Parametric evaluation of ladle refractory brick erosion and ladle safety monitoring method based on fitting regression model

Abstract

The present invention discloses a method for parameterized evaluation of ladle refractory brick erosion and ladle safety monitoring based on a fitting regression model. The method uses the historical offline ladle age of a certain type of steel furnace and metallurgical process parameters to form a fitting regression model. The variance analysis method is used to find the significant factors affecting the erosion of the ladle refractory bricks, and the erosion ability of each smelting process parameter on the ladle refractory bricks is digitized; the fitting regression model response is optimized relative to historical data to find the maximum allowable ladle age. The smelting target parameters set by the process are substituted into the fitting regression model, and the 95% confidence interval and 95% prediction interval of the calculated value are output; the actual smelting process parameters of each furnace are brought into the fitting regression model, and the calculated value is obtained and compared with the 95% prediction interval. If it is lower than the 95% prediction interval, the system will issue an alarm for abnormal erosion of the ladle bricks. The present invention can estimate the reasonable service life of refractory bricks, alarm for abnormal ladle smelting processes, and reduce operational risks.

Description

Technical Field

[0001] The invention belongs to the technical field of improving the service life of ladle refractory bricks used in steelmaking, and specifically provides a parameterized evaluation method for erosion of ladle refractory bricks and a ladle safety monitoring method based on a fitting regression model. Background Art

[0002] A ladle is a container used for refining, holding, and transferring molten steel. It consists of a steel shell and an internal refractory lining. The refractory lining includes a working layer, a permanent layer, and an insulating layer. Refractory bricks include slag line bricks, bath bricks, ladle bottom bricks, breather bricks, and functional taphole components.

[0003] The life of ladle refractory bricks is affected by the quality of each ladle refractory brick, metallurgical process and usage scheduling factors. The failure of ladle refractory bricks mainly includes the following three types:

[0004] First: chemical erosion by steel slag or molten steel;

[0005] Second: The erosion caused by the stirring of molten steel in the ladle and the instantaneous high temperature and extreme temperature changes of the electrodes;

[0006] Third: The internal stress caused by the thermal shock causes mechanical damage such as cracks inside the refractory bricks.

[0007] The three failure factors mentioned above act together. Different steel grades erode refractory bricks to varying degrees due to varying slag compositions and smelting processes. Quantifying and digitizing these erosion factors is crucial for assessing refractory brick erosion, determining optimal ladle life, and ensuring ladle operation safety.

[0008] Because ladle refractory bricks are subject to numerous factors affecting their erosion, it is difficult to calculate and digitize the failure capacity of each erosion factor based on chemical reaction principles, thermodynamic principles, and fracture mechanics theory. It is also impossible to determine the actual impact of various metallurgical process factors on the erosion capacity of ladle refractory bricks during use, nor is it possible to determine the maximum allowable ladle life that can be achieved under safe conditions. Summary of the Invention

[0009] Purpose of the invention: In response to the above technical problems, the present invention proposes a parametric evaluation method for the erosion of ladle refractory bricks and a ladle safety monitoring method based on a fitting regression model. Under the premise of ensuring the safety of no ladle penetration, the smelting process parameters are appropriately adjusted, the refractory bricks of the ladle are reasonably used, and the ladle life is improved; the reasonable service life of the refractory bricks is estimated; the ladle smelting process is monitored to reduce the risk of operation and use.

[0010] In order to achieve the above technical objectives, the technical means adopted by the present invention are:

[0011] A method for parameterized evaluation of ladle refractory brick corrosion and ladle safety monitoring based on a fitted regression model comprises the following steps:

[0012] (1) Digitalize the erosion capacity of various factors affecting refractory bricks during metallurgical processes:

[0013] Analyze the factors that affect the age of refractory bricks in different parts of the ladle, and collect data on the age of all steel grades and various metallurgical process parameters;

[0014] (2) Fitting regression model of the corrosion level of refractory bricks used in ladle and various influencing factors:

[0015] Using data analysis software, a regression model is fitted for the refractory bricks at different locations of the ladle using the sum of the ladle ages of all steel grades and the parameter data of each metallurgical process obtained in step (1);

[0016] (3) Determine the maximum allowable age of refractory bricks in different parts:

[0017] The response of the fitted regression model obtained for the refractory bricks in different parts of the ladle for each type of steel ladle age is optimized separately, and the maximum value of the response optimization is taken to determine the maximum allowable ladle age of the refractory bricks in that part;

[0018] (4) Evaluate whether the smelting process of each ladle is abnormal:

[0019] Substitute the target metallurgical process parameters of the process setting into the multiple fitting regression models obtained in step (2), and obtain the 95% confidence interval and 95% prediction interval of the calculated value of the fitting regression model. Collect the actual metallurgical process parameter data of each furnace smelting process, and substitute it into the fitting regression model to obtain the calculated value. Compare the calculated value with the 95% prediction interval. If it is lower than the range, an abnormal corrosion alarm is issued; if the calculated value is within the 95% prediction interval, the calculated ladle age is accumulated by 1. If the calculated value is not within the interval, the reciprocal of the calculated value is recorded as the calculated ladle age of the furnace smelting;

[0020] (5) The calculated ladle age of refractory bricks in different parts of the ladle is accumulated. When the maximum allowable ladle age is reached, the refractory bricks in that part are removed. When the ladle melt pool bricks reach the maximum allowable ladle age, the ladle is taken offline and the refractory bricks are replaced or the ladle reaches the end of its life.

[0021] In step (1), the ladle age of the steel grade is the number of furnaces used to smelt the steel grade in one ladle.

[0022] In step (1), the refractory bricks in different parts of the ladle are divided into three categories according to the different materials of the bricks and the different end-of-use cycles, namely, melt pool bricks and ladle bottom bricks, slag line bricks and breathable bricks, wherein the melt pool bricks and ladle bottom bricks are made of magnesia carbon bricks, which are removed together after use and have the same use cycle, and are classified as the first category of refractory bricks; slag line bricks and breathable bricks are respectively divided into the second category of refractory bricks and the third category of refractory bricks, and fitting regression models of the ladle age and various metallurgical process parameters of this type of refractory bricks are made for different steel grades;

[0023] Factors that affect the age of bath bricks and ladle bottom bricks include: refining tapping temperature, molten steel in ladle time, refining time and oxygen blowing time;

[0024] Factors that affect the age of slag line bricks include the amount of CaF2 added, the amount of dolomite added, refining and tapping temperature, the time the molten steel is in the ladle, the refining time and the oxygen blowing time;

[0025] Factors that affect the life of the air brick include argon blowing amount, argon blowing pressure, refining tapping temperature, molten steel in the ladle time, refining time and oxygen blowing time.

[0026] In step (2), the data analysis software adopts Minitab statistical analysis software.

[0027] In step (4), the target metallurgical process parameters set in the process are the target metallurgical process parameters required to be achieved when formulating the metallurgical process;

[0028] The calculated package age is the package age of the sum of the calculated values ​​of the fitted regression model;

[0029] The 95% prediction interval is an interval output by substituting the target metallurgical process parameters into the fitting regression model and is used to predict whether the calculated value is abnormal relative to historical data.

[0030] In step (5), the calculated ladle age of the air bricks and slag line bricks is accumulated, and when the maximum allowable ladle age is reached, the ladle is taken offline for replacement. The air bricks and slag line bricks are replaced 2 to 3 times during the entire ladle life cycle; the calculated ladle age of the melt pool bricks and ladle bottom bricks is accumulated, and the ladle is finally taken offline when the maximum allowable ladle age is reached. The melt pool bricks and ladle bottom bricks are not replaced during the entire ladle life cycle.

