CN116384160A - Continuous casting process simulation prediction method, system and application thereof - Google Patents
Continuous casting process simulation prediction method, system and application thereof Download PDFInfo
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Abstract
The invention belongs to the technical field of continuous casting of ferrous metallurgy, in particular to a simulation prediction method and a simulation prediction system for a continuous casting process and application thereof, wherein collaborative management of a parameter library, an algorithm library, a model library and a result library in a simulation process is realized through a unified user interaction interface, and an analysis means and a data support are provided for a plurality of fields such as process parameter optimization, internal quality monitoring, casting capacity development, new product development and the like of a continuous casting process through building a system architecture such as equipment parameter-steel grade parameter-process parameter-model parameter-online/offline simulation prediction calculation-result display-quality monitoring/temperature control/process parameter optimization/casting capacity development/new steel grade development, realizing simulation of temperature distribution of a casting machine, realizing prediction of casting blank segregation and macroscopic tissue characteristics.
Description
Technical Field
The invention relates to the technical field of ferrous metallurgy continuous casting, in particular to a continuous casting process simulation prediction method, a continuous casting process simulation prediction system and application of the continuous casting process simulation prediction method.
Background
The development of the steel production flow from experience drive to digitization and intellectualization is a necessary trend of high efficiency of steel production, and the digital simulation has obvious advantages in the aspects of cost reduction and efficiency enhancement, lean production, labor productivity improvement and the like of the continuous casting process. But is limited by the diversity of steel production equipment and the complexity of production links, is limited by the concern of steel enterprises on big data and digitalization, is limited by the lack of professional compound talents, the development difficulty of intelligent manufacturing progress and digital simulation systems in the steel industry is far greater than that of other manufacturing industries, the continuous casting process involves complicated heat transfer, mass transfer and solidification behaviors, the environment of the continuous casting process is in a high-temperature and airtight space, and the scientificity and accuracy of digital simulation of the continuous casting process are all the bottleneck links which are not broken through by metallurgical workers. The digital simulation result is slowly applied in the fields of optimization of technological parameters of casting technology, internal quality monitoring, capability development of casting machine, new product development and the like.
Disclosure of Invention
In order to solve the problems in the prior art, the main purpose of the invention is to provide a continuous casting process simulation prediction method, a continuous casting process simulation prediction system and application of the continuous casting process simulation prediction method.
In order to solve the technical problems, according to one aspect of the present invention, the following technical solutions are provided:
a continuous casting process simulation prediction method comprises the following steps:
s1, acquiring parameters and storing the parameters into a MongoDB database, wherein the parameters comprise equipment parameters, steel grade parameters, process parameters and model parameters;
s2, sending the simulation calculation task to a simulation management server, and starting a model in the simulation calculation server by the simulation management server to perform simulation calculation, wherein the model comprises a temperature simulation model, a segregation prediction model and a solidification structure prediction model;
s3, storing simulation calculation results into a MongoDB database;
s4, performing casting blank quality monitoring, temperature control calibration and process parameter optimization according to simulation calculation results.
As a preferable scheme of the continuous casting process simulation prediction method, the invention comprises the following steps: in the step S1, the actual equipment parameters and steel parameters of the continuous casting machine are read from an Oracle database; the device parameters include: continuous caster section, metallurgical length, crystallizer copper tube length, secondary cooling zone length, etc.; the steel grade parameters include: steel grade actual composition, physical parameters, etc.
As a preferable scheme of the continuous casting process simulation prediction method, the invention comprises the following steps: in the step S1, the actual technological parameters and model parameters of the continuous casting machine are read from an Oracle database; the technological parameters include: pulling speed, tundish temperature, cooling partition water quantity, electromagnetic stirring parameters and the like; the model parameters include: parameters of the temperature simulation model, parameters of the segregation prediction model and parameters of the solidification structure prediction model.
As a preferable scheme of the continuous casting process simulation prediction method, the invention comprises the following steps: in the step S1, if the process parameter and the model parameter have errors, the error parameter is directly modified on line at the HMI interface by means of manual input.
As a preferable scheme of the continuous casting process simulation prediction method, the invention comprises the following steps: in the step S2, a temperature simulation sub-model and a 3D turbulence area solute distribution sub-model are started simultaneously for simulation; after the temperature simulation is completed, starting a macroscopic tissue predictor model for simulation; and after the 3D turbulent flow region solute distribution simulation is completed, starting a 2D laminar flow region solute distribution sub-model to simulate.
