A method for applying a steelmaking full-process temperature flow model

Abstract

The present application relates to the technical field of steelmaking production, and discloses a kind of steelmaking whole-process temperature flow model application method, comprising the following steps: S1, temperature prediction model from refining end to continuous casting, S2, refining process temperature prediction model, S3, temperature drop of ladle to refining transmission, S4, temperature drop of tapping, by sorting the temperature drop law and influencing factors of whole process, clear target control temperature of each key process from converter tapping to final refining smelting to continuous casting casting, develop mechanism model suitable for temperature control of steel and iron steelmaking plant, can greatly improve the prediction accuracy of temperature flow and the temperature control ability of scene, and by developing the temperature flow of whole process of steel and iron steelmaking plant, realize the automatic calculation target ladle pouring temperature, refining end temperature, refining initial temperature and target tapping temperature, to stabilize the control of steelmaking plant temperature flow, reduce the proportion of refining temperature adjustment, improve the intermediate ladle temperature hit rate of continuous casting.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steelmaking production, and particularly relates to a steelmaking full-process temperature flow model application method. BACKGROUND

[0002] The existing temperature control in China mainly relies on manual experience to estimate the temperature, and the end temperature of refining is determined by the continuous casting central control according to the liquidus temperature requirement of the steel grade and the pouring temperature, and the end temperature is corrected by the refining master according to the corresponding ladle state, the transmission interval from refining to continuous casting, the pouring section and the required pouring temperature of continuous casting, and lacks the guidance of specific temperature drop rules to the temperature drop correction value.

[0003] In production, the target converter tapping temperature is determined by the refining master according to the total transmission interval, the steel grade processing temperature drop, the ladle state and other factors, and the temperature value is corrected by the master according to personal experience. The temperature correction is determined by experience value, the transmission interval temperature drop from the converter to the refining and the process temperature drop value in the refining process are estimated by manual estimation, there is individual human deviation, there is no theoretical temperature drop calculation rule support, the correction value deviation is large, and it is not conducive to the accurate control of the full-process temperature flow, therefore, in order to solve the above problems, the steelmaking full-process temperature flow model application method is necessary. SUMMARY

[0004] (I) Technical problems solved

[0005] In view of the deficiencies in the prior art, the present application provides a steelmaking full-process temperature flow model application method, which solves the problems existing in the background art.

[0006] (II) Technical scheme

[0007] To achieve the above object, the present application provides the following technical scheme: a steelmaking full-process temperature flow model application method, comprising the following steps:

[0008] S1, temperature prediction model from refining end to continuous casting: by analyzing the factors that may affect the temperature drop during the molten steel waiting process, the influencing factors are summarized as transmission interval, ladle state, ladle slagging time, number of times in the ladle minor repair period, RH treatment time, forehearth ladle temperature and continuous casting pouring time;

[0009] S2, refining process temperature prediction model: the temperature drop in the process is mainly caused by the heat absorption of the ladle, the heat absorption of the vacuum tank, the heat radiation and the heat carried away by the exhaust gas, and the influence on the molten steel temperature is nonlinear, the part of the temperature drop is not only related to the processing time, but also closely related to the previous process, and the temperature drop caused by the addition of different tapping marks and alloys needs to be considered in the process.

[0010] S3, temperature drop during ladle to refining: the temperature drop during ladle to refining is mainly affected by the factors such as the transfer time, ladle turnaround time, ladle temperature, etc. Each factor is analyzed;

[0011] S4, temperature drop during tapping: the tapping process is mainly affected by the factors such as tapping time, tapping amount, empty ladle turnaround time, alloy addition amount, and ladle waiting time before tapping, etc. After analyzing the temperature drop law and influencing factors, a self-learning temperature drop model suitable for tapping to ladle is developed.