[0031] Beneficial effects:

[0032] First, the present invention digitizes the factors affecting the service life of refractory bricks in various parts of the ladle during the metallurgical process of smelting various steel grades, compares the sizes of various influencing factors, so as to facilitate the adjustment of the smelting process to improve the service life of the ladle, and makes a fitting regression model, which solves the purpose of reasonably using the refractory bricks of the ladle and improving the service life of the ladle while ensuring the safety of no ladle penetration.

[0033] Second, the present invention incorporates each smelting process parameter into a fitted regression model. The calculated value is then compared with the 95% prediction interval and 95% confidence interval of the calculated value obtained by substituting the target metallurgical process parameters into the fitted regression equation. For heats falling below the 95% prediction interval, an abnormality alarm is issued and abnormal process parameters are investigated. This system monitors the safety of the ladle and estimates the reasonable lifespan of refractory bricks, reducing the operational risks of the ladle. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The fishbone diagram of factors affecting the corrosion of the ladle refractory bricks of the present invention;

[0035] Figure 2 Variance analysis for judging the significance of the fitted regression model and the significance of each metallurgical process factor;

[0036] Figure 3 It is the residual analysis diagram of the fitting regression model of the influence of ladle age on various smelting parameters of molten pool bricks and ladle bottom bricks in smelting SG400 steel grade, and the residual diagram of the response variable;

[0037] Figure 4 It is the residual analysis diagram of the fitting regression model of the influence of ladle age on the smelting parameters of SG400 steel grade on the molten pool brick and ladle bottom brick, and the residual diagram of the smelting time of the refining furnace;

[0038] Figure 5 It is the residual analysis diagram of the fitting regression model of the influence of ladle age on the smelting parameters of SG400 steel grade for molten pool bricks and ladle bottom bricks, and the residual diagram of ladle time;

[0039] Figure 6 The residual analysis diagram of the fitting regression model of the influence of ladle age on various smelting parameters for molten pool bricks and ladle bottom bricks in smelting SG400 steel grade, and the residual diagram of MgO content in slag;

[0040] Figure 7 The response optimization results of the fitting regression model of the molten pool bricks and ladle bottom bricks in the smelting of SG400 steel grade between ladle age and various smelting parameters;

[0041] Figure 8 The response optimization results of the fitting regression model between the ladle age and various smelting parameters for the molten pool bricks and ladle bottom bricks in the smelting of SWRS82B series high carbon steel grades;

[0042] Figure 9 The response optimization results of the fitting regression model between the ladle age and various smelting parameters for the molten pool bricks and ladle bottom bricks in the smelting of HRB600 series steel grades;

[0043] Figure 10When smelting SWRS82B series high carbon steel, the system monitors in real time whether the corrosion of refractory bricks exceeds the 95% prediction interval during each smelting of the bath bricks and the bottom bricks. DETAILED DESCRIPTION

[0044] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] The present invention provides a method for parameterized evaluation of erosion of ladle refractory bricks and ladle safety monitoring based on a fitting regression model. The method digitizes the various erosion capacity factors of refractory bricks in the metallurgical process, fits a regression model, and uses the fitting regression model to judge the digital erosion degree of each furnace of a certain type of steel smelted by this type of refractory bricks. The calculated value obtained by substituting the actual smelting parameters into the fitting regression equation is compared with the 95% prediction interval obtained by substituting the target smelting parameters into the fitting regression equation to determine whether the erosion process is abnormal. The ladle smelting process is monitored to reduce the risk of operation and use. The regression equation response is optimized to obtain a reasonable maximum allowable ladle age. The calculated ladle age obtained by substituting the actual smelting parameters of each furnace into the fitting regression equation is added to the maximum allowable ladle age to estimate the reasonable service life of the refractory bricks. The method specifically includes the following steps:

[0046] A method for parameterized evaluation of ladle refractory brick corrosion and ladle safety monitoring based on a fitted regression model comprises the following steps:

[0047] (1) Digitalize the erosion capacity of various factors affecting refractory bricks during metallurgical processes:

[0048] Analyze the factors that affect the age of refractory bricks in different parts of the ladle, and collect data on the age of all steel grades and metallurgical process parameters of each influencing factor;

