Slab continuous casting machine crystallizer liquid level control method and automatic casting method

Abstract

The invention discloses a slab continuous casting machine crystallizer liquid level control method and an automatic casting method, and belongs to the technical field of metallurgy. In the second liquid level control stage, the liquid level of the crystallizer is increased to a second liquid level set value from the first liquid level set value, the second liquid level set value is the liquid level when the casting blank begins to be pulled out, whether parameters are abnormal or not is judged through parameter collection and parameter analysis, the opening degree of a stopper rod is adjusted according to the judgment result, and the liquid level of the crystallizer is controlled; single-variable statistical process control and multivariable statistical process control are respectively carried out through single-variable analysis and multivariable analysis, so that the opening value of a stopper rod is adjusted and controlled, the liquid level control of the crystallizer in automatic casting operation is realized, unnecessary shutdown and adjustment time are avoided, the efficiency of a continuous casting production line is improved to the maximum extent, and the production cost is reduced. The productivity of production equipment is fully utilized, and the utilization rate of a production line is greatly improved.

Description

Technical Field

[0001] The invention belongs to the technical field of metallurgy, and in particular relates to a slab continuous casting machine crystallizer liquid level control method and an automatic pouring method. Background Art

[0002] Continuous casting is a casting process commonly used in the steel industry. It injects liquid metal directly into the crystallizer, controls the cooling conditions so that the molten steel can solidify according to the process requirements, realizes the continuous casting process, and produces ingots with certain shapes and sizes. The continuous casting process has the characteristics of high efficiency, speed and automation. At present, the continuous casting process accounts for more than 90% of the steel production, and its position in the modern steel production process is very important. The automatic pouring of the tundish is a key technology to ensure that the automatic pouring can be realized within the process requirement time after the replacement of important components such as the tundish replacement and the immersion nozzle replacement. Although there are some public methods to realize the automatic pouring of the tundish, there are still some key factors to consider in the automatic pouring process, such as the timing of pouring, flow regulation, and the stability of the ingot quality. The automatic pouring process relies on manual monitoring and other limitations. There is no better method for monitoring the pouring process. How to achieve effective control of the crystallizer level has a great impact on the success rate of the automatic pouring of the tundish and the quality of the ingot during the pouring process. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention discloses a slab continuous casting machine crystallizer liquid level control method and an automatic casting method.

[0004] The present invention adopts the following technical solution:

[0005] A method for controlling the liquid level of a slab continuous casting machine crystallizer, the method being used for automatically starting casting when replacing a slab continuous casting tundish, the method comprising the following steps:

[0006] S1, liquid level control stage 1, in which the crystallizer liquid level is adjusted by controlling the stopper rod opening value to raise the crystallizer liquid level from the initial liquid level value to the first liquid level setting value;

[0007] S2, liquid level control stage 2, in which the crystallizer liquid level is adjusted by controlling the stopper rod opening value, and the crystallizer liquid level is raised from the first liquid level setting value to the second liquid level setting value, and the second liquid level setting value is the liquid level when the billet starts to be pulled out;

[0008] S3, the billet pulling stage, after the liquid level of the crystallizer reaches the second liquid level setting value, the fan-shaped segment operation is started, the billet is pulled out, and automatic pouring is completed;

[0009] The adjustment and control of the stopper rod opening value includes: parameter collection and parameter analysis, judging whether the parameters are abnormal according to the result of the parameter analysis, and adjusting the stopper rod opening according to the judgment result to control the crystallizer liquid level;

[0010] The parameters include: steel type, section width, tundish temperature, tundish weight, mold level, stopper opening, immersion nozzle depth and casting speed;

[0011] The parameter analysis includes univariate analysis and multivariate analysis, the univariate analysis is used for univariate statistical process control, the multivariate analysis is used for multivariate statistical process control, and the univariate statistical process control and multivariate statistical process control are used for adjustment control of the stopper rod opening value;

[0012] The single variable statistical process control includes: collecting parameter data in chronological order during the production process, determining control limits based on the parameter data, and controlling the corresponding parameter data within the control limits during the production process;

[0013] The multivariate statistical process control includes: for a production process in which multiple variables are highly correlated, parameter data of multiple variables are collected simultaneously, and the correlation between multiple variables and the production process and product quality is comprehensively analyzed using multivariate statistical process control to achieve multivariate production process monitoring and quality anomaly analysis.

[0014] Furthermore, the adjustment and control of the stopper rod opening value includes: using regression analysis to fit the relationship formula between the stopper rod opening and the amount of steel passing through, the amount of steel passing through Q = f(K) = V*S, where K is the stopper rod opening, V is the molten steel flow rate, and S is the opening area.

[0015] Furthermore, the multivariate analysis includes: using PCA-T 2 -SPE analysis method to analyze, obtain control limits, compare T 2 and SPE statistics and control limits, if T 2 If the and SPE statistics are greater than the control limits, it is considered that the observed data are faulty; if T 2 If the SPE statistic is less than or equal to the control limit, the observed data are considered normal.

[0016] Furthermore, the multivariate analysis includes:

[0017] The process monitoring model is established, the operation data of the casting process is collected, and the operation data of the automatic casting of the tundish replacement that meets the casting quality requirements are selected as training data. The training data are first standardized, and then principal component analysis is performed to construct T 2 and SPE statistics, determine the corresponding control limits, which are the criteria for the model to judge abnormalities;

[0018] Process monitoring and abnormality monitoring, obtain new tundish pouring data, calculate T with new data 2 The statistics and SPE statistics are compared with the control limits to determine whether they exceed the corresponding control limits. This is used for online real-time abnormal detection of tundish pouring, and automatic monitoring of tundish pouring during continuous casting is achieved.

[0019] Parameter optimization, for automatic pouring process T 2 The main parameters whose statistics and SPE statistics exceed the control limits are optimized. For the stopper opening, the μ±2σ range of the opening K is selected as the limit to select the stopper opening, μ is the parameter mean, and σ is the parameter standard deviation.

