Method for predicting the lifespan of refractory materials

Abstract

The method for predicting the lifespan of a refractory is a method for predicting the lifespan of a refractory that is in contact with molten metal with a deposit on its surface lining a molten steel container, and includes the steps of: acquiring measurement data indicating the surface condition of the refractory measured by a sensor (S22); calculating thickness data from the difference between the measurement data and surface reference data indicating a reference surface condition (S23); calculating deposit thickness data indicating the thickness of the deposit by comparing minimum thickness data, which is calculated based on past thickness data, with thickness data (S24); and predicting the lifespan of the refractory by inputting the deposit thickness data into a lifespan estimation model (S26).

Description

Technical Field

[0004] , , , , ,

[0001] The present disclosure relates to a method for predicting the life of refractories. In particular, the present disclosure relates to a method for predicting the life considering the peeling of monolithic refractories used for the lining of molten steel containers that convey or hold molten steel and perform various refining processes in the steelmaking field.

Background Art

[0002] The refractories used in molten steel containers are divided into a lining that contacts the high-temperature melt and an outer lining (permanent lining) on the casing side such as an iron skin. The lining refractory is damaged by chemical erosion due to contact with molten steel and slag, the occurrence of cracks due to thermal load, and peeling following the occurrence of cracks. Therefore, before the lining refractory completely disappears, it is replaced with a new lining refractory. The damage situation is confirmed by visual monitoring of the lining, in addition to monitoring the temperature of the iron skin using thermocouples embedded in the refractory lining or infrared thermography and measuring the remaining thickness using a three-dimensional laser scanner.

[0003] For example, Patent Document 1 proposes a measurement system for predicting the future state of a refractory lining that is lined on the inner surface of the outer wall of a metallurgical container and exposed to heat while being exposed to molten metal. This system includes one or more laser scanners and a processor. The laser scanner performs a plurality of laser scans on the refractory lining when the metallurgical container is empty. At least one of the laser scanners laser-scans the refractory lining before heating and collects data related to the structural state of the refractory lining before heating. At least one of the laser scanners collects data related to the structural state of the refractory lining after heating. The processor predicts the future state of the lining.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

[0005] Here, the technology described in Patent Document 1 does not assume a state in which there are deposits on the surface of amorphous refractories, and therefore it is difficult to estimate the peeling damage of such refractories.

[0006] In view of these circumstances, the purpose of this disclosure is to provide a method for predicting the lifespan of refractories, taking into account delamination damage to refractories used to transport or hold molten metal and to line the inner surface of molten steel containers. [Means for solving the problem]

[0007] (1) A method for predicting the lifespan of a refractory material according to one embodiment of the present disclosure is: A method for predicting the lifespan of a refractory material that comes into contact with molten metal while having deposits on the surface lining a molten steel container, The steps include: acquiring measurement data indicating the surface condition of the refractory material measured by the sensor; The steps include: calculating thickness data from the difference between the measurement data and surface reference data indicating a reference surface state, A step of calculating adhesion thickness data that indicates the thickness of the adhesion by comparing minimum thickness data, which is calculated based on past thickness data and indicates the minimum thickness, with the aforementioned thickness data, The step includes predicting the lifespan of the refractory material by inputting the aforementioned adhesion thickness data into a lifespan estimation model.

[0008] (2) As one embodiment of the present disclosure, in (1), The life estimation model is a model generated by machine learning, taking past adhesion thickness data as input and life data indicating the past lifespan of the refractory material as output.

[0009] (3) As one embodiment of the present disclosure, in (2), The life estimation model is a model generated by machine learning, with the surface temperature of the refractory material and operational data from the manufacturing of metal products from the molten metal as further inputs.

[0010] (4) As one embodiment of the present disclosure, in (2), The lifetime estimation model is a model generated by machine learning, with the thickness data and the minimum thickness data as further inputs.

[0011] (5) As one embodiment of the present disclosure, in (4), The input image is an image created by combining multiple images that represent the thickness data and minimum thickness data for multiple charges.

[0012] (6) As one embodiment of the present disclosure, in (2), The life estimation model is a model generated by machine learning, with the mean and standard deviation calculated from the thickness data at each measurement point of the refractory material as further input. [Effects of the Invention]

[0013] According to this disclosure, a method for predicting the lifespan of refractories can be provided, which takes into account delamination damage to refractories lining the inner surface of molten steel containers used for transporting or holding molten metal. [Brief explanation of the drawing]

[0014] [Figure 1] Figure 1 is a schematic diagram of a refractory life prediction system that implements a refractory life prediction method according to one embodiment of the present disclosure. [Figure 2] Figure 2 shows an example of the configuration of a molten steel container. [Figure 3] Figure 3 is a diagram illustrating the temperature gradient of refractory materials. [Figure 4] Figure 4 is a diagram illustrating delamination damage. [Figure 5] Figure 5 shows how a molten steel container is measured using a sensor. [Figure 6] FIG. 6 is a flowchart illustrating the process of a method for generating a life estimation model. [Figure 7] FIG. 7 is a flowchart illustrating the process of a refractory life prediction method according to an embodiment of the present disclosure.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, a refractory life prediction method according to an embodiment of the present disclosure will be described with reference to the drawings. Here, the drawings are schematic and may differ from actual shapes, sizes, etc.