[0049] (2) Fitting regression model of the corrosion level of refractory bricks used in ladle and various influencing factors:

[0050] Using data analysis software, the sum of the ladle ages of all steel grades and the parameter data of the metallurgical processes of various influencing factors obtained in step (1) is used to make a fitting regression model, and the above-mentioned method is used to make the fitting regression model for the refractory bricks in different parts of the ladle;

[0051] (3) Determine the maximum allowable age of each type of refractory brick:

[0052] Perform response optimization on the fitted regression model obtained for each type of refractory brick for each type of steel, and take the maximum value of the response optimization to determine the maximum allowable age of the refractory brick;

[0053] (4) Evaluate whether the ladle smelting process is abnormal:

[0054] Substitute the set target metallurgical process parameters into the fitting regression model to obtain the 95% confidence interval and 95% prediction interval of the calculated value of the fitting regression model. Collect the actual metallurgical process parameter data of each smelting process and substitute it into the fitting regression model to obtain the calculated value. The calculated value is compared with the 95% prediction interval. If it is lower than the range, an abnormal erosion alarm is issued; if the calculated value exceeds the lower limit of the 95% confidence interval, the specific ladle age record value = 1 / calculated value. Because when the settlement value is less than 1, it means that the degree of erosion of the refractory bricks by the smelting process parameters is higher than the historical average, so the ladle age of the refractory bricks will be lower, so the ladle age value to be recorded needs to be converted to the reciprocal of the calculated value; similarly, if the calculated value exceeds the upper limit of the confidence interval, the ladle age record value is less than 1, and the specific recorded value = 1 / calculated value.

[0055] (5) The calculated ladle age of refractory bricks in different parts of the ladle is accumulated. When the maximum allowable ladle age is reached, the refractory bricks in that part are removed. When the ladle melt pool bricks reach the maximum allowable ladle age, the ladle is taken offline and replaced with the refractory bricks of that type, or the ladle reaches the end of its life.

[0056] Preferably, in step (1), the ladle age of the steel grade is the number of times the steel grade is smelted in one ladle.

[0057] Preferably, in step (1), the refractory bricks in different parts of the ladle include: melt pool bricks and ladle bottom bricks, slag line bricks and breathable bricks, wherein the melt pool bricks and ladle bottom bricks are made of magnesia carbon bricks, which are removed together after use and have the same service life; the melt pool bricks and ladle bottom bricks, slag line bricks and breathable bricks in the ladle are made of different materials and have different end-of-use cycles, and need to be treated differently, and fitting regression models of the ladle age and various metallurgical process parameters of this type of refractory bricks are made for different steel grades respectively;

[0058] Factors that affect the age of bath bricks and ladle bottom bricks include: refining tapping temperature, molten steel in ladle time, refining time and oxygen blowing time;

[0059] Factors that affect the age of slag line bricks include the amount of CaF2 added, the amount of dolomite added, refining and tapping temperature, the time the molten steel is in the ladle, the refining time and the oxygen blowing time;

[0060] Factors that affect the life of the air brick include argon blowing amount, argon blowing pressure, refining tapping temperature, molten steel in the ladle time, refining time and oxygen blowing time.

[0061] Preferably, in step (4), the target metallurgical process parameters set are target process parameters required to be achieved when formulating the metallurgical process.

[0062] Preferably, in step (2), the data analysis software adopts Minitab statistical analysis software.

[0063] Preferably, in step (4), the 95% prediction interval is an interval output by substituting the target metallurgical process parameters required by the process into the fitting regression model to predict whether the calculated value is abnormal relative to the historical data.

[0064] Preferably, the calculated ladle age in step (4) is the ladle age obtained by summing the calculated values ​​of the fitted regression model; the actual ladle age is the number of times the ladle is actually used for smelting.