[0020] Furthermore, the PCA-T 2 -SPE analysis methods include T 2 The calculation of the statistic, the T 2 The steps to calculate the statistic include:

[0021] S1.1 Center the original data of the parameters, subtract the mean of each variable so that the mean of the data is zero;

[0022] S1.2 Calculate the covariance matrix of the data to reflect the correlation between the variables;

[0023] S1.3 Perform eigenvalue decomposition on the covariance matrix to find its eigenvalues ​​and eigenvectors;

[0024] S1.4 multiplying the original data by the eigenvector matrix to obtain projected data, wherein the projected data is the principal component;

[0025] S1.5 Calculation of T 2 Statistics, the formula is: Among them, z i is the i-th principal component, λ i is the i-th eigenvalue, k is the number of principal components;

[0026] S1.6 Calculation of T 2 The control limit of the statistic is: Where n is the number of samples, k is the number of principal components, α is the significance level, and F α (k,n--k) is the alpha quantile of an F-distribution with k and n--k degrees of freedom.

[0027] Furthermore, the PCA-T 2 -SPE analysis method includes: The implementation method steps of SPE statistics include:

[0028] S2.1 preprocessing the raw data of the parameter, wherein the preprocessing includes centering, standardization or removal of outliers;

[0029] S2.2 Perform principal component analysis on the preprocessed data to obtain its principal component matrix and contribution rate;

[0030] S2.3 Determine the number of principal components based on the contribution rate, and select the principal components with a cumulative contribution rate of more than 90%;

[0031] S2.4 Preprocess the new observation data so that they have the same scale as the original data.

[0032] S2.5 Calculate the SPE statistic, the formula is: SPE = |x 2 |=|(I―PP T )x| 2 , where x is the new observation data, P is the principal component matrix, and I is the identity matrix;

[0033] S2.6 Calculate the control limits of the SPE statistic using the formula: In the formula, For X n×p The i-th power of the j-th eigenvalue of the covariance matrix, u α is the critical value of the normal distribution at a significance level of α, g is the number of principal components retained in the model, and p is the number of variables.

[0034] Furthermore, the liquid level of the crystallizer is raised in a manner of first fast and then slow. The liquid level of the crystallizer in the pouring process of the automatic pouring model is:

[0035]

[0036] Among them, h is the liquid level height of the crystallizer, T is the pouring time, L1 is the total length of the crystallizer; L2 is the length of the dummy rod, v1 is the actual pulling speed, and v2 is the target pulling speed.

[0037] Furthermore, the crystallizer liquid level control includes: during the pouring process, after the ingot is pulled out, controlling the crystallizer liquid level to fluctuate periodically, and the fluctuation range is 800±3 mm.

[0038] Furthermore, the liquid level control stage 1 includes four time periods, namely, a first time period, a second time period, a third time period and a fourth time period, and the four time periods are all within the range of 1-10S. Through the four time periods, the crystallizer liquid level rises from the initial liquid level value to the first liquid level setting value;

[0039] At the beginning of the first period, the stopper rod opening is adjusted to the first stopper rod opening value, and the first stopper rod opening value is maintained in the first period; at the end of the first period, the first stopper rod opening value is reduced by 3-3.5mm from the first stopper rod opening value to reach the initial value of the second period of the stopper rod, and the stopper rod opening value slowly increases in the second period. At the end of the second period, the stopper rod opening value increases by 0.5-1mm relative to the initial value of the second period of the stopper rod, and the stopper rod opening reaches the end value of the second period of the stopper rod; in the third period, the stopper rod opening value continues to increase on the basis of the end value of the second period of the stopper rod, and ... The stopper rod opening value within the segment increases by 1-1.5mm, and the stopper rod opening reaches the end value of the third time period of the stopper rod; in the fourth time period, the stopper rod opening value continues to increase on the basis of the end value of the third time period of the stopper rod, and the stopper rod opening value in the fourth time period increases by 0.5-1mm; the initial value of the liquid level is 680±5mm; the first liquid level setting value is 752mm; the second liquid level setting value is 775mm; the first opening value of the stopper rod is: k×10mm, k is the ratio of the current minimum cross-sectional width dimension of the ingot to the standard cross-sectional width dimension, and the standard cross-sectional width dimension is 1300mm.

[0040] A method for automatically starting pouring in a slab continuous casting tundish, the method adopting the above control method to control the liquid level of the crystallizer.

[0041] Beneficial effects:

[0042] The present invention takes the dynamic and stable control of the crystallizer liquid level as the core, meets the process requirements of the tundish pouring in the continuous casting production process, and combines the automatic control concept. By controlling the dynamic stability of the crystallizer liquid level, the continuous casting machine can obtain high-quality steel without leakage or overflow during operation. The application of automatic pouring technology in the slab continuous casting tundish can effectively improve the production efficiency and quality stability of the slab continuous casting production line. By real-time monitoring of the tundish and crystallizer state parameters, and performing automatic pouring operations according to preset pouring process parameters, unnecessary shutdown and adjustment time can be avoided, the efficiency of the continuous casting production line can be maximized, and the production capacity of the production equipment can be fully utilized, thereby greatly improving the utilization rate of the production line.

[0043] The crystallizer liquid level control method disclosed in the present invention reduces the intervention of human factors, reduces the quality fluctuation and production risks caused by different skill levels and fatigue levels of operators, and ensures the stability and consistency of the quality of the casting.

[0044] In the technical solution of the present invention, the crystallizer liquid level is controlled according to the liquid level set value and the set time in the pouring procedure, and the stopper rod performs the corresponding opening according to the crystallizer liquid level set value; after the crystallizer liquid level reaches the set value, the crystallizer liquid level enters the automatic control mode, and the stopper rod opening determines the flow rate of the molten steel in the tundish into the crystallizer. During the automatic control of the crystallizer liquid level, the stopper rod opening control is automatically realized by the crystallizer liquid level control system; the crystallizer liquid level control method disclosed by the present invention not only realizes automatic pouring and ensures the automatic pouring rate, but also ensures the safety of tundish pouring. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 It is a schematic diagram of the liquid level of the crystallizer during the automatic pouring process of the slab continuous casting tundish of the present invention;

[0047] Figure 2 It is a schematic diagram of the stopper rod position during the automatic pouring process of the slab continuous casting tundish of the present invention;

[0048] Figure 3 It is a schematic diagram of the casting speed during the automatic pouring process of the slab continuous casting tundish of the present invention;