[0012] Further, the temperature control of the whole process aims at the final intermediate ladle casting steel temperature hit, and the casting temperature of continuous casting steel refers to the temperature of the molten steel in the tundish, which is the sum of the liquidus temperature of the steel grade and the superheat degree of the molten steel in the tundish. That is:

[0013] t CC =t L +Δt

[0014] Where t cc is the casting temperature; t L is the liquidus temperature of the molten steel; and Δt is the superheat degree of the molten steel in the tundish.

[0015] And the previous process of continuous casting, from the beginning to the end of refining treatment, to the ladle to the continuous casting ladle pouring process, there is always a temperature drop. Going further back to the converter, from the end of converter tapping to the ladle hoisting to the refining treatment, there is also a temperature drop. Through the backstepping of each smelting process, we get:

[0016] t 钢包 =t CC +t 传隔1 +t 温降 +t 传隔2

[0017] Where t 传隔1 is the temperature drop from the end of refining to continuous casting, t 温降 is the temperature drop during refining treatment, and t 传隔2 is the temperature drop during tapping to refining.

[0018] Further, the step 1 uses the test method of F probability, when the F value significance level is less than 0.05, it is introduced, greater than 0.10, it is eliminated, the R square value of regression model analysis table is 0.608, it is explained that the regression model of liquid steel temperature has better effect. The significance probability is less than 5%, the original hypothesis of rejecting regression coefficient is 0, the regression equation is meaningful, the regression coefficient table lists the correlation coefficient, according to the table, the significance probability of the ladle holding time, the ladle temperature, the RH treatment time, the end temperature, the continuous casting transmission interval time and the casting time is less than 5%, the regression coefficient is meaningful, the factor of non correlation is included in statistical analysis, according to the above analysis result, the temperature drop calculation from refining end to continuous casting is determined by multiple regression.

[0019] Further, in the step 2, the temperature drop is inconsistent in different treatment stages in the vacuum treatment process, from the process temperature measurement data, with the extension of treatment time, the temperature drop presents the trend of gentle reduction. 7.4 min, 2 ℃ / min, 11.5 min, 1.58 ℃ / min, 18.5 min, 1.15 ℃ / min. Each factor that may affect the treatment temperature drop is analyzed one by one to determine the main key factor, the vacuum tank temperature (treatment interval time) and the ladle state (empty ladle turnover time) are the factors that affect the decarburization and net circulation temperature drop coefficient, the influence value correction coefficient α, the fitting relationship between the treatment temperature drop and the empty ladle turnover time is S=6.15154 R-Sq=1.0% R-Sq(adjustment)=0.8%, P=0.052, the fitting relationship between the treatment temperature drop and the interval time drop is S=6.69837 R-Sq=0.1% R-Sq(adjustment)=0.0%, the fitting relationship between the temperature deviation and the OB oxygen content is S=6.67175 R-Sq=13.2% R-Sq(adjustment)=12.3%, the basic temperature drop =decarburization temperature drop x temperature drop coefficient 1 + alloy temperature drop + alloying cycle time temperature drop coefficient x temperature drop coefficient 2 + deoxidizing temperature rise.

[0020] Further, the fitting relationship between the treatment temperature drop and the empty ladle turnover time (the interval time from the end of the forehearth slagging to the present furnace tapping) in the step 2 is: S=4.97348 R-Sq=7.7% R-Sq(adjustment)=6.8%, the P value is 0.005, the correlation is strong, the treatment temperature drop calculation formula is determined by analysis.

[0021] Further, the R square correlation coefficient in the step 3 is 0.293, the regression model has better effect, according to the correlation coefficient listed in the variance coefficient table, the transmission interval time, the forehearth slagging time and the number of uses in the ladle small repair period are less than 5%. The transmission and rest temperature drop calculation formula from the converter to the refining can be determined by multiple regression analysis.