[0065] Preferably, in step (5), the calculated ladle age of the air bricks and slag line bricks is accumulated, and when the maximum allowable ladle age is reached, the ladle is taken offline for replacement. The air bricks and slag line bricks are replaced 2 to 3 times during the entire ladle life cycle; the calculated ladle age of the melt pool bricks and ladle bottom bricks is accumulated and finally taken offline when the maximum allowable ladle age is reached. The melt pool bricks and ladle bottom bricks are not replaced during the entire ladle life cycle, and are taken offline when they are severely corroded.

[0066] Example

[0067] The regression equation fitting of the corrosion ability of molten pool bricks, bottom bricks, slag line bricks and breathable bricks in smelting SG400 series, HRB600 low carbon steel and smelting SWRS82B series high carbon steel includes the following steps:

[0068] (1) Analyze the factors that affect the service life of ladle refractory bricks during the smelting process and make a fishbone diagram, such as Figure 1 The data on the ladle age of each SG400 series steel ladle, refining time, time in ladle, refining tapping temperature, dolomite addition amount, fluorite addition amount, and oxygen blowing amount for each ladle of SG400 series steel grades mentioned later, which were produced in the past with a low carbon steel ratio greater than 50%, are summarized.

[0069] For SG400 series steel grades smelted in the same ladle, the smelting process parameters of all ladle ages are summed up according to the type, and the summary table is 1:

[0070] Table 1

[0071]

[0072]

[0073] (2) Import the above data into Minitab software, select Statistics--Regression--Fit Regression Model, input the response and continuous predictor variables in turn; select the graphics option, and make a four-in-one graph of the residuals and variables of the continuous predictor variables in turn; select standardization for the residuals in the graph; select stepwise regression fitting.

[0074] The significance of the fitting regression model and the significance of each metallurgical process factor were judged. It was found that the fitting regression model was significant, and only the refining furnace smelting time, ladle time, and MgO content in the slag were significant.

[0075] like Figure 2 As shown in the figure, the P values ​​are all less than 0.05, which means that these factors have a significant impact on the response variable (steel grade and ladle age).

[0076] like Figure 3 As shown in the figure, the residual analysis of the fitted regression model shows that the residuals of the fitting value for the response variable steel grade ladle age are normally distributed, without bending, bell-shaped or abnormality; the residuals of the fitting value for the refining furnace smelting time, ladle time and MgO content in the slag are all normally distributed, without bending, bell-shaped or abnormality; there are no abnormal values ​​in the residual graph, as shown in the figure. Figure 4 、 Figure 5 、 Figure 6 This indicates that there is no need to add higher-order terms when fitting the regression model.

[0077] The model fitting effect is shown in Table 2. The regression standard error S value is small, and the error percentage of the improved regression model to the total error R-Sq (adjusted) is high enough, indicating that the fit is very high.

[0078] Table 2

[0079] S R-sq R-sq(adjustment) R-sq(prediction) 0.555795 99.92％ 99.89％ 99.80％

[0080] Fitting regression model 1 is

[0081] Steel grade ladle age = -0.464 + 0.01374 × smelting time in refining furnace + 0.002306 × time in ladle + 0.000056 MgO content in slag.

[0082] Using the same fitting method, the regression model 2 for the ladle age of the SWRS82B series steel grades smelted is: ladle age = 380-0.2369×electric furnace tapping temperature+0.01708×refining furnace smelting time-0.000274×Mg0 content in slag+0.0184×soft stirring time;

[0083] The ladle age fitting regression model 3 for the HRB600 series steel grades smelted is:

[0084]

[0085] (3) Figure 7 As shown in the figure, the response optimizer in Minitab is used to solve the maximum ladle age for the fitted regression model 1, and the maximum ladle age for smelting SG400 steel in the historical offline ladle data is 60 furnaces, with a compliance of 0.99639.

[0086] Similarly, the response optimizer is used to solve the maximum allowable ladle age for fitting regression model 2 and fitting regression model 3, and the maximum ladle age of SWRH82B steel grade produced in the historical off-line ladle data is 29 furnaces. Figure 8 The maximum ladle age for smelting HRB600 steel is 29 furnaces. Figure 9 .

