CN117947241A - LF (ladle furnace) intelligent refining system and method - Google Patents
LF (ladle furnace) intelligent refining system and method Download PDFInfo
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- 238000007670 refining Methods 0.000 title claims abstract description 104
- 238000000034 method Methods 0.000 title claims abstract description 64
- 238000009847 ladle furnace Methods 0.000 title abstract description 58
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 158
- 239000010959 steel Substances 0.000 claims abstract description 158
- 239000002893 slag Substances 0.000 claims abstract description 65
- 230000008569 process Effects 0.000 claims abstract description 44
- 238000006477 desulfuration reaction Methods 0.000 claims abstract description 32
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- 230000000694 effects Effects 0.000 claims abstract description 3
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- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 24
- 229910052717 sulfur Inorganic materials 0.000 claims description 24
- 239000011593 sulfur Substances 0.000 claims description 24
- 238000007726 management method Methods 0.000 claims description 17
- 238000005070 sampling Methods 0.000 claims description 16
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 claims description 15
- 239000011575 calcium Substances 0.000 claims description 15
- 229910052791 calcium Inorganic materials 0.000 claims description 15
- 238000003756 stirring Methods 0.000 claims description 12
- 230000033764 rhythmic process Effects 0.000 claims description 9
- 230000003009 desulfurizing effect Effects 0.000 claims description 8
- 239000000203 mixture Substances 0.000 claims description 7
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 239000001301 oxygen Substances 0.000 claims description 6
- 229910052760 oxygen Inorganic materials 0.000 claims description 6
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
- C21C7/00—Treating molten ferrous alloys, e.g. steel, not covered by groups C21C1/00 - C21C5/00
- C21C7/0075—Treating in a ladle furnace, e.g. up-/reheating of molten steel within the ladle
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- C21C7/00—Treating molten ferrous alloys, e.g. steel, not covered by groups C21C1/00 - C21C5/00
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- C21C2300/00—Process aspects
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Abstract
The invention belongs to the technical field of ferrous metallurgy, and particularly relates to an LF (ladle furnace) intelligent refining system and an LF intelligent refining method, which coordinate molten steel temperature control, slag formation and desulfurization control and molten steel component control, thereby realizing synchronous and accurate control of molten steel terminal temperature, slag formation and desulfurization effect and molten steel component, and realizing the design targets of energy conservation and consumption reduction. Meanwhile, the invention also has the overall operation rule of both the upstream and downstream of the smelting process, establishes and optimizes the outbound time and the temperature of LF refining production in real time according to the actual demand of continuous casting and pouring on the refining smelting period, performs overall planning on each secondary model, and realizes the collaborative matching and the efficient operation of LF-RH/VD-CC.
Description
Technical Field
The invention relates to the technical field of ferrous metallurgy, in particular to an LF intelligent refining system and an LF intelligent refining method.
Background
The LF refining furnace is a main process of steelmaking production, and the accuracy and precision of molten steel temperature control, alloy addition and bottom argon blowing control in the production directly influence the quality of molten steel and the smooth operation of the process. In actual production, the external refining production is judged by experience of staff for a long time, subjective impressions and changes in the history heat operation process are taken as references, slag making auxiliary materials of lower furnace steel and adding of alloy auxiliary materials are organized, smelting time is long, and operation deviation of different staff causes a certain amount of alloy and slag loss, so that the influence on field management and product quality is larger. When studying the optimization design problems of LF refining temperature control, desulfurization control and composition control, a learner mostly adopts an independent monomer optimization mode, but under the control of operating variables in the LF refining process, the temperature, composition and other quality index parameters of molten steel are mutually coupled, and as the action mechanisms are different, the coupling relations among various indexes are often contradictory, for example, slag forming desulfurization efficiency is influenced by the composition of molten steel and temperature, slag forming quantity can enhance desulfurization capacity, but electric arc thermal efficiency is influenced, heating efficiency is further influenced, alloy yield is influenced by the temperature of molten steel, and slag and alloy addition can cause temperature drop of molten steel, so that the traditional method cannot obtain an economic and reasonable process optimization scheme, and LF intelligent refining cannot be realized.