[0049] Figure 4 It is a schematic diagram of the weight of the tundish during the automatic pouring of the slab continuous casting tundish of the present invention;

[0050] Figure 5 It is a schematic diagram of the cumulative contribution rate of the PCA principal component variance during the automatic pouring process of the slab continuous casting tundish of the present invention;

[0051] Figure 6 The present invention is a parameter monitoring method for the automatic pouring process of the slab continuous casting tundish. 2 , schematic diagram of SPE results;

[0052] Figure 7 It is a schematic diagram of the analysis of the main variable contribution values ​​of the abnormal data sample of the No. 35 tundish replacement during the automatic pouring of the slab continuous casting tundish of the present invention. DETAILED DESCRIPTION

[0053] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0054] It should be clear that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1

[0056] A method for controlling the liquid level of a slab continuous casting machine crystallizer, the method being used for automatically starting casting when replacing a slab continuous casting tundish, the method comprising the following steps:

[0057] S1 liquid level control stage 1, in which the crystallizer liquid level is adjusted by controlling the stopper rod opening value to raise the crystallizer liquid level from the initial liquid level value to the first liquid level setting value;

[0058] S2 liquid level control stage 2, in which the crystallizer liquid level is adjusted by controlling the stopper rod opening value, and the crystallizer liquid level is raised from the first liquid level setting value to the second liquid level setting value, and the second liquid level setting value is the liquid level when the billet starts to be pulled out;

[0059] In the S3 billet pulling stage, after the liquid level of the crystallizer reaches the second liquid level setting value, the fan-shaped segment operation is started, the billet is pulled out, and automatic pouring is completed;

[0060] The adjustment and control of the stopper rod opening value includes: parameter collection and parameter analysis, judging whether the parameters are abnormal according to the result of the parameter analysis, and adjusting the stopper rod opening according to the judgment result to control the crystallizer liquid level;

[0061] The parameters include: steel type, section width, tundish temperature, tundish weight, mold level, stopper opening, immersion nozzle depth and casting speed;

[0062] The parameter analysis includes univariate analysis and multivariate analysis, the univariate analysis is used for univariate statistical process control, the multivariate analysis is used for multivariate statistical process control, and the univariate statistical process control and multivariate statistical process control are used for adjustment control of the stopper rod opening value;

[0063] The single variable statistical process control includes: collecting parameter data in chronological order during the production process, determining control limits based on the parameter data, and controlling the corresponding parameter data within the control limits during the production process;

[0064] The multivariate statistical process control includes: for a production process in which multiple variables are highly correlated, parameter data of multiple variables are collected simultaneously, and the correlation between multiple variables and the production process and product quality is comprehensively analyzed using multivariate statistical process control to achieve multivariate production process monitoring and quality anomaly analysis.

[0065] Furthermore, the adjustment and control of the stopper rod opening value includes: using regression analysis to fit the relationship formula between the stopper rod opening and the amount of steel passing through, the amount of steel passing through Q = f(K) = V*S, where K is the stopper rod opening, V is the molten steel flow rate, and S is the opening area.

[0066] Furthermore, the multivariate analysis includes: using PCA-T 2 -SPE analysis method to analyze, obtain control limits, compare T 2 and SPE statistics and control limits, if T 2 If the and SPE statistics are greater than the control limits, it is considered that the observed data are faulty; if T 2 If the SPE statistic is less than or equal to the control limit, the observed data are considered normal.

[0067] Furthermore, the multivariate analysis includes:

[0068] The process monitoring model is established, the operation data of the casting process is collected, and the operation data of the automatic casting of the tundish replacement that meets the casting quality requirements are selected as training data. The training data are first standardized, and then principal component analysis is performed to construct T 2 and SPE statistics, determine the corresponding control limits, which are the criteria for the model to judge abnormalities;

[0069] Process monitoring and abnormality monitoring, obtain new tundish pouring data, calculate T with new data 2 The statistics and SPE statistics are compared with the control limits to determine whether they exceed the corresponding control limits. This is used for online real-time abnormal detection of tundish pouring, and automatic monitoring of tundish pouring during continuous casting is achieved.

[0070] Parameter optimization, for automatic pouring process T 2 The main parameters whose statistics and SPE statistics exceed the control limits are optimized. For the stopper opening, the μ±2σ range of the opening K is selected as the limit to select the stopper opening, μ is the parameter mean, and σ is the parameter standard deviation.

[0071] Furthermore, the PCA-T 2 -SPE analysis methods include T 2 The calculation of the statistic, the T 2 The steps to calculate the statistic include:

[0072] S1.1 Center the original data of the parameters, subtract the mean of each variable so that the mean of the data is zero;

[0073] S1.2 Calculate the covariance matrix of the data to reflect the correlation between the variables;

[0074] S1.3 Perform eigenvalue decomposition on the covariance matrix to find its eigenvalues ​​and eigenvectors;

[0075] S1.4 multiplying the original data by the eigenvector matrix to obtain projected data, wherein the projected data is the principal component;

[0076] S1.5 Calculation of T 2 Statistics, the formula is: Among them, z i is the i-th principal component, λ i is the i-th eigenvalue, k is the number of principal components;

[0077] S1.6 Calculation of T 2 The control limit of the statistic is: Where n is the number of samples, k is the number of principal components, α is the significance level, and F α (k,n--k) is the alpha quantile of an F-distribution with k and n--k degrees of freedom.

[0078] Furthermore, the PCA-T 2 -SPE analysis method includes: The implementation method steps of SPE statistics include:

[0079] S2.1 preprocessing the raw data of the parameter, wherein the preprocessing includes centering, standardization or removal of outliers;

[0080] S2.2 Perform principal component analysis on the preprocessed data to obtain its principal component matrix and contribution rate;

[0081] S2.3 Determine the number of principal components based on the contribution rate, and select the principal components with a cumulative contribution rate of more than 90%;

[0082] S2.4 Preprocess the new observation data so that they have the same scale as the original data.