As a preferable scheme of the continuous casting process simulation prediction method, the invention comprises the following steps: in the step S3, the result of the simulation calculation includes data and cloud image.
In order to solve the above technical problems, according to another aspect of the present invention, the following technical solutions are provided:
a continuous casting process simulation prediction system, comprising:
the database server comprises a Qracle database server and a MongoDB database server; the Qracle database server stores and manages equipment parameters, steel grade parameters, process parameters, model parameters and the like of the continuous casting machine; the MongoDB database server stores and inquires process parameters, model parameters, simulation results obtained by system simulation calculation and the like displayed from an HMI interface;
the HMI interface displays equipment parameters, steel components, physical parameters, process parameters and model parameters, updates (inputs, modifies and the like) the process parameters and the model parameters, and has the functions of starting simulation tasks, inquiring simulation progress and results;
the simulation management server is connected with the HMI interface and the simulation calculation server and is mainly used for receiving signals submitted by tasks of the HMI interface, transmitting the signals to the simulation calculation server, starting a model, starting calculation and feeding back relevant information calculated by the simulation server to the HMI interface;
the simulation calculation server starts the model to perform simulation calculation by receiving the service requirement from the simulation management server, and sends the relevant information of the simulation calculation to the simulation management server and the MongoDB database server through an interface program.
In order to solve the above technical problems, according to another aspect of the present invention, the following technical solutions are provided:
the continuous casting process simulation prediction method and/or the continuous casting process simulation prediction system are applied to the fields of casting blank quality monitoring and process parameter optimization.
The continuous casting process simulation prediction method and/or the continuous casting process simulation prediction system are applied to the field of development of the working capacity of a casting machine.
The continuous casting process simulation prediction method and/or the continuous casting process simulation prediction system are applied to the fields of development of new steel types and casting blank quality prediction.
The beneficial effects of the invention are as follows:
the invention provides a continuous casting process simulation prediction method, a system and application thereof, wherein the simulation system comprises a database server, an HMI interface, a simulation management server and a simulation calculation server, the collaborative management of a parameter library, an algorithm library, a model library and a result library in a simulation process is realized through a unified user interaction interface, and an equipment parameter-steel grade parameter-process parameter-model parameter-online/offline simulation prediction calculation-result display-quality monitoring/temperature control/process parameter optimization/casting capacity development/new steel grade development architecture is built, so that the simulation of the temperature distribution of a casting machine is realized, the prediction of casting blank segregation and macroscopic tissue characteristics is realized.
Detailed Description
The following description will be made clearly and fully with reference to the technical solutions in the embodiments, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a continuous casting process simulation prediction method, a continuous casting process simulation prediction system and application thereof, which can be applied to the fields of casting blank quality monitoring and process parameter optimization, the field of working capacity development of casting machines, the field of new steel grade development, casting blank quality prediction and the like.
According to one aspect of the invention, the invention provides the following technical scheme:
a continuous casting process simulation prediction method comprises the following steps:
s1, acquiring parameters and storing the parameters into a MongoDB database, wherein the parameters comprise equipment parameters, steel grade parameters, process parameters and model parameters;
s2, after submitting a calculation task at an HMI interface, the system sends the simulation calculation task to a simulation management server, the simulation management server starts a model in the simulation calculation server, and after endowing the calculation program for writing command streams with each parameter information, simulation calculation is carried out, wherein the model comprises a temperature simulation model, a segregation prediction model and a solidification structure prediction model;
s3, storing the simulation calculation result into a MongoDB database, displaying the simulation calculation result on an HMI interface, and displaying a column of viewable simulation calculation result on the HMI interface;
s4, performing casting blank quality monitoring, temperature control calibration and process parameter optimization according to simulation calculation results, and realizing application in the fields of casting blank quality monitoring and process parameter optimization, working capacity development of a casting machine, development of new steel types, casting blank quality prediction and the like.
Preferably, in the step S1, actual equipment parameters and steel parameters of the continuous casting machine are read from an Oracle database; the device parameters include: continuous caster section, metallurgical length, crystallizer copper tube length, secondary cooling zone length, etc.; the steel grade parameters include: steel grade actual composition, physical parameters, etc.
