obtains equipment from Oracle database parameters, process parameters, model parameters. 4. The method for temperature simulation and macrostructure prediction of continuous casting process according to claim 1, characterized in that, in the step S2, the size and algorithm of the model are set according to the equipment parameters. 5. The method for continuous casting process temperature simulation and macrostructure prediction according to claim 1, characterized in that, in the step S2, initial conditions and boundary conditions of the model are set according to process parameters. 6. The method for continuous casting process temperature simulation and macrostructure prediction according to claim 1, characterized in that, in the step S2, the monitor, the iteration step size, and the number of iteration steps of the model are set according to the model parameters. 7. The method for temperature simulation and macrostructure prediction of continuous casting process according to claim 1, characterized in that, in the step S3, the temperature simulation results include: temperature distribution nephogram, solidified liquid core nephogram, solidified slab shell growth curve , Temperature change curve; macroscopic tissue prediction results include: macroscopic tissue ratio, macroscopic tissue topography map. 8. A system for continuous casting process temperature simulation and macrostructure prediction for implementing the method for continuous casting process temperature simulation and macrostructure prediction according to any one of claims 1-7. 9. An information data processing terminal for realizing the method for continuous casting process temperature simulation and macrostructure prediction according to any one of claims 1-7. 10. A computer-readable storage medium, comprising instructions, which, when run on a computer, cause the computer to execute the method for temperature simulation and macrostructure prediction of a continuous casting process according to any one of claims 1-7.

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