Disclosure of Invention
In order to solve the problems in the prior art, the main purpose of the invention is to provide an LF intelligent refining system and an LF intelligent refining method.
In order to solve the technical problems, according to one aspect of the present invention, the following technical solutions are provided:
An LF intelligent refining system comprising: a database management module and a model management module;
A database management module for managing material data required for calculation by the model management module including equipment, materials and process parameters;
The model management module is used for cooperatively managing molten steel temperature control, slagging and desulfurization control and a molten steel component control model, and the model for managing molten steel temperature control, slagging and desulfurization control and molten steel component control comprises the following components: a slagging desulfurization model, a heating control model, an alloy addition calculation model, a molten steel component prediction model, a bottom-blowing argon stirring model and a calcium treatment model;
The slagging desulfurization model is used for providing a slag charge addition amount and a charging combination according to conditions including the slag discharge amount of the converter, the refining slag turning amount and target slag system components of different steel grades and combining target control requirements to realize the control of the slagging desulfurization process;
the heating control model is used for determining the influence of factors including the molten steel temperature of molten steel entering a station, the grade of a large tank, the addition amount of alloy and slag, the refining period, the heating efficiency of a heating gear and the argon gas flow value of each stage of the process on the temperature;
the alloy addition calculation model is used for predicting the yield of each alloy element according to the components, temperature and oxygen determination of molten steel during converter tapping;
The molten steel component prediction model predicts the molten steel component through the alloy addition calculated by the alloy addition calculation model, compares the molten steel component with the steel grade target component, and feeds back the result to the alloy addition calculation model to adjust the alloy addition so as to ensure the hit of the end point component;
And controlling ladle bottom argon blowing equipment to perform bottom argon blowing according to a preset stage according to the process progress fed back by the refining process tracking model by the bottom argon blowing stirring model, and automatically adjusting the flow of argon.
The calcium treatment model is used for building a calcium line execution standard matrix according to the oxygen fixation, slag turning quantity, large tank grade and process path of the converter.
As a preferable scheme of the LF intelligent refining system, the invention comprises the following steps: the LF intelligent refining system further comprises: the rhythm control module analyzes operation steps of the refining process taking time sequence as an axial direction in advance and then divides LF refining into seven stages, wherein the steps are as follows: the method comprises a ladle falling stage, a slag melting stage, a heating stage, an alloying stage, a calcium treatment stage, a soft blowing stage and an outbound stage, wherein a ladle reaches a treatment position, a treatment position limit is triggered as a starting point, a treatment position leaving is taken as an end point, and a stage progress of the current heat is tracked and displayed; the module also calculates the outbound target temperature and the target moment of the LF furnace, and then establishes a reasonable heating plan according to the feedback of the target moment, the target temperature and the heating control model.
As a preferable scheme of the LF intelligent refining system, the invention comprises the following steps: the LF intelligent refining system further comprises: and the auxiliary function module is used for verifying the actual sampling components and the actual temperature of the molten steel in the refining process as a heating control model and a molten steel component prediction model, so as to realize automatic correction of the temperatures and the components.
As a preferable scheme of the LF intelligent refining system, the invention comprises the following steps: the equipment parameters comprise ladle parameters (ladle capacity, ladle inner diameter and inner cavity height, furnace lining thickness), argon blowing parameters (argon blowing hole number and diameter, bottom blowing hole included angle), electrode parameters (voltage gear and power grade), wire feeding parameters (wire feeder hole number and wire feeder number); the material parameters comprise slag forming material components and alloy material components; the technological parameters include the corresponding internal control range of steel grade, the target slag system component and the alloy yield.
As a preferable scheme of the LF intelligent refining system, the invention comprises the following steps: the slagging desulfurization model calculates the added alloy amount according to the molten steel station entering sulfur content and the alloy addition calculation model, calculates the sulfur content intake amount of the slagging material in the slag formation addition, calculates the total sulfur content intake amount of the smelting furnace number according to the real-time molten steel composition, refining slag condition, molten steel temperature, bottom blowing argon flow, molten steel amount and refining duration, calculates the steel-slag sulfur capacity, slag-steel sulfur component ratio and mass transfer coefficient of S, and predicts the molten steel sulfur content.