Preferably, in the step S1, actual process parameters and model parameters of the continuous casting machine are read from an Oracle database; the technological parameters include: pulling speed, tundish temperature, cooling partition water quantity, electromagnetic stirring parameters and the like; the model parameters include: parameters of the temperature simulation model, parameters of the segregation prediction model and parameters of the solidification structure prediction model.
Preferably, in the step S1, if the process parameter and the model parameter have errors, the error parameter is directly modified on line at the HMI interface by means of manual input.
Preferably, in the step S2, a temperature simulation sub-model and a 3D turbulence area solute distribution sub-model are simultaneously started for simulation; after the temperature simulation is completed, starting a macroscopic tissue predictor model for simulation; and after the 3D turbulent flow region solute distribution simulation is completed, starting a 2D laminar flow region solute distribution sub-model to simulate.
Preferably, in the step S3, the result of the simulation calculation includes data and a cloud image.
According to another aspect of the invention, the invention provides the following technical scheme:
a continuous casting process simulation prediction system, comprising:
the database server comprises a Qracle database server and a MongoDB database server; the Qracle database server stores and manages equipment parameters, steel grade parameters, process parameters, model parameters and the like of the continuous casting machine; the MongoDB database server stores and inquires process parameters, model parameters, simulation results obtained by system simulation calculation and the like displayed from an HMI interface;
the HMI interface displays equipment parameters, steel components, physical parameters, process parameters and model parameters, updates (inputs, modifies and the like) the process parameters and the model parameters, and has the functions of starting simulation tasks, inquiring simulation progress and results;
the simulation management server is connected with the HMI interface and the simulation calculation server and is mainly used for receiving signals submitted by tasks of the HMI interface, transmitting the signals to the simulation calculation server, starting a model, starting calculation and feeding back relevant information calculated by the simulation server to the HMI interface;
the simulation calculation server starts the model to perform simulation calculation by receiving the service requirement from the simulation management server, and sends the relevant information of the simulation calculation to the simulation management server and the MongoDB database server through an interface program.
According to another aspect of the invention, the invention provides the following technical scheme:
in actual production, the on-site continuous casting production is generally carried out by setting process parameters (drawing speed, cooling partition water quantity, electromagnetic stirring parameters and the like) according to the temperature of molten steel in a tundish and the superheat degree interval where the tundish temperature is, wherein the continuous casting process simulation prediction method and/or the continuous casting process simulation prediction system can simulate continuous casting processes under a plurality of superheat degree conditions, and according to data and cloud patterns of simulation results, the monitoring of the quality of the casting blank is realized, the temperature control calibration is realized, a proper process parameter matching mechanism is defined, and the narrow interval control of the process parameters is realized.
The continuous casting process simulation prediction method or the continuous casting process simulation prediction system is applied to the field of development of the working capacity of a casting machine, high-pulling-speed continuous casting is an important way for realizing high efficiency and green steel production flow, and the flow and heat transfer in a crystallizer, the thickness of a solidified blank shell, the flow and solidification process in a pasty area and the like can be greatly changed along with the lifting of the pulling speed. The continuous casting process simulation prediction method and/or the continuous casting process simulation prediction system can simulate the continuous casting process under different drawing speeds, predict the continuous casting production process according to the data of simulation results and cloud patterns, determine the rationality of process parameters under the high drawing speed condition, realize the efficient utilization of a casting machine and promote the core competitiveness of products.
The application of the continuous casting process simulation prediction method or the continuous casting process simulation prediction system in the fields of development of new steel grades and casting blank quality prediction is required to develop new steel grades to meet the requirements in order to improve the strength and the service life of the steel grades or to meet other special quality requirements of products. The continuous casting process simulation prediction method and/or the continuous casting process simulation prediction system can simulate the continuous casting process of steel grades with different components, predict the solidification process of molten steel and the quality of casting blanks under the condition of different components according to the data and cloud patterns of simulation results, provide guidance directions and data support for the development of new products, and improve the safety of the development and production of the new products.
The technical scheme of the invention is further described below by combining specific embodiments.