[0083] S2.5 Calculate the SPE statistic, the formula is: SPE = |x 2 |=|(I―PP T )x| 2 , where x is the new observation data, P is the principal component matrix, and I is the identity matrix;

[0084] S2.6 Calculate the control limits of the SPE statistic using the formula: In the formula, For X n×P The i-th power of the j-th eigenvalue of the covariance matrix, u α is the critical value of the normal distribution at a significance level of α, h is the number of principal components retained in the model, and p is the number of variables.

[0085] Furthermore, the liquid level of the crystallizer is raised in a manner of first fast and then slow. The liquid level of the crystallizer in the pouring process of the automatic pouring model is:

[0086]

[0087] Where: h: crystallizer liquid level height (mm); T: pouring time (s); L1: total length of crystallizer (mm); L2: dummy rod length (mm); v1: actual pulling speed (m / min); v2: target pulling speed (m / min). Figure 1 As shown, Figure 1 This is a schematic diagram of the crystallizer liquid level during the automatic pouring of the slab continuous casting tundish. The figure shows the liquid level change curve during t1 to t6, where t1 to t5 is the first stage of crystallizer liquid level control, and t6 is the second stage of crystallizer liquid level control.

[0088] Furthermore, the crystallizer liquid level control includes: during the pouring process, after the ingot is pulled out, controlling the crystallizer liquid level to fluctuate periodically, and the fluctuation range is 800±3 mm.

[0089] Furthermore, the liquid level control stage 1 includes four time periods, namely, a first time period, a second time period, a third time period and a fourth time period, and the four time periods are all within the range of 1-10S. Through the four time periods, the crystallizer liquid level rises from the initial liquid level value to the first liquid level setting value;

[0090] At the beginning of the first period, the stopper rod opening is adjusted to the first stopper rod opening value, and the first stopper rod opening value is maintained in the first period; at the end of the first period, the first stopper rod opening value is reduced by 3-3.5mm from the first stopper rod opening value to reach the initial value of the second period of the stopper rod, and the stopper rod opening value slowly increases in the second period. At the end of the second period, the stopper rod opening value increases by 0.5-1mm relative to the initial value of the second period of the stopper rod, and the stopper rod opening reaches the end value of the second period of the stopper rod; in the third period, the stopper rod opening value continues to increase on the basis of the end value of the second period of the stopper rod, and ... The stopper rod opening value increases by 1-1.5mm in the segment, and the stopper rod opening reaches the end value of the third period of the stopper rod; in the fourth period, the stopper rod opening value continues to increase on the basis of the end value of the third period of the stopper rod, and the stopper rod opening value increases by 0.5-1mm in the fourth period; the initial value of the liquid level is 680±5mm; the first liquid level setting value is 752mm; the second liquid level setting value is 775mm; the first stopper rod opening value is: k×10mm, k is the ratio of the current minimum cross-sectional width dimension of the ingot to the standard cross-sectional width dimension, and the standard cross-sectional width dimension is 1300mm. Figure 2 As shown, Figure 2This is a schematic diagram of the stopper rod position during the automatic pouring process of the slab continuous casting tundish. The figure shows the change curve of the stopper rod opening during the process of t1 to t6. By controlling the stopper rod opening, the flow rate of molten steel entering the crystallizer from the tundish, that is, the amount of steel flowing, can be controlled to achieve the purpose of controlling the crystallizer liquid level. By comparing Figure 1 and Figure 2 When the stopper rod opening is larger, the crystallizer liquid level increases more. When the crystallizer liquid level fluctuates greatly, the stopper rod opening will also fluctuate greatly. Figure 3 This is a schematic diagram of the casting speed during the automatic casting process of the slab continuous casting tundish. It can be seen from the figure that the casting speed increases slowly from t6 and gradually reaches the target casting speed. The casting speed and the crystallizer liquid level increase synchronously, and finally the casting speed is stabilized at the target casting speed, and the crystallizer is stabilized at 800±3mm. Figure 2 and Figure 3 As the pulling speed increases, the stopper opening will also increase to meet the mold's requirements for port throughput. Figure 4 The figure shows the weight of the tundish during the automatic pouring of the slab continuous casting tundish. The flow of the tundish increases steadily during the whole process and finally stabilizes at about 70t. At this time, the stable casting stage is entered and the automatic pouring is completed. The tundish will maintain a stable weight until the ladle is replaced or the tundish is replaced. If the life of the tundish expires, the tundish replacement and automatic pouring procedure will be entered again.

[0091] A method for automatically starting pouring in a slab continuous casting tundish, the method adopting the above control method to control the liquid level of the crystallizer.

[0092] Example 2

[0093] A method for controlling the liquid level of a slab continuous casting machine crystallizer, the method being used for automatically starting pouring of a slab continuous casting tundish, the method comprising:

[0094] Liquid level control stage 1, in which the crystallizer liquid level is adjusted by controlling the stopper rod opening value to raise the crystallizer liquid level from the initial liquid level value to the first liquid level setting value;

[0095] Liquid level control stage 2, in which the liquid level of the crystallizer is adjusted by controlling the stopper rod opening value, and the liquid level of the crystallizer is raised from the first liquid level setting value to the second liquid level setting value, and the second liquid level setting value is the liquid level when the billet starts to be pulled out;

[0096] The adjustment and control of the stopper rod opening value includes: parameter collection and parameter analysis, judging whether the parameters are abnormal according to the result of the parameter analysis, and adjusting the stopper rod opening according to the judgment result to control the crystallizer liquid level;

[0097] The growth process of the crystallizer liquid level during the automatic pouring process is directly affected by the stopper rod opening. The relationship between the stopper rod opening and the amount of steel passing through is fitted using regression analysis. The amount of steel passing through is Q = f (K) = V * S, where K is the stopper rod opening, V is the molten steel flow rate, and S is the opening area. The stopper rod opening K determines the opening area S, so it becomes a function of the amount of steel passing through and K. In the first stage of liquid level control, the amount of steel passing into the crystallizer is controlled by controlling the stopper rod opening and opening time, thereby controlling the liquid level growth of the crystallizer. In the second stage of liquid level control, the crystallizer liquid level can be stabilized at 800 ± 3 mm when the casting speed changes by controlling the stopper rod opening K.