[0087] The reasonable ladle age estimation of molten pool bricks and ladle bottom bricks in smelting SG400 series steel grades and smelting HRB600 series steel grades is shown in Table 3:

[0088] Table 3

[0089]

[0090] In Table 3, high carbon steel refers to HRB600 steel and SWRH82B steel; low carbon steel refers to SG400 series steel.

[0091] According to Table 3, the number of low carbon steel smelting furnaces can reach: 70 × 80% + 70 × 20% × (10 / 51 to 55 / 70) = 59 to 66 furnaces;

[0092] The ladle age for smelting high-carbon steel grades can reach 85 furnaces, and the ladle age for smelting low-carbon steel grades can reach 60 furnaces. Assuming that the corrosion capacity of smelting high-carbon steel on refractory bricks is 1, 1 = 85 furnaces / 85.

[0093] Note: 85 means the ladle for smelting high carbon steel is not corroded and is full brick. 85 is the maximum allowable ladle age in the technical agreement for smelting high carbon steel.

[0094] Then the corrosion capacity of low carbon steel is: 1.25 = (85-5) / [(66 / 70)×70];

[0095] 70 represents the uncorroded full bricks of the ladle for smelting mild steel, and 70 is the maximum allowable ladle age in the technical agreement signed for smelting mild steel;

[0096] Therefore, the maximum erosion capacity of the two molten steel and slag for smelting low carbon steel and smelting high carbon steel is: 1.21:1;

[0097] All low carbon steel is smelted, and the number of furnaces for smelting low carbon steel can reach 70×80%+70×20%×(10 / 51~55 / 70)=59~66 furnaces.

[0098] If all high carbon steel is smelted, the number of smelting furnaces can reach 66×1.25+(85-66)=95 furnaces;

[0099] (4) When smelting SG400 series, HRB600 series low carbon steel and SWRS82B series high carbon steel, the erosion level of refractory bricks by each smelting process parameter is compared with the historical value to evaluate whether there is any abnormality.

[0100] When smelting SWRS82B series steel, the smelting process parameters of one furnace are:

[0101] Electric furnace tapping temperature: 15996℃,

[0102] The refining furnace smelting time is 46.3 minutes.

[0103] The MgO content in the slag is 38.39kg,

[0104] Soft stirring time: 10.7 min.

[0105] The pack age value calculated by fitting regression model 1 is 0.853345, the 95% confidence interval of this value is (-0.16799, 1.87468), and the 95% prediction interval is (-0.82774, 2.53443). The maximum allowable pack age of this type of refractory brick is 95 furnaces. Figure 10 In the figure, the circular points are normal heats, and the triangular points are abnormally corroded heats.

[0106] When the calculated ladle age value is below the 95% prediction interval, it means that the degree of erosion of refractory bricks by the smelting process parameters is higher than the historical average, so the ladle age of the refractory bricks will be lower. Therefore, the ladle age value to be recorded needs to be converted, that is, the reciprocal of the calculated value; similarly, if the calculated value exceeds the upper limit of the confidence interval, the recorded ladle age value is less than 1, and the specific recorded value = 1 / calculated value.