The continuous casting process simulation prediction system adopted by each embodiment of the invention comprises:
the database server comprises a Qracle database server and a MongoDB database server; the Qracle database server stores and manages equipment parameters, steel grade parameters, process parameters, model parameters and the like of the continuous casting machine; the MongoDB database server stores and inquires process parameters, model parameters, simulation results obtained by system simulation calculation and the like displayed from an HMI interface;
the HMI interface displays equipment parameters, steel components, physical parameters, process parameters and model parameters, updates (inputs, modifies and the like) the process parameters and the model parameters, and has the functions of starting simulation tasks, inquiring simulation progress and results;
the simulation management server is connected with the HMI interface and the simulation calculation server and is mainly used for receiving signals submitted by tasks of the HMI interface, transmitting the signals to the simulation calculation server, starting a model, starting calculation and feeding back relevant information calculated by the simulation server to the HMI interface;
the simulation calculation server starts the model to perform simulation calculation by receiving the service requirement from the simulation management server, and sends the relevant information of the simulation calculation to the simulation management server and the MongoDB database server through an interface program.
Example 1
A continuous casting process simulation prediction method comprises the following steps:
s1, acquiring parameters and storing the parameters into a MongoDB database, wherein the parameters comprise equipment parameters, steel grade parameters, process parameters and model parameters; the parameters are specifically as follows: LX82A cord steel, continuous casting bloom production, wherein the bloom section size is 390 mm multiplied by 300 mm, the superheat degree is 20-22 ℃, the cooling water quantity of a crystallizer is 3000L/min, the specific water quantity of a secondary cooling zone is 0.25L/kg, the initial carbon content of molten steel is 0.82 wt%, an electromagnetic stirring device (current/frequency: 750A/1.5 Hz) of the crystallizer is started, the pulling speed is 0.6 m/min (the pulling speed set by a continuous casting process table is 0.55 m/min), and offline calculation is started.
S2, after submitting a calculation task at an HMI interface, the system sends the simulation calculation task to a simulation management server, the simulation management server starts a model in the simulation calculation server, and after endowing the calculation program for writing command streams with each parameter information, simulation calculation is carried out, wherein the model comprises a temperature simulation model, a segregation prediction model and a solidification structure prediction model;
and S3, after calculation is finished, storing the data of the simulation result and a cloud picture (the position of a solidification end point is 20.69 and m from a meniscus, the equiaxial crystal proportion is 30.11%, the columnar crystal proportion is 54.86%, positive segregation of carbon elements exists in the center of a casting blank, the segregation degree is 1.14) into a MongoDB database, and displaying a column of the result in an HMI interface to view the simulation result.
According to the data of the simulation result and the cloud picture, the position of the solidification end point is found to be at a reasonable position from the meniscus 20.69 m, and the probability of defects such as stress cracks and the like of the casting blank is very low; the equiaxial crystal proportion is 30.11%, the requirement of the solidification structure proportion of the casting blank is met, and the isotropy index of the casting blank is good; the carbon element positive segregation with the segregation degree of 1.14 exists in the center of the casting blank, and the positive segregation is in a reasonable range, so that obvious negative effects on the processing performance of subsequent products can not be caused. When the steel (LX 82A cord steel) is continuously cast, the pulling speed is recommended to be increased from 0.55 m/min to 0.6 m/min, so that the high-efficiency utilization of the casting machine can be realized on the premise of ensuring the casting blank quality, and the application of a continuous casting process simulation prediction method and a casting process simulation system in the field of the working capacity of the casting machine is realized.
Example 2
A continuous casting process simulation prediction method comprises the following steps:
s1, acquiring parameters and storing the parameters into a MongoDB database, wherein the parameters comprise equipment parameters, steel grade parameters, process parameters and model parameters; the parameters are specifically as follows: bridge steel SWRS87B, continuous casting bloom production, bloom section size of 390 mm multiplied by 300 mm, pulling speed of 0.65 m/min, crystallizer cooling water quantity of 3150L/min, secondary cooling area specific water quantity of 0.27L/kg, molten steel initial carbon content of 0.87wt%, and starting an electromagnetic stirring device (current/frequency: 750A/1.5 Hz) of the crystallizer, wherein the superheat degree is 20 ℃, and on-line calculation is started.
S2, after submitting a calculation task at an HMI interface, the system sends the simulation calculation task to a simulation management server, the simulation management server starts a model in the simulation calculation server, and after endowing the calculation program for writing command streams with each parameter information, simulation calculation is carried out, wherein the model comprises a temperature simulation sub-model, a solute distribution sub-model and a macroscopic organization prediction sub-model;
and S3, after calculation is finished, storing the data of the simulation result and a cloud picture (the position of a solidification end point is away from a meniscus 22.51 m, the equiaxial crystal proportion is 37.40%, the columnar crystal proportion is 49.50%, positive segregation of carbon elements exists in the center of a casting blank, the segregation degree is 1.16) into a MongoDB database, and displaying a column of the result in an HMI interface to view the simulation result.