As a preferable scheme of the LF intelligent refining system, the invention comprises the following steps: the heating control model is combined with a big data algorithm to carry out theoretical quantification on the influence of all important operations or events in the LF period on the molten steel temperature, and the predicted value is compensated and corrected by combining with on-site temperature measurement analysis and is attached to actual production, so that the molten steel temperature is accurately predicted in real time.
As a preferable scheme of the LF intelligent refining system, the invention comprises the following steps: the alloy addition calculation model calculates the alloy addition by adopting a linear programming mode.
As a preferable scheme of the LF intelligent refining system, the invention comprises the following steps: and determining the argon flow of each stage preset by the bottom argon blowing stirring model according to the simulation result of ladle argon blowing numerical simulation.
As a preferable scheme of the LF intelligent refining system, the invention comprises the following steps: the calcium treatment model determines current heat feed parameters based on an execution standard matrix.
In order to solve the technical problems, according to one aspect of the present invention, the following technical solutions are provided:
an LF intelligent refining method adopts the LF intelligent refining system, which comprises the following steps:
s1, starting an LF intelligent refining system, initializing the system, and enabling a rhythm control module to judge whether a ladle arrives at a station;
S2, automatically starting a bottom blowing argon stirring model after the steel ladle arrives at a station, starting bottom blowing, performing bottom blowing argon according to set argon flow, and automatically adjusting subsequent argon flow according to a feedback stage process;
s3, automatically acquiring ladle basic information, including the current heat smelting number, the large tank grade, the smelting steel grade, the molten steel weight, the refining slag turning amount and the refining path;
S4, after the sampling and detecting result of the molten steel components is out, starting a slagging and desulfurizing model to calculate the addition amount of slag materials, wherein a charging system can obtain the calculation data of the slagging and desulfurizing model and finish weighing the slag materials, and finally adding the weighed slag materials into a ladle to realize molten steel desulfurization and white slag refining;
S5, according to the sampling detection result of the molten steel components, the alloy addition calculation model starts to calculate the addition amount of each alloy, the charging system can obtain the calculation data of the alloy addition calculation model and finish weighing and adding the alloy materials, and then the molten steel component prediction module predicts the molten steel components;
s6, according to the actual sampling components and temperature of the molten steel in the refining process, the actual sampling components and temperature are used as verification of a heating control model and a molten steel component prediction model, and automatic correction of the temperature and the components is achieved;
s7, the rhythm control module adjusts a heating plan according to the outbound target moment, the target temperature and feedback of the heating control model;
And S8, finally, comparing and judging the predicted result of the end point component and the temperature of the molten steel with the target outlet temperature and the steel grade component requirement, if the steel is qualified, finishing refining, executing outlet operation by the steel ladle, and if the steel is unqualified, continuing to circularly go on until the molten steel is qualified.
The beneficial effects of the invention are as follows:
The invention provides an LF intelligent refining system and an LF intelligent refining method, which coordinate molten steel temperature control, slag formation and desulfurization control and molten steel component control, thereby realizing synchronous and accurate control of molten steel terminal temperature, slag formation and desulfurization effect and molten steel component, and realizing the design targets of energy conservation and consumption reduction. Meanwhile, the invention also has the overall operation rule of both the upstream and downstream of the smelting process, establishes and optimizes the outbound time and the temperature of LF refining production in real time according to the actual demand of continuous casting and pouring on the refining smelting period, performs overall planning on each secondary model, and realizes the collaborative matching and the efficient operation of LF-RH/VD-CC.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an LF intelligent refining system according to the present invention.
Fig. 2 is a main logic schematic diagram of the LF intelligent refining method of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
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 an LF intelligent refining system and an LF intelligent refining method, which are used for controlling the temperature of molten steel, slagging and desulfurizing of the molten steel in process coupling relation. After temperature measurement and sampling, taking the inbound temperature as an initial value of a heating control model, and calculating and predicting the molten steel temperature in real time by the heating control model according to bottom argon blowing flow feedback, slag forming material addition and alloy addition; determining the alloy element yield according to the real-time molten steel temperature, and planning the addition of alloy; meanwhile, the slagging desulfurization model calculates steel-slag sulfur capacity, slag-steel sulfur component ratio, mass transfer coefficient of S and the like according to the real-time temperature and components of molten steel, so that the steel liquid sulfur content is predicted.