[0022] (III) Beneficial Effects

[0023] The present application provides a steelmaking full-process temperature flow model application method, which has the following beneficial effects:

[0024] By analyzing the temperature drop law and influencing factors of the full process, the target control temperature of each key process from converter tapping to final refining and continuous casting is determined, and a mechanism model suitable for temperature control of a steelmaking plant is developed, which can greatly improve the prediction accuracy of temperature flow and the on-site temperature control ability.

[0025] By developing the temperature flow of the steelmaking plant, the target ladle pouring temperature, refining end temperature, refining initial temperature and target tapping temperature are automatically calculated to stabilize the temperature flow control of the steelmaking plant, reduce the refining temperature adjustment ratio and improve the intermediate ladle temperature hit rate. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 It is a reverse temperature drop prediction model diagram of a steelmaking full-process temperature flow model application method;

[0027] Figure 2 It is a regression model analysis table of step one in a steelmaking full-process temperature flow model application method;

[0028] Figure 3 It is a regression coefficient table of step one in a steelmaking full-process temperature flow model application method;

[0029] Figure 4 It is a regression model analysis table of step three in a steelmaking full-process temperature flow model application method;

[0030] Figure 5 It is a regression coefficient table of step three in a steelmaking full-process temperature flow model application method. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0032] The present application provides a technical solution: a steelmaking full-process temperature flow model application method, comprising the following steps:

[0033] S1, temperature prediction model from refining end to continuous casting: by analyzing the possible influence on temperature drop during the molten steel waiting process, several influencing factors are summarized, including transfer time, ladle state, ladle slagging time, number of times in ladle minor repair period, RH treatment time, forehearth ladle temperature, continuous casting pouring time, temperature control of the whole process, and finally the purpose of hitting the continuous casting tundish pouring molten steel temperature. The pouring temperature of the continuous casting molten steel refers to the temperature of the molten steel in the tundish, which is the sum of the liquidus temperature of the steel grade and the superheat degree of the tundish molten steel. That is:

[0034] t CC = t L + Δt

[0035] In the formula, t cc is the pouring temperature; t L is the liquidus temperature of the molten steel; and Δt is the superheat degree of the tundish molten steel.

[0036] And the previous process of continuous casting refining, from the beginning to the end of refining treatment, to the ladle to the continuous casting ladle pouring process, there is always a temperature drop. Further back to the converter, from the end of converter tapping to the ladle hoisting to the refining treatment, there is also a temperature drop. Through the backstepping of each process of smelting:

[0037] t 钢包 = t CC + t 传隔1 + t 温降 + t 传隔2

[0038] In the formula, t 传隔1 is the transfer temperature drop from the end of refining to continuous casting, t 温降 is the refining treatment temperature drop, t 传隔2 is the transfer temperature drop from converter tapping to refining, and F probability test method is adopted. When the F value is less than 0.05, it is introduced, and when it is greater than 0.10, it is rejected. The R square value of the regression model analysis table is 0.608, indicating that the regression model of molten steel temperature has good effect. The significance probability is less than 5%, the null hypothesis of regression coefficient is rejected, and the regression equation is meaningful. The regression coefficient table lists the correlation coefficients. According to the table, the significance probability of forehearth holding time, forehearth ladle temperature, RH treatment time, end temperature, continuous casting transfer time and pouring time is less than 5%, and the regression coefficient is meaningful. The factors that are not related to the cause are included in the statistical analysis. According to the above analysis results, the temperature drop from the end of refining to continuous casting is determined by multiple regression. By analyzing the temperature drop rule and influencing factors of the whole process, the target control temperature of each key process from the end of converter tapping to the end of refining to the continuous casting pouring is determined, and the mechanism model suitable for temperature control of steel smelting plant is developed, which can greatly improve the prediction accuracy of temperature flow and the temperature control ability on site.