[0016] FIG. 1 is a schematic configuration diagram of a refractory life prediction system that executes the refractory life prediction method according to the present embodiment. The refractory life prediction system includes a data processing device 10 and a sensor 20. The refractory life prediction system may further include an operation database 21. The operation database 21 stores operation data when manufacturing metal products from molten metal. The operation database 21 also stores various measurement data related to the manufacturing equipment. The data processing device 10 includes a storage unit 11 and a calculation unit 12. The storage unit 11 stores various data used in the refractory life prediction method, including the life prediction model described later. The calculation unit 12 executes various calculations of the refractory life prediction method. The data processing device 10 predicts the life of a refractory that is lined in a molten steel container 30 (see FIG. 2) and contacts molten metal with deposits on its surface. Hereinafter, the components of the refractory life prediction system will be described individually.

[0017] (Structure of the molten steel container 30) Figure 2 shows an example of the configuration of a molten steel container 30. The molten steel container 30 is sometimes referred to as a ladle. The molten steel container 30 has a multi-layered structure with an inner lining and a permanent lining inside the steel shell. As an inner lining, bricks (shaped refractory material) are installed on the upper part of the sides, and refractory material (unshaped refractory material) is installed on the lower part. On the upper part of the sides, the refractory material is damaged (melted away) by contact with molten slag. For this reason, dense bricks with excellent melting resistance are used. On the lower part of the sides, since it is mainly in contact with molten steel, unshaped refractory material with excellent workability is used. In this embodiment, the refractory material that is the subject of life prediction is the unshaped refractory material installed on the lower part of the sides. Hereinafter, unshaped refractory material will simply be referred to as refractory material.

[0018] The molten steel container 30 receives molten steel from the converter, performs secondary refining within the container, and then discharges the molten steel from the bottom of the container 30 for casting. After that, any remaining slag in the container 30 is discharged by tilting the container 30. The container 30 remains empty until the next batch of molten steel is received from the converter. Receiving molten steel from the converter and receiving the next batch of molten steel constitutes one cycle (one charge).

[0019] (Adhered material and invasive phase) The molten steel container 30 receives molten steel from the converter multiple times until the refractory material is dismantled and repaired. Through use, a deposit called buildup forms on the surface of the molten steel container 30. This deposit is an impurity mixture of slag and steel mixed with the alumina content in the steel that has precipitated on the refractory material.

[0020] Furthermore, during the secondary refining process, slag penetrates into the refractory material of the molten steel container 30 through open vents. The refractory material and slag react chemically to form compounds. Here, Figure 3 is a diagram illustrating the temperature gradient of the refractory material. The refractory material has a temperature gradient where the working surface side in contact with the molten steel is hotter, and the back side (the side away from the molten steel) is hotter. Because the temperature inside the refractory material is lower than the working surface side, the solidification temperature is reached at a predetermined location inside the refractory material. The region of refractory material where slag reacts with the refractory material and solidifies is called the wetting phase. The region of refractory material where slag does not penetrate and retains the original structure is called the sound phase. Here, since the thermal conductivity of the adhering material and the wetting phase differs from that of the sound phase, the temperature gradient is not strictly linear, but in Figure 3, the temperature gradient is shown as a straight line for convenience.

[0021] (Changes in thickness) The refractory material of the molten steel container 30 decreases in thickness with use. The factors causing this decrease in thickness are delamination damage and melting. Of these, delamination damage occurs suddenly. Conventional technology has not included the estimation of delamination damage. When delamination damage occurs, there is a risk of steel leakage due to the rapid thinning of the refractory material, so estimating delamination damage is important for life management, such as repair planning for the refractory material. In addition, as mentioned above, deposits are formed on the surface of the refractory material. The thickness of these deposits increases with use.

[0022] (Splitting damage) Figure 4 is a diagram illustrating delamination damage and shows a cross-section of a portion of the molten steel container 30. As described above, the refractory material consists of an adhering layer, a wetting phase, and a sound phase. Due to the effects of heating and cooling, as well as differences in physical properties, as shown in Figure 4, a portion of the refractory material on the working surface (the surface in contact with the molten steel) may delaminate at the interface between the wetting phase and the sound phase.

[0023] (Estimated peeling damage) In this embodiment, the refractory life prediction system measures the surface of the molten steel container 30 with the sensor 20 before receiving the next batch of molten steel from the converter. Here, the surface of the molten steel container 30 is the working surface of the refractory material of the molten steel container 30 (see Figure 4). The sensor 20 is not limited to any specific device as long as it is capable of measuring surface shape, but may be, for example, a 3D laser scanner or an infrared thermograph. In this embodiment, the sensor 20 will be described as a 3D laser scanner.

[0024] Figure 5 shows the process of measuring the molten steel vessel 30 with a 3D laser scanner. The 3D laser scanner measures the surface condition of the refractory material at each charge before receiving the next batch of molten steel from the converter. As shown in Figure 5, the surface condition is measured at multiple measurement points on the refractory material. In this embodiment, the 3D laser scanner also measures the molten steel vessel 30 before the refractory material is applied (permanent lining only). The measurement data of the molten steel vessel 30 before the refractory material is applied is used as surface reference data indicating a standard surface condition. In this embodiment, all data measured by the 3D laser scanner is stored in the operation database 21.

[0025] The calculation unit 12 of the data processing device 10 acquires measurement data indicating the surface condition of the measured refractory material from the operation database 21 or a 3D laser scanner. The calculation unit 12 also acquires surface reference data indicating the reference surface condition of the molten steel container 30. The calculation unit 12 can calculate thickness data from the difference between the measurement data and the surface reference data. The thickness data indicates the thickness of the refractory material at each measurement point. Here, as shown in Figure 4, the calculated thickness is the total thickness of the refractory material and the adhering material. Hereafter, the thickness of the adhering material alone will be referred to as the adhering thickness.

[0026] As described above, the thickness of the refractory material may become thinner than its initial thickness due to sudden delamination damage. Therefore, if the