According to one aspect of the invention, the invention provides the following technical scheme:
As shown in fig. 1, an LF intelligent refining system includes: a database management module and a model management module;
the database management module builds a MySQL database for managing material data required by calculation of the model management module comprising equipment, materials and process parameters, can correct the material data according to the change of the charging structure, and is called during calculation;
The model management module is used for cooperatively managing molten steel temperature control, slagging and desulfurization control and a molten steel component control model, and the model for managing molten steel temperature control, slagging and desulfurization control and molten steel component control comprises the following components: a slagging desulfurization model, a heating control model, an alloy addition calculation model, a molten steel component prediction model, a bottom-blowing argon stirring model and a calcium treatment model;
The slagging desulfurization model is used for providing a slag charge addition amount and a charging combination according to conditions including the slag discharge amount of the converter, the refining slag turning amount and target slag system components of different steel grades and combining target control requirements to realize the control of the slagging desulfurization process;
the heating control model is used for determining the influence of factors including the molten steel temperature of molten steel entering a station, the grade of a large tank, the addition amount of alloy and slag, the refining period, the heating efficiency of a heating gear and the argon gas flow value of each stage of the process on the temperature;
the alloy addition calculation model is used for predicting the yield of each alloy element according to the components, temperature and oxygen determination of molten steel during converter tapping;
The molten steel component prediction model predicts the molten steel component through the alloy addition calculated by the alloy addition calculation model, compares the molten steel component with the steel grade target component, and feeds back the result to the alloy addition calculation model to adjust the alloy addition so as to ensure the hit of the end point component;
And controlling ladle bottom argon blowing equipment to perform bottom argon blowing according to preset stages (slag melting, heating, alloying, calcium treatment, soft blowing and the like) according to the process progress fed back by the refining process tracking model by the bottom argon blowing stirring model, and automatically adjusting the flow of argon.
The calcium treatment model is used for building a calcium line execution standard matrix according to the oxygen fixation, slag turning quantity, large tank grade and process path of the converter.
Preferably, the LF intelligent refining system further comprises: the rhythm control module analyzes operation steps of the refining process taking time sequence as an axial direction in advance and then divides LF refining into seven stages, wherein the steps are as follows: the method comprises a ladle falling stage, a slag melting stage, a heating stage, an alloying stage, a calcium treatment stage, a soft blowing stage and an outbound stage, wherein a ladle reaches a treatment position, a treatment position limit is triggered as a starting point, a treatment position leaving is taken as an end point, and a stage progress of the current heat is tracked and displayed; because the ladle undergoes different working procedures in the turnover process, the working procedures are mutually influenced, and the high-efficiency operation among the working procedures is ensured for saving the cost, and the module also calculates the outbound target temperature and the target moment of the LF furnace; for the direct upper casting process, calculating liquidus temperature according to the composition requirements of steel types, analyzing and obtaining a temperature drop model from LF refining to continuous casting based on a big data algorithm through historical temperature records of all links, and calculating the target temperature of LF tapping; for the process requiring over vacuum, a temperature drop model between VD/RH and LF is additionally built, and the target temperature of LF tapping is calculated. Calculating the pouring time of the residual molten steel according to the section and the pulling speed of the continuous casting steel billet and the total molten steel amount information to be poured in front of the steel in the furnace, establishing a turnover time model among all working procedures according to different process paths, further calculating the target time of LF tapping, and then making a reasonable heating plan (comprising heating time and heating gear selection) according to the feedback of the target time, the target temperature and a heating control model.
Preferably, the LF intelligent refining system further comprises: the auxiliary function module is used for verifying the actual sampling components and the actual temperature of the molten steel in the refining process as a heating control model and a molten steel component prediction model according to the influence of factors of site instability, so as to realize automatic correction of the temperatures and the components. And after the furnace is finished for 10-15 min, the material and energy consumption of the furnace are automatically collected, and the corresponding alloy, energy consumption, slag charge and other costs and ton steel cost are output according to the matching operation of the material price and the molten steel weight in the system, so that a reference is provided for field cost management and control.