S4, finding that all indexes are in a reasonable range according to the data of the simulation result and the cloud picture, wherein the existing process parameters are reasonably matched, and the production can be continuously carried out by adopting the set secondary cooling section parameters.
Example 3
A continuous casting process simulation prediction method comprises the following steps:
s1, acquiring parameters and storing the parameters into a MongoDB database, wherein the parameters comprise equipment parameters, steel grade parameters, process parameters and model parameters; the parameters are specifically as follows: LX92A cord steel, continuous casting bloom production, wherein the bloom section size is 390 mm multiplied by 300 mm, the pulling speed is 0.60 m/min, the cooling water quantity of a crystallizer is 3000L/min, the specific water quantity of a secondary cooling zone is 0.26L/kg, the initial carbon content of molten steel is 0.92 wt%, an electromagnetic stirring device (current/frequency: 750A/1.5 Hz) of the crystallizer is started, the superheat degree is 25 ℃, and online calculation is started.
S2, after submitting a calculation task at an HMI interface, the system sends the simulation calculation task to a simulation management server, the simulation management server starts a model in the simulation calculation server, and after endowing the calculation program for writing command streams with each parameter information, simulation calculation is carried out, wherein the model comprises a temperature simulation sub-model, a solute distribution sub-model and a macroscopic organization prediction sub-model;
and S3, after calculation is finished, storing the data of the simulation result and a cloud picture (the position of a solidification end point is away from a meniscus 22.37 m, the equiaxial crystal proportion is 28.61%, the columnar crystal proportion is 56.09%, positive segregation of carbon elements exists in the center of a casting blank, the segregation degree is 1.25) into a MongoDB database, and displaying a column of the result in an HMI interface to view the simulation result.
S4, according to data of simulation results and cloud pictures, the fact that carbon element positive segregation with segregation degree of 1.25 exists in the center of a casting blank, and the segregation degree is high indicates that the existing process parameters are unreasonable to match. According to the multiple simulation calculation of a simulation system, when the pulling speed is reduced from 0.60 m/min to 0.57 m/min and the cooling water quantity of a crystallizer is increased from 3000L/min to 3150L/min, the segregation degree of the carbon element in the center of a casting blank is reduced to 1.25 to 1.17, and the segregation degree is within a reasonable range, so that obvious negative effects on the processing performance of subsequent products can not be caused. When the steel (LX 92A cord steel) is continuously cast, when the overheat is 25 ℃, the pulling speed is reduced from 0.60 m/min to 0.57 m/min, the cooling water quantity of the crystallizer is increased from 3000L/min to 3150L/min, the center segregation of the casting blank can be optimized, and reasonable matching of technological parameters is realized. The method for predicting the continuous casting process simulation is applied to the fields of casting blank quality monitoring and process parameter optimization.
The invention establishes a digitalized, visualized and intelligent continuous casting process digitalized simulation method and system, wherein the simulation system comprises a database server, an HMI interface, a simulation management server and a simulation calculation server, realizes the collaborative management of a parameter library, an algorithm library, a model library and a result library of a simulation process through a unified user interaction interface, establishes an architecture of 'equipment parameter-steel grade parameter-process parameter-model parameter-online/offline simulation prediction calculation-result display-quality monitoring/temperature control/process parameter optimization/casting machine capability development/new steel grade development', realizes the simulation of the temperature distribution of a casting machine, realizes the prediction of casting blank segregation and macroscopic organization characteristics, and can provide analysis means and data support for the fields of process parameter optimization, internal quality monitoring, casting machine capability development, new product development and the like of continuous casting flows through accurate model calculation and combining with actual industrial parameters.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the content of the present invention or direct/indirect application in other related technical fields are included in the scope of the present invention.