[0107] The same fitting method is used to fit the regression model of the influence of various factors on the age of slag line bricks and breathable brick steel grades.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for parameterized evaluation of ladle refractory brick corrosion and ladle safety monitoring based on a fitted regression model, characterized in that: The following steps are involved: (1) Digitalize the erosion capacity of various factors affecting refractory bricks during metallurgical processes: Analyze the factors that affect the age of refractory bricks in different parts of the ladle, and collect data on the age of all steel grades and various metallurgical process parameters; (2) Fitting regression model of the corrosion level of refractory bricks used in ladle and various influencing factors: Using data analysis software, a regression model is fitted for the refractory bricks in different parts of the ladle using the sum of the ladle age of all steel grades and the parameter data of each metallurgical process obtained in step (1); the significance of the fitted regression model and the significance of each metallurgical process factor are judged, and significant variables are screened based on variance analysis; (3) Determine the maximum allowable age of refractory bricks in different parts of the ladle: The response of the fitted regression model obtained for the refractory bricks in different parts of the ladle for each type of steel ladle age is optimized separately, and the maximum value of the response optimization is taken to determine the maximum allowable ladle age of the refractory bricks in that part; (4) Evaluate whether the smelting process of each ladle is abnormal: Substitute the target metallurgical process parameters of the process setting into the multiple fitting regression models obtained in step (2), and obtain the 95% confidence interval and 95% prediction interval of the calculated value of the fitting regression model. Collect the actual metallurgical process parameter data of each furnace smelting process, and substitute it into the fitting regression model to obtain the calculated value. Compare the calculated value with the 95% prediction interval. If it is lower than the 95% prediction interval, an abnormal erosion alarm is issued; if the calculated value is within the 95% prediction interval, the calculated ladle age is accumulated by 1. If the calculated value is not within the interval, the reciprocal of the calculated value is recorded as the calculated ladle age of the furnace smelting; (5) Accumulate the calculated age of refractory bricks in different parts of the ladle. When the maximum allowable age is reached, remove the refractory bricks in that part. When the ladle molten pool bricks reach the maximum allowable age, the ladle is taken offline and the refractory bricks are replaced or the ladle reaches the end of its life. In step (1), the refractory bricks in different parts of the ladle are divided into three categories according to the different materials of the bricks and the different end-of-use cycles, namely, melt pool bricks and ladle bottom bricks, slag line bricks and breathable bricks, wherein the melt pool bricks and ladle bottom bricks are made of magnesia carbon bricks, which are removed together after use and have the same use cycle, and are classified as the first category of refractory bricks; slag line bricks and breathable bricks are respectively divided into the second category of refractory bricks and the third category of refractory bricks, and fitting regression models of the ladle age and various metallurgical process parameters of this type of refractory bricks are made for different steel grades; Factors that affect the age of bath bricks and ladle bottom bricks include: refining tapping temperature, molten steel in ladle time, refining time and oxygen blowing time; Factors that affect the age of slag line bricks include the amount of CaF2 added, the amount of dolomite added, refining and tapping temperature, the time the molten steel is in the ladle, the refining time and the oxygen blowing time; Factors that affect the life of the air brick include argon blowing amount, argon blowing pressure, refining tapping temperature, molten steel in the ladle time, refining time and oxygen blowing time.

2. The method for parameterized evaluation of ladle refractory brick corrosion and ladle safety monitoring based on a fitted regression model according to claim 1, characterized in that: In step (1), the ladle age of the steel grade is the number of furnaces used to smelt the steel grade in one ladle.

3. The method for parameterized evaluation of ladle refractory brick corrosion and ladle safety monitoring based on a fitted regression model according to claim 1, characterized in that: In step (2), the data analysis software adopts Minitab statistical analysis software.

4. The method for parameterized evaluation of ladle refractory brick corrosion and ladle safety monitoring based on a fitted regression model according to claim 1, characterized in that: In step (4), the target metallurgical process parameters set in the process are the target metallurgical process parameters required to be achieved when formulating the metallurgical process; The calculated package age is the package age of the sum of the calculated values ​​of the fitted regression model; The 95% prediction interval is an interval output by substituting the target metallurgical process parameters into the fitting regression model and is used to predict whether the calculated value is abnormal relative to historical data.

5. The method for parameterized evaluation of ladle refractory brick corrosion and ladle safety monitoring based on a fitted regression model according to claim 1, characterized in that: In step (5), the calculated ladle age of the air bricks and slag line bricks is accumulated, and when the maximum allowable ladle age is reached, the ladle is taken offline for replacement. The air bricks and slag line bricks are replaced 2 to 3 times during the entire ladle life cycle; the calculated ladle age of the melt pool bricks and ladle bottom bricks is accumulated, and the ladle is finally taken offline when the maximum allowable ladle age is reached. The melt pool bricks and ladle bottom bricks are not replaced during the entire ladle life cycle.

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