Preferably, the equipment parameters comprise ladle parameters (ladle capacity, ladle inner diameter and inner cavity height, furnace lining thickness), argon blowing parameters (argon blowing hole number and diameter, bottom blowing hole included angle), electrode parameters (voltage gear, power grade) and wire feeding parameters (wire feeder hole number, wire feeder number); the material parameters comprise slag forming material components and alloy material components; the technological parameters include the corresponding internal control range of steel grade, the target slag system component and the alloy yield.
Preferably, the slagging desulfurization model calculates the added alloy amount according to the molten steel station entering sulfur content, the alloy addition calculation model and the sulfur content introduction amount calculation smelting furnace sulfur content introduction total amount in the slagging material addition amount according to the desulfurization slagging model, and calculates the steel-slag sulfur capacity, slag-steel sulfur component ratio and mass transfer coefficient of S according to real-time molten steel components, refining slag conditions, molten steel temperature, bottom blowing argon flow, molten steel amount and refining duration, thereby predicting the molten steel sulfur content.
Preferably, the heating control model combines a big data algorithm to theoretically quantify the influence of all important operations or events during LF on the molten steel temperature, and combines on-site temperature measurement analysis to compensate and correct the predicted value and attach to actual production, thereby realizing real-time accurate prediction of the molten steel temperature. The heating control model can effectively control the treatment process, reduce the frequency of electrifying and heating, improve the hit rate of the treatment end temperature, and simultaneously can be operated stably, and shorten the treatment period. And the module can formulate a reasonable heating plan (comprising heating times and heating gear selection) according to the outbound time of the refining target, the outbound temperature of the refining target and the feedback of a temperature forecast model.
Preferably, the alloy addition calculation model calculates the alloy addition in a linear programming mode, so that a more economical alloy addition mode is selected on the premise of meeting the component requirements of target steel types.
Preferably, the argon flow of each stage preset by the bottom argon blowing stirring model is determined according to the ladle argon blowing numerical simulation result.
Preferably, the calcium treatment model determines current heat feed parameters (feed line speed and feed amount) based on an execution standard matrix.
According to one aspect of the invention, the invention provides the following technical scheme:
as shown in fig. 2, an LF intelligent refining method, using an LF intelligent refining system as described above, includes the following steps:
s1, starting an LF intelligent refining system, initializing the system, and enabling a rhythm control module to judge whether a ladle arrives at a station;
S2, automatically starting a bottom blowing argon stirring model after the steel ladle arrives at a station, starting bottom blowing, performing bottom blowing argon according to set argon flow, and automatically adjusting subsequent argon flow according to a feedback stage process;
s3, automatically acquiring ladle basic information, including the current heat smelting number, the large tank grade, the smelting steel grade, the molten steel weight, the refining slag turning amount and the refining path;
S4, after the sampling and detecting result of the molten steel components is out, starting a slagging and desulfurizing model to calculate the addition amount of slag materials, wherein a charging system can obtain the calculation data of the slagging and desulfurizing model and finish weighing the slag materials, and finally adding the weighed slag materials into a ladle to realize molten steel desulfurization and white slag refining;
s5, according to the sampling detection result of the molten steel components, the alloy addition calculation model starts to calculate the addition amount of each alloy, the charging system can obtain the calculation data of the alloy addition calculation model and finish weighing and adding the alloy materials, then the molten steel component forecasting module predicts the molten steel components, the prediction result is uploaded to the primary control system in real time, and the automatic adjustment of the molten steel components is realized through the alloy addition calculation model;
s6, according to the actual sampling components and temperature of the molten steel in the refining process, the actual sampling components and temperature are used as verification of a heating control model and a molten steel component prediction model, and automatic correction of the temperature and the components is achieved;
s7, the rhythm control module adjusts a heating plan (comprising heating time and heating gear selection) according to the outbound target time, the target temperature and feedback of the heating control model;
And S8, finally, comparing and judging the predicted result of the end point component and the temperature of the molten steel with the requirements of the target outlet temperature and the steel grade component, if the steel is qualified, finishing refining, executing outlet operation by the steel ladle, and if the steel is unqualified, continuing to circularly process until the molten steel is qualified, and automatically generating refined report data of the current heat for subsequent checking and cost accounting 10-15min after the steel is finished.