Claims (10)
1. The continuous casting process simulation prediction method is characterized by comprising the following steps of:
s1, acquiring parameters and storing the parameters into a MongoDB database, wherein the parameters comprise equipment parameters, steel grade parameters, process parameters and model parameters;
s2, sending the simulation calculation task to a simulation management server, and starting a model in the simulation calculation server by the simulation management server to perform simulation calculation, wherein the model comprises a temperature simulation model, a segregation prediction model and a solidification structure prediction model;
s3, storing simulation calculation results into a MongoDB database;
s4, performing casting blank quality monitoring, temperature control calibration and process parameter optimization according to simulation calculation results.
2. The simulation prediction method of continuous casting process according to claim 1, wherein in the step S1, actual equipment parameters and steel parameters of the continuous casting machine are read from an Oracle database; the device parameters include: continuous caster section, metallurgical length, crystallizer copper tube length, secondary cooling zone length; the steel grade parameters include: actual composition and physical parameters of the steel.
3. The simulation prediction method of continuous casting process according to claim 1, wherein in the step S1, actual process parameters and model parameters of the continuous casting machine are read from an Oracle database; the technological parameters include: pulling speed, tundish temperature, cooling partition water quantity and electromagnetic stirring parameters; the model parameters include: parameters of the temperature simulation model, parameters of the segregation prediction model and parameters of the solidification structure prediction model.
4. The continuous casting process simulation prediction method according to claim 1, wherein in the step S1, if there is an error in the process parameter and the model parameter, the error parameter is directly modified on line at the HMI interface by means of manual input.
5. The continuous casting process simulation prediction method according to claim 1, wherein in the step S2, a temperature simulation sub-model and a 3D turbulence area solute distribution sub-model are simultaneously started for simulation; after the temperature simulation is completed, starting a macroscopic tissue predictor model for simulation; and after the 3D turbulent flow region solute distribution simulation is completed, starting a 2D laminar flow region solute distribution sub-model to simulate.
6. The continuous casting process simulation prediction method according to claim 1, wherein in the step S3, the result of the simulation calculation includes data and cloud image.
7. A continuous casting process simulation prediction system for implementing the continuous casting process simulation prediction method as claimed in any one of claims 1 to 6, comprising:
the database server comprises a Qracle database server and a MongoDB database server; the Qracle database server stores and manages equipment parameters, steel grade parameters, process parameters and model parameters of the continuous casting machine; the MongoDB database server stores and inquires the simulation results obtained by the process parameters, the model parameters and the system simulation calculation displayed from the HMI interface;
the HMI interface displays equipment parameters, steel components, physical parameters, process parameters and model parameters, updates the process parameters and the model parameters, and has the functions of starting simulation tasks, inquiring simulation progress and results;
the simulation management server is connected with the HMI interface and the simulation calculation server and is mainly used for receiving signals submitted by tasks of the HMI interface, transmitting the signals to the simulation calculation server, starting a model, starting calculation and feeding back relevant information calculated by the simulation server to the HMI interface;
the simulation calculation server starts the model to perform simulation calculation by receiving the service requirement from the simulation management server, and sends the relevant information of the simulation calculation to the simulation management server and the MongoDB database server through an interface program.
8. Use of the continuous casting process simulation prediction method according to any one of claims 1 to 6 and/or the continuous casting process simulation prediction system according to claim 7 in the field of casting blank quality monitoring and process parameter optimization.
9. Use of the continuous casting process simulation prediction method of any one of claims 1 to 6 and/or the continuous casting process simulation prediction system of claim 7 in the field of development of the working capacity of a casting machine.
10. Use of the continuous casting process simulation prediction method according to any one of claims 1 to 6 and/or the continuous casting process simulation prediction system according to claim 7 in the field of new steel grade development and casting blank quality prediction.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101727530A (en) * | 2010-01-07 | 2010-06-09 | 冶金自动化研究设计院 | System for realizing simulation of continuous casting process and method thereof |
| CN102228971A (en) * | 2011-06-30 | 2011-11-02 | 中冶南方工程技术有限公司 | Method for online simulation of molten steel solidification heat-transfer process inside continuous casting crystallizer |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101727530A (en) * | 2010-01-07 | 2010-06-09 | 冶金自动化研究设计院 | System for realizing simulation of continuous casting process and method thereof |
| CN102228971A (en) * | 2011-06-30 | 2011-11-02 | 中冶南方工程技术有限公司 | Method for online simulation of molten steel solidification heat-transfer process inside continuous casting crystallizer |
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|---|
| 梅康元 等: ""连铸生产线智能化技术研究"" * |
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