[0098] Statistical process monitoring model establishment and abnormal monitoring process: Model establishment: By collecting the key parameters in the casting process, the automatic casting operation data of the tundish replacement with casting quality meeting the requirements are selected as training data. The training data is first standardized, and then principal component analysis is performed to construct T 2 and SPE statistics, determine the corresponding control limit, which is the standard for the model to judge abnormalities; process monitoring and abnormality monitoring: obtain new tundish pouring data, and calculate T 2 Compare the SPE statistics with the control limit to determine whether it exceeds the corresponding control limit, thereby realizing online real-time tundish pouring anomaly detection and automatic monitoring of tundish pouring during continuous casting; parameter optimization process: For the automatic pouring process T 2 The main reasons for the abnormality caused by the training sample analysis of the statistics and SPE statistics exceeding the limit are analyzed, that is, which main variables cause the abnormal quality of the ingot cast by automatic pouring, and the variables that have the main contribution to the process abnormality are optimized. Specifically, through the method of data statistics, taking the stopper opening as an example, the stopper opening is selected as the range of μ±2σ of the opening K, μ is the parameter mean, and σ is the parameter standard deviation.

[0099] The parameters include: steel type, section width, tundish temperature, tundish weight, mold level, stopper opening, immersion nozzle depth and casting speed;

[0100] The above parameters are the parameters that play a major role in the growth and stability of the crystallizer liquid level. The steel type determines the basic flow characteristics of the molten steel. Considering the large differences between low-carbon steel, medium-carbon steel, peritectic steel, etc., the steel type is subdivided for parameter setting. The section width and the pulling speed together determine the amount of steel passing through the crystallizer. The stopper rod opening affects the area of ​​molten steel flowing from the tundish into the crystallizer, and then affects the amount of steel passing through. During the pouring process, the weight of the tundish will determine the depth of the molten steel. The deeper the molten steel, the faster the flow rate. The tundish temperature determines the superheat, that is, the molten steel temperature exceeds the temperature of the liquidus line of the molten steel. The higher the temperature, the stronger the fluidity. The insertion depth of the submerged nozzle determines the flow rate of the molten steel after it flows into the crystallizer. The depth must ensure that the molten steel from the side hole flows into the crystallizer below the protective slag to avoid slag rolling. The pulling speed also determines the amount of steel passing through. After the ingot is pulled out, in the second stage of liquid level growth, as the pulling speed increases, the opening increases until the pulling speed stabilizes to the target value, and the crystallizer liquid level is controlled at 800±3mm.

[0101] The parameter analysis includes univariate analysis and multivariate analysis, the univariate analysis is used for univariate statistical process control, the multivariate analysis is used for multivariate statistical process control, and the univariate statistical process control and multivariate statistical process control are used for adjustment control of the stopper rod opening value;

[0102] The method of statistical process control is used to monitor the automatic pouring process of the tundish. The conventional monitoring method is to monitor a certain variable. According to metallurgical experience, the key variables in the parameters are selected for separate monitoring. The statistical control chart and process capability analysis are used for monitoring. The fluctuation of the variable over a period of time is observed, and the upper and lower limits of the variable are statistically calculated. However, with the complexity of the process and the increase of variables, it is difficult to monitor the impact of all variables in the production process on the entire production quality at the same time. Although the monitored variables are all in the normal range, the production quality is abnormal. In order to cope with the high correlation of multiple variables and the difficulty in accurately describing the complexity of the production process, multivariate statistical process control is used to comprehensively consider the correlation of each variable to achieve multivariate production process monitoring and quality abnormality analysis. Single variable statistical process control monitors the crystallizer liquid level, and the collected data is marked on the graph in chronological order, and the upper and lower control limits are marked at the same time. In the normal pouring process, if the process changes remain within the control limit, it is considered that the change is normal and the process is under statistical control. If the data point exceeds the control limit, it means that abnormal factors have a significant impact on the process.

[0103] The billet is pulled out, and after the liquid level in the crystallizer reaches the second liquid level setting value, the fan-shaped segment operation is started, the billet is pulled out, and automatic pouring is completed.

[0104] Furthermore, the basis for determining abnormalities during univariate analysis includes the following eight situations: one point is more than three standard deviations away from the center line; nine consecutive points are on the same side; six consecutive points continue to rise or fall; 14 consecutive points rise and fall alternately; two of three consecutive points are two standard deviations away from the center line; four of five consecutive points are one standard deviation away from the center line; fifteen consecutive points are within one standard deviation of the center line; and eight consecutive points are outside one standard deviation of the center line.

[0105] Furthermore, the multivariate analysis includes: using PCA-T 2 -SPE analysis method is used to analyze and obtain control limits. If the control limits are exceeded, an abnormality is predicted.

[0106] Furthermore, the PCA-T 2 -SPE analysis methods include: 2 The steps to calculate the statistic include:

[0107] S1.1 Center the original data of the parameters, subtract the mean of each variable so that the mean of the data is zero;

[0108] S1.2. Calculate the covariance matrix of the data to reflect the correlation between the variables;

[0109] S1.3 Perform eigenvalue decomposition on the covariance matrix to find its eigenvalues ​​and eigenvectors;

[0110] S1.4 multiplies the original data by the eigenvector matrix to obtain the projected data, i.e., the principal components;

[0111] S1.5 Calculation of T 2 Statistics, the formula is: Among them, z i is the i-th principal component, λ i is the i-th eigenvalue, k is the number of principal components;

[0112] S1.6 Calculation of T 2 The control limit of the statistic is: Where n is the number of samples, k is the number of principal components, α is the significance level, and F α (k,n--k) is the alpha quantile of an F-distribution with k and n--k degrees of freedom.

[0113] Furthermore, the PCA-T 2 -SPE analysis method includes: The implementation method steps of SPE statistics include:

[0114] S2.1 preprocessing the raw data of the parameter, wherein the preprocessing includes centering, standardization or removal of outliers;

[0115] S2.2 Perform principal component analysis on the preprocessed data to obtain its principal component matrix and contribution rate;

[0116] S2.3 Determine the number of principal components based on the contribution rate, and select the principal components with a cumulative contribution rate of more than 90%;

[0117] S2.4 Preprocess the new observation data so that they have the same scale as the original data.