By adopting the embodiment of the invention, intelligent refining is carried out on the LF furnace of a certain factory, wherein the temperature and S content of the LF refining outlet time which are predicted and actually measured by the 18 furnace application model are shown in the following table:
From the above table, the predicted outbound temperature is well matched with the target outbound temperature and the measured outbound temperature of the steel grade, and the predicted outbound S content is well matched with the measured outbound S content. The intelligent refining of the LF furnace can be realized by adopting the system and the method provided by the invention, and the invention coordinates and controls the temperature of molten steel, slagging and desulfurizing of which the process coupling relation exists, and the composition of molten steel. After temperature measurement and sampling, taking the inbound temperature as an initial value of a heating control model, and calculating and predicting the molten steel temperature in real time by the heating control model according to bottom argon blowing flow feedback, slag forming material addition and alloy addition; determining the alloy element yield according to the real-time molten steel temperature, and planning the addition of alloy; meanwhile, the slagging desulfurization model calculates steel-slag sulfur capacity, slag-steel sulfur component ratio, mass transfer coefficient of S and the like according to the real-time temperature and components of molten steel, so that the steel liquid sulfur content is predicted. Meanwhile, the invention also has the overall operation rule of both the upstream and downstream of the smelting process, establishes and optimizes the outbound time and the temperature of LF refining production in real time according to the actual demand of continuous casting and pouring on the refining smelting period, performs overall planning on each secondary model, and realizes the collaborative matching and the efficient operation of LF-RH/VD-CC.
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. An LF intelligent refining system, comprising: a database management module and a model management module;
A database management module for managing material data required for calculation by the model management module including equipment, materials and process parameters;
The model management module is used for cooperatively managing molten steel temperature control, slagging and desulfurization control and a molten steel component control model, and the model for managing molten steel temperature control, slagging and desulfurization control and molten steel component control comprises the following components: a slagging desulfurization model, a heating control model, an alloy addition calculation model, a molten steel component prediction model, a bottom-blowing argon stirring model and a calcium treatment model;
The slagging desulfurization model is used for providing a slag charge addition amount and a charging combination according to conditions including the slag discharge amount of the converter, the refining slag turning amount and target slag system components of different steel grades and combining target control requirements to realize the control of the slagging desulfurization process;
the heating control model is used for determining the influence of factors including the molten steel temperature of molten steel entering a station, the grade of a large tank, the addition amount of alloy and slag, the refining period, the heating efficiency of a heating gear and the argon gas flow value of each stage of the process on the temperature;
the alloy addition calculation model is used for predicting the yield of each alloy element according to the components, temperature and oxygen determination of molten steel during converter tapping;
The molten steel component prediction model predicts the molten steel component through the alloy addition calculated by the alloy addition calculation model, compares the molten steel component with the steel grade target component, and feeds back the result to the alloy addition calculation model to adjust the alloy addition so as to ensure the hit of the end point component;
And controlling ladle bottom argon blowing equipment to perform bottom argon blowing according to a preset stage according to the process progress fed back by the refining process tracking model by the bottom argon blowing stirring model, and automatically adjusting the flow of argon.
The calcium treatment model is used for building a calcium line execution standard matrix according to the oxygen fixation, slag turning quantity, large tank grade and process path of the converter.
2. The LF intelligent refining system of claim 1, wherein the LF intelligent refining system further comprises: the rhythm control module analyzes operation steps of the refining process taking time sequence as an axial direction in advance and then divides LF refining into seven stages, wherein the steps are as follows: the method comprises a ladle falling stage, a slag melting stage, a heating stage, an alloying stage, a calcium treatment stage, a soft blowing stage and an outbound stage, wherein a ladle reaches a treatment position, a treatment position limit is triggered as a starting point, a treatment position leaving is taken as an end point, and a stage progress of the current heat is tracked and displayed; the module also calculates the outbound target temperature and the target moment of the LF furnace, and then establishes a reasonable heating plan according to the feedback of the target moment, the target temperature and the heating control model.