[0039] S2, refining process temperature prediction model: the temperature drop of the process is mainly due to the heat absorption of the ladle, the heat absorption of the vacuum tank, the radiation heat dissipation and the heat carried away by the exhaust gas, which has a nonlinear influence on the temperature of the molten steel. The temperature drop of this part is not only related to the processing time, but also closely related to the previous process. The temperature drop caused by the addition of different tapping marks of alloy is inconsistent. The temperature drop factor of the alloy needs to be considered in the process. In the vacuum treatment process, the temperature drop is also inconsistent in different treatment stages. From the process temperature measurement data, with the extension of the treatment time, the temperature drop shows a trend of gentle decrease. 7.4 min, 2 ℃ / min, 11.5 min, 1.58 ℃ / min, 18.5 min, 1.15 ℃ / min. Analyze each factor that may affect the treatment temperature drop one by one, determine the main key factors, the vacuum tank temperature (processing interval time), the ladle state (empty ladle turnover time) are all factors affecting the decarburization and net circulation temperature drop coefficient, the influence value correction coefficient α, the fitting relationship between the treatment temperature drop and the empty ladle turnover time is S=6.15154 R-Sq=1.0% R-Sq(adjust)=0.8%, P=0.052, the fitting relationship between the treatment temperature drop and the interval time is S=6.69837 R-Sq=0.1% R-Sq(adjust)=0.0%, the fitting relationship between the temperature deviation and the OB oxygen content is S=6.67175 R-Sq=13.2% R-Sq(adjust)=12.3%, the basic temperature drop = decarburization temperature drop x temperature drop coefficient 1 + alloy temperature drop + alloying cycle time temperature drop coefficient x temperature drop coefficient 2 + deoxidizing temperature rise, by developing the temperature flow of the whole process of the steelmaking plant, the target open ladle pouring temperature, the refining end temperature, the refining initial temperature and the target tapping temperature are realized. Automatic calculation to stabilize the temperature flow control of the steelmaking plant, reduce the refining temperature adjustment ratio, and improve the intermediate ladle temperature hit rate.

[0040] S3, ladle to refining temperature drop: the ladle to refining is mainly affected by the factors such as transfer time, ladle turnover time, ladle temperature, etc. After analyzing each factor, the correlation coefficient is 0.293, and the regression model has good effect. According to the correlation coefficient listed in the variance coefficient table, the transfer time, the former furnace slag turning time and the number of uses in the ladle repair period are less than 5%. The transfer temperature drop calculation formula from converter to refining can be determined by multiple regression analysis.

[0041] S4, tapping temperature drop: the tapping process is mainly affected by tapping time, tapping quantity, empty ladle turnover time, alloy addition quantity and ladle waiting time before tapping, etc. After combing the temperature drop law and influencing factors, a self-learning temperature drop model suitable for tapping to ladle is developed.

[0042] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and illustrative figures, it should be apparent that the scope of the present application is not limited to these specific embodiments.

[0043] While the embodiments of the application have been shown and described herein, it will be understood by those skilled in the art that many changes, modifications, substitutions and alterations to these embodiments can be made without departing from the principles and spirits of the application, and it is intended that the scope of the application be limited solely by the scope of the appended claims and the equivalents thereof.