[0118] S2.5 Calculate the SPE statistic, the formula is: SPE = |x 2 |=|(I―PP T )x| 2 , where x is the new observation data, P is the principal component matrix, and I is the identity matrix;

[0119] S2.6 Calculate the control limits of the SPE statistic using the formula: In the formula, For X n×p The i-th power of the j-th eigenvalue of the covariance matrix, u α is the critical value of the normal distribution at a significance level of α, h is the number of principal components retained in the model, and p is the number of variables.

[0120] Furthermore, the PCA-T 2 -SPE analysis methods include: Comparison of T 2 and SPE statistics and control limits, if T 2 If the and SPE statistics are greater than the control limits, it is considered that the observed data are faulty; if T 2 If the SPE statistic is less than or equal to the control limit, the observed data are considered normal.

[0121] Furthermore, the liquid level of the crystallizer is raised in a manner of first fast and then slow. The liquid level of the crystallizer in the pouring process of the automatic pouring model is:

[0122]

[0123] Where: h: height of crystallizer liquid level (mm); T: pouring time (s); L1: total length of crystallizer (mm); L2: length of dummy rod (mm); v1: actual pulling speed (m / min); v2: target pulling speed (m / min).

[0124] Furthermore, the crystallizer liquid level control includes: during the pouring process, after the ingot is pulled out, controlling the crystallizer liquid level to fluctuate periodically, and the fluctuation range is 800±3 mm.

[0125] Furthermore, the liquid level control stage 1 includes four time periods, namely, a first time period, a second time period, a third time period and a fourth time period, and the four time periods are all within the range of 1-10S. Through the four time periods, the liquid level of the crystallizer rises from the initial liquid level value to the first liquid level setting value; at the beginning of the first time period, the stopper rod opening is adjusted to the first stopper rod opening value, and the first stopper rod opening value is maintained in the first time period; at the end of the first time period, the first stopper rod opening value is instantly reduced by 3-3.5mm from the first stopper rod opening value to obtain the stopper rod initial value of the second time period, and the stopper rod opening value slowly rises in the second time period. The stopper rod opening value at the end of the second period increases by 0.5-1mm relative to the initial value of the stopper rod in the second period; in the third period, the stopper rod opening value continues to increase, and the stopper rod opening value in the third period increases by 1-1.5mm; in the fourth period, the stopper rod opening value continues to increase, and the stopper rod opening value in the fourth period increases by 0.5-1mm; the initial value of the liquid level is 680±5mm; the first liquid level setting value is 752mm; the second liquid level setting value is 775mm; the first stopper rod opening value is: k×10mm, k is the ratio of the current minimum cross-sectional width size of the ingot to the standard cross-sectional width size. The standard cross-sectional size is 1300mm.

[0126] A method for automatically starting pouring in a slab continuous casting tundish, the method adopting the above control method to control the liquid level of the crystallizer.

[0127] Example 3

[0128] A method for controlling liquid level of crystallizer of slab continuous casting machine

[0129] Specific conditions of the slab continuous casting machine: No. 1 slab continuous casting machine has 1 machine and 2 streams, an arc radius of 9.5m, a cross-sectional size of 900~1900mm×230~250mm for the ingot, a slab length of 9~11m, a metallurgical length of 35.1m, an effective length of 800mm for the crystallizer, a pulling speed range of 0.4~2.0m / min, and 11 secondary cooling zones, all of which use air-water mixed cooling.

[0130] 1. Data collection and preprocessing

[0131] The parameters of 52 normal tundish pouring were collected. Normal tundish pouring means that the length of the mixed pouring part is small and the quality of the ingot is good. The main parameters collected include: steel type, section width, tundish temperature, tundish weight, mold level, stopper opening, immersion nozzle depth and casting speed and other process parameters, as shown in Table 1.

[0132] Table 1 Main parameters of data acquisition

[0133]

[0134] The collected data is cleaned, denoised, normalized, and other processes to lay a solid foundation for subsequent model training and prediction.

[0135] 2. Principal Component Analysis (PCA)

[0136] Principal component analysis (PCA) projects high-dimensional data into low-dimensional data. The PCA model projects data samples onto subspaces by establishing principal component subspaces and residual subspaces, and establishes T 2 Statistics and squared prediction error (SPE) statistics.

[0137] Using the singular value decomposition method, the data matrix X n×p The singular value decomposition of X n×p =UΛV T , where U=[u1,u2,…,u n ]∈R n×n ,V=[v1,v2,…,v n ]∈R p×p ,

[0138]

[0139] The column vectors of the U matrix and the V matrix are orthogonal to each other, and the length of the vector is 1 (unit vector). The singular value σ after eigenvalue decomposition is i is the eigenvalue λ of the covariance matrix i The square root of . Each eigenvalue λ represents the amount of variance information carried by the corresponding principal component, and they are in the order of λ1≥λ2≥…≥λ k Therefore, it can be expressed in another way where σ1 is the matrix X n×p The first principal component of , v1 is the first principal direction, and so on.

[0140] Cumulative Percent Variance (CPV) is the proportion of data variation explained by the first h principal components to the total data variation. The cumulative contribution rate of the first h principal components can be expressed as Among them, λ j Represents the eigenvalue corresponding to the jth principal component. Generally, when the cumulative variance contribution rate reaches 85% or higher, it is generally considered that enough original data information has been captured. According to specific needs, the number of the first 4 principal components is selected according to the requirements of model accuracy and visualization. The results of PCA analysis of the 52 samples of automatic pouring parameters of the tundish collected this time are as follows: Figure 5 shown.

[0141] 3. T 2 , SPE statistic

[0142] (1)T 2 Statistics

[0143] T 2 The steps to calculate the statistic are as follows:

[0144] S1.1 Center the original data of the parameters, subtract the mean of each variable so that the mean of the data is zero;

[0145] S1.2. Calculate the covariance matrix of the data to reflect the correlation between the variables;

[0146] S1.3 Perform eigenvalue decomposition on the covariance matrix to find its eigenvalues ​​and eigenvectors;

[0147] S1.4 multiplies the original data by the eigenvector matrix to obtain the projected data, i.e., the principal components;

[0148] S1.5 Calculation of T 2 Statistics, the formula is: Among them, z i is the i-th principal component, λ i is the i-th eigenvalue, k is the number of principal components;

[0149] S1.6 Calculation of T 2 The control limit of the statistic is: Where n is the number of samples, k is the number of principal components, α is the significance level, and F α (k,n--k) is the alpha quantile of an F-distribution with k and n--k degrees of freedom.

[0150] Compare T 2 Statistics and control limits, if T 2 If the statistic is greater than the control limit, the null hypothesis is rejected, that is, the means are not equal or not equal to the given value; if T 2 If the statistic is less than or equal to the control limit, then accept the null hypothesis that the means are equal or equal to the given value.

[0151] (2) SPE statistics

[0152] The SPE statistic is a fault diagnosis method based on principal component analysis (PCA). It is used to measure the projection of a sample on the residual subspace. It reflects the amount of information that cannot be described by the principal component model. It can detect faults of non-principal variables or changes in the correlation between variables.

[0153] The implementation steps of SPE statistics are as follows:

[0154] S2.1 preprocessing the raw data of the parameter, wherein the preprocessing includes centering, standardization or removal of outliers;

[0155] S2.2 Perform principal component analysis on the preprocessed data to obtain its principal component matrix and contribution rate;

[0156] S2.3 Determine the number of principal components based on the contribution rate, and select the principal components with a cumulative contribution rate of more than 90%;

[0157] S2.4 Preprocess the new observation data so that they have the same scale as the original data.

[0158] S2.5 Calculate the SPE statistic, the formula is: SPE = |x 2 |=|(I―PP T )x| 2 , where x is the new observation data, P is the principal component matrix, and I is the identity matrix;

[0159] S2.6 Calculate the control limits of the SPE statistic using the formula: In the formula, For X n×p The i-th power of the j-th eigenvalue of the covariance matrix, u α is the critical value of the normal distribution at a significance level of α, g is the number of principal components retained in the model, and p is the number of variables.

[0160] Compare the SPE statistic and the control limit. If the SPE statistic is greater than the control limit, it is considered that the observed data is faulty; if the SPE statistic is less than or equal to the control limit, the observed data is considered normal.

[0161] Specifically, for the T2 statistics and SPE statistics of 52 samples, it is found that the 14th and 35th samples are out of limit. Figure 6 shown.

[0162] 4. Abnormal contribution analysis

[0163] T 2 Cumulative contribution value By calculating the cumulative contribution of the jth variable of the i-th sample point to the h components To determine which process variables cause T 2 The statistic exceeds the limit. The total contribution of the jth variable of the i-th sample point to the h components The calculation formula is:

[0164] In the formula, x ij represents the observed value of the jth variable at the ith sample point, l jkrepresents the jth component of the kth main direction vector l, s tk Represents the standard deviation of the kth principal component tk. Each sample point consists of p variables, and it is necessary to solve the sum of the projection values ​​of each variable in h directions, that is, the cumulative contribution value of each variable to the h components.

[0165] Total contribution value Contr of SPE: SPE is used to measure the distance of each sample point in the principal component space, reflecting the degree of deviation of the sample point from the normal process. By calculating the total contribution value Contr of the value of the jth variable of the i-th sample point to SPE, it is possible to evaluate which variable contributes more to the overall abnormality. When the squared prediction error (SPE) of the i-th sample point exceeds its control limit, the contribution value of the jth variable of the i-th sample point to SPE can be calculated and the SPE contribution graph can be output. The calculation formula is Among them, x ij is the jth component of the abnormal sample point x(i), z ij is the jth component of the reconstructed data z(i) of x(i). The analysis results of the outliers are as follows: Figure 7 shown.

[0166] In the SPE contribution graph, the height of the large contribution value is relatively high, so the main factor causing the abnormality is the abnormality of the variable. Through statistical control analysis of continuous casting process parameters, it was found that T appeared in the 14th and 35th samples. 2 and the SPE control amount exceeds the range. Taking the 35th sample point as an example, the T 2 The main reason is that variables 4, 5, and 6 exceed the normal range, which correspond to the mold liquid level, stopper opening, and casting speed, respectively. In order to further improve the quality of tundish pouring, the stopper opening is further optimized. The optimization results are shown in Table 2 below.

[0167] Table 2 Main parameters of automatic pouring of tundish after optimization of stopper opening

[0168]

[0169] The present invention has been tested in actual trials in steel mills. According to statistical results, the present method can effectively improve the automatic pouring rate of the tundish. Table 3 is a verification record table, in which March 2023 is the statistics of the pouring rate of the traditional tundish, and November 2023 is the statistics of the pouring rate after the present method is applied. By comparing the statistical data, it can be found that the use of the method of the present invention has improved the automatic pouring rate of the main steel grades, and the successful pouring rate has reached more than 90%.

[0170] Table 3 Slab tundish automatic pouring rate verification record

[0171]

[0172] The embodiments of the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for controlling the liquid level of a slab continuous casting machine crystallizer, the method being used for automatic pouring of a slab continuous casting tundish replacement, characterized in that: The method comprises the following steps: S1, liquid level control stage 1, in which the crystallizer liquid level is adjusted by controlling the stopper rod opening value to raise the crystallizer liquid level from the initial liquid level value to the first liquid level setting value; S2, liquid level control stage 2, in which the crystallizer liquid level is adjusted by controlling the stopper rod opening value, and the crystallizer liquid level is raised from the first liquid level setting value to the second liquid level setting value, and the second liquid level setting value is the liquid level when the billet starts to be pulled out; S3, the billet pulling stage, after the liquid level of the crystallizer reaches the second liquid level setting value, the fan-shaped segment operation is started, the billet is pulled out, and automatic pouring is completed; The adjustment and control of the stopper rod opening value includes: parameter collection and parameter analysis, judging whether the parameters are abnormal according to the result of the parameter analysis, and adjusting the stopper rod opening according to the judgment result to control the crystallizer liquid level; The parameters include: steel type, section width, tundish temperature, tundish weight, mold level, stopper opening, immersion nozzle depth and casting speed; The parameter analysis includes univariate analysis and multivariate analysis, the univariate analysis is used for univariate statistical process control, the multivariate analysis is used for multivariate statistical process control, and the univariate statistical process control and multivariate statistical process control are used for adjustment control of the stopper rod opening value; The single variable statistical process control includes: collecting parameter data in chronological order during the production process, determining control limits based on the parameter data, and controlling the corresponding parameter data within the control limits during the production process; The multivariate statistical process control includes: for a production process in which multiple variables are highly correlated, parameter data of multiple variables are collected simultaneously, and the correlation between multiple variables and the production process and product quality is comprehensively analyzed using multivariate statistical process control to achieve multivariate production process monitoring and quality anomaly analysis.

2. The control method according to claim 1, characterized in that: The adjustment and control of the stopper rod opening value includes: using regression analysis to fit the relationship formula between the stopper rod opening and the amount of steel flowing, the amount of steel flowing Q = f (K) = V * S, where K is the stopper rod opening, V is the flow rate of molten steel, and S is the opening area.

3. The control method according to claim 2, characterized in that: The multivariate analysis includes: using PCA-T 2 -SPE analysis method to analyze, obtain control limits, compare T 2 and SPE statistics and control limits, if T 2 If the and SPE statistics are greater than the control limits, it is considered that the observed data are faulty; if T 2 If the SPE statistic is less than or equal to the control limit, the observed data are considered normal.

4. The control method according to claim 3, characterized in that: The multivariate analysis included: The process monitoring model is established, the operation data of the casting process is collected, and the operation data of the automatic casting of the tundish replacement that meets the casting quality requirements are selected as training data. The training data are first standardized, and then principal component analysis is performed to construct the T 2 and SPE statistics, determine the corresponding control limits, which are the criteria for the model to judge abnormalities; Process monitoring and abnormality monitoring, obtain new tundish pouring data, calculate T with new data 2 The statistics and SPE statistics are compared with the control limits to determine whether they exceed the corresponding control limits. This is used for online real-time abnormal detection of tundish pouring, and automatic monitoring of tundish pouring during continuous casting is achieved. Parameter optimization, for automatic pouring process T 2 The main parameters whose statistics and SPE statistics exceed the control limits are optimized. For the stopper opening, the μ±2σ range of the opening K is selected as the limit to select the stopper opening, μ is the parameter mean, and σ is the parameter standard deviation.

5. The control method according to claim 4, characterized in that: The PCA-T 2 -SPE analysis methods include T 2 The calculation of the statistic, the T 2 The steps to calculate the statistic include: S1.1 Center the original data of the parameters, subtract the mean of each variable so that the mean of the data is zero; S1.2 Calculate the covariance matrix of the data to reflect the correlation between the variables; S1.3 Perform eigenvalue decomposition on the covariance matrix to find its eigenvalues ​​and eigenvectors; S1.4 multiplying the original data by the eigenvector matrix to obtain projected data, wherein the projected data is the principal component; S1.5 Calculation of T 2 Statistics, the formula is: Among them, z i is the i-th principal component, λ i is the i-th eigenvalue, k is the number of principal components; S1.6 Calculation of T 2 The control limit of the statistic is: Where n is the number of samples, k is the number of principal components, α is the significance level, and F α (k,n--k) is the alpha quantile of an F-distribution with k and n--k degrees of freedom.

6. The control method according to claim 4, characterized in that: The PCA-T 2 - The SPE analysis method includes the realization of SPE statistics, and the steps of the method for realizing SPE statistics include: S2.1 preprocessing the raw data of the parameter, wherein the preprocessing includes centering, standardization or removal of outliers; S2.2 Perform principal component analysis on the preprocessed data to obtain its principal component matrix and contribution rate; S2.3 Determine the number of principal components according to the contribution rate, and select the principal components with a cumulative contribution rate of more than 90%; S2.4 Preprocess the new observation data to make it have the same scale as the original data; S2.5 Calculate the SPE statistic, the formula is: SPE = |x 2 |=|(I―PP T )x| 2 , where x is the new observation data, P is the principal component matrix, and I is the identity matrix; S2.6 Calculate the control limits of the SPE statistic.

7. The control method according to claim 6, characterized in that: The liquid level of the crystallizer is raised in a first fast and then slow manner. The liquid level height of the crystallizer during the pouring process in the automatic pouring model is: Among them, h is the liquid level height of the crystallizer, T is the pouring time, L1 is the total length of the crystallizer; L2 is the length of the dummy rod, v1 is the actual pulling speed, and v2 is the target pulling speed.

8. The control method according to claim 7, characterized in that: The crystallizer liquid level control includes: during the pouring process, after the ingot is pulled out, controlling the crystallizer liquid level to fluctuate periodically, and the fluctuation range is 800±3mm.

9. The control method according to claim 8, characterized in that: The liquid level control stage 1 includes four time periods, namely, the first time period, the second time period, the third time period and the fourth time period, and the four time periods are all within the range of 1-10S. Through the four time periods, the crystallizer liquid level rises from the initial liquid level value to the first liquid level setting value; At the beginning of the first period, the stopper rod opening is adjusted to the first stopper rod opening value, and the first stopper rod opening value is maintained during the first period; at the end of the first period, the first stopper rod opening value is reduced by 3-3.5mm from the first stopper rod opening value to reach the initial value of the second period of the stopper rod, and the stopper rod opening value slowly increases during the second period. At the end of the second period, the stopper rod opening value increases by 0.5-1mm relative to the initial value of the second period of the stopper rod, and the stopper rod opening reaches the end value of the second period of the stopper rod; In the third period, the stopper rod opening value continues to increase based on the end value of the stopper rod in the second period, and the stopper rod opening value increases by 1-1.5 mm in the third period, and the stopper rod opening value reaches the end value of the stopper rod in the third period; During the fourth time period, the stopper rod opening value continues to increase based on the end value of the stopper rod in the third time period, and the stopper rod opening value increases by 0.5-1mm during the fourth time period; the initial value of the liquid level is 680±5mm; the first liquid level setting value is 752mm; the second liquid level setting value is 775mm; the first opening value of the stopper rod is: k×10mm, k is the ratio of the current minimum cross-sectional width of the ingot to the standard cross-sectional width, and the standard cross-sectional width is 1300mm.

10. A method for automatically pouring slab continuous casting tundish, characterized in that: The pouring method adopts the control method as described in any one of claims 1-9 to control the liquid level of the crystallizer.

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