3. The LF intelligent refining system of claim 2 wherein the LF intelligent refining system further comprises: and the auxiliary function module is used for verifying the heating control model and the molten steel component prediction model according to the components and the temperature of the actual molten steel sample in the refining process, so as to realize automatic correction of the temperature and the components.
4. The LF intelligent refining system of claim 1, wherein the equipment parameters include ladle parameters, argon blowing parameters, electrode parameters, wire feeding parameters; the material parameters comprise slag forming material components and alloy material components; the technological parameters include the corresponding internal control range of steel grade, the target slag system component and the alloy yield.
5. The LF intelligent refining system according to claim 1, wherein the slagging desulfurization model calculates an added alloy amount according to a molten steel incoming sulfur content, an alloy addition calculation model, and calculates a sulfur content introduction amount in the slag former addition amount according to a desulfurization slagging model, calculates a total amount of sulfur content introduction into the smelting furnace, calculates a steel-slag sulfur capacity, a slag-steel sulfur component ratio, and a mass transfer coefficient of S according to a real-time molten steel composition, a refining slag condition, a molten steel temperature, a bottom-blowing argon flow rate, a molten steel amount, and a refining duration, thereby predicting a molten steel sulfur content.
6. The LF intelligent refining system according to claim 1, where the heating control model combines with a big data algorithm to theoretically quantify the effect of all important operations or events during the LF on the molten steel temperature, and combines with on-site temperature measurement analysis to compensate and correct the predicted value and attach to the actual production, so as to implement real-time accurate prediction of the molten steel temperature.
7. The LF intelligent refining system of claim 1 in which the alloy addition calculation model calculates the alloy addition using linear programming.
8. The LF intelligent refining system of claim 1 wherein the magnitude of argon flow at each stage predetermined by the bottom blowing argon stirring model is determined based on ladle argon blowing numerical simulation results.
9. The LF intelligent refining system of claim 1 wherein the calcium-handling model determines current heat feed parameters based on an execution criteria matrix.
10. An LF intelligent refining method, characterized in that the LF intelligent refining system according to any one of claims 3-9 is used, comprising the steps of:
s1, starting an LF intelligent refining system, initializing the system, and enabling a rhythm control module to judge whether a ladle arrives at a station;
S2, automatically starting a bottom blowing argon stirring model after the steel ladle arrives at a station, starting bottom blowing, performing bottom blowing argon according to set argon flow, and automatically adjusting subsequent argon flow according to a feedback stage process;
s3, automatically acquiring ladle basic information, including the current heat smelting number, the large tank grade, the smelting steel grade, the molten steel weight, the refining slag turning amount and the refining path;
S4, after the sampling and detecting result of the molten steel components is out, starting a slagging and desulfurizing model to calculate the addition amount of slag materials, wherein a charging system can obtain the calculation data of the slagging and desulfurizing model and finish weighing the slag materials, and finally adding the weighed slag materials into a ladle to realize molten steel desulfurization and white slag refining;
S5, according to the sampling detection result of the molten steel components, the alloy addition calculation model starts to calculate the addition amount of each alloy, the charging system can obtain the calculation data of the alloy addition calculation model and finish weighing and adding the alloy materials, and then the molten steel component prediction module predicts the molten steel components;
s6, according to the actual sampling components and temperature of the molten steel in the refining process, the actual sampling components and temperature are used as verification of a heating control model and a molten steel component prediction model, and automatic correction of the temperature and the components is achieved;
s7, the rhythm control module adjusts a heating plan according to the outbound target moment, the target temperature and feedback of the heating control model;
And S8, finally, comparing and judging the predicted result of the end point component and the temperature of the molten steel with the target outlet temperature and the steel grade component requirement, if the steel is qualified, finishing refining, executing outlet operation by the steel ladle, and if the steel is unqualified, continuing to circularly go on until the molten steel is qualified.
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