Claims

1. A method of applying a steelmaking full flow temperature flow model, characterized in that, Comprising the following steps: S1, temperature prediction model from refining end to continuous casting: by analyzing several factors that may affect the temperature drop during the molten steel waiting process: interval time, ladle condition, ladle slagging time, number of times during the ladle minor repair period, RH treatment time, ladle temperature, continuous casting pouring time, temperature control throughout the process, and finally aiming at the target of the continuous casting tundish pouring molten steel temperature, the continuous casting molten steel pouring temperature refers to the temperature of the molten steel in the tundish, which is the sum of the liquidus temperature of the steel grade and the superheat degree of the tundish molten steel, i.e.: In the formula is the casting temperature; is the liquidus temperature of the molten steel; is the superheat of the molten steel in the tundish; And the previous refining process of continuous casting, from the beginning to the end of refining treatment, to the ladle to the continuous casting ladle pouring process, there is always a temperature drop, and further to the converter, from the end of converter tapping to the ladle hoisting to the refining treatment, there is also a temperature drop change, through the backstepping of each process of smelting: In the formula The temperature drop from the end of refining to continuous casting, The temperature drop from the end of refining, The temperature drop from the end of refining to continuous casting, the F probability test method is used, when the F value significance level is less than 0.05, it is introduced, and when it is greater than 0.10, it is rejected, the R square value in regression model analysis is 0.608, which shows that the regression model of molten steel temperature has good effect; the significance probability is less than 5%, the original hypothesis that the regression coefficient is 0 is rejected, the regression equation is meaningful, the significance probability of the forehearth steel ladle temperature, RH treatment time, end temperature, continuous casting transmission time and casting time is less than 5%, the regression coefficient is meaningful, the factors unrelated to causality are included in statistical analysis, according to the above analysis results, the temperature drop calculation from the end of refining to continuous casting is determined by multiple regression, through the analysis of the temperature drop law and influencing factors of the whole process, the target control temperature of each key process from the end of refining to continuous casting is determined, the mechanism model suitable for temperature control of steelmaking plant is developed, which can greatly improve the prediction accuracy of temperature flow and the temperature control ability on site. S2, temperature prediction model of refining process: the temperature drop during the treatment process is mainly due to the heat absorption of the ladle, the heat absorption of the vacuum tank, the radiation heat loss and the heat carried away by the exhaust gas, which has a nonlinear effect on the temperature of the molten steel. This part of the temperature drop is not only related to the treatment time, but also closely related to the previous process. The temperature drop caused by the addition of different tapping marks of alloy will be inconsistent. The temperature drop factor of the alloy needs to be considered during the treatment process. During the vacuum treatment process, the temperature drop is inconsistent at different treatment stages. From the process temperature measurement data, with the extension of the treatment time, the temperature drop shows a trend of gentle decrease; 7.4 min, 2 ℃ / min, 11.5 min, 1.58 ℃ / min, 18.5 min, 1.15 ℃ / min; Each factor that may affect the treatment temperature drop is analyzed one by one to determine the main key factors, the vacuum tank temperature and the ladle condition are the factors affecting the decarburization and net circulation temperature drop coefficient, the influence value correction coefficient α, the fitting relationship between the treatment temperature drop and the empty ladle turnover time is S = 6.15154 R-Sq = 1.0% R-Sq (adjust) = 0.8%, P = 0.052, the fitting relationship between the treatment temperature drop and the interval time is S = 6.69837 R-Sq = 0.1% R-Sq (adjust) = 0.0%, the fitting relationship between the temperature deviation and the OB oxygen content is S = 6.67175 R-Sq = 13.2% R-Sq (adjust) = 12.3%, the basic temperature drop = decarburization temperature drop × temperature drop coefficient 1 + alloy temperature drop + alloying cycle time temperature drop coefficient × temperature drop coefficient 2 + deoxidization temperature rise, by developing the temperature flow of the whole process of the steel smelting plant, the target ladle pouring temperature, the refining end temperature, the refining initial temperature and the target tapping temperature are realized to stabilize the temperature flow control of the steel smelting plant, reduce the refining temperature adjustment ratio, and improve the continuous casting tundish temperature hit rate; S3, temperature drop from tapping to refining: the main factors affecting the temperature drop from tapping to refining are the transfer time, ladle turnaround time, and ladle temperature. The R correlation coefficient is 0.293, and the regression model has good effect. The transfer time, forehearth slag turning time, and the number of times used in the ladle repair cycle are significantly less than 5%. Through multiple regression analysis, the temperature drop calculation formula from the converter to the refining can be determined; S4, temperature drop: the main factors affecting the temperature drop during tapping are the tapping time, tapping quantity, empty ladle turnaround time, alloy addition quantity, and ladle waiting time before tapping. After analyzing the temperature drop law and influencing factors, a self-learning temperature drop model suitable for tapping to ladle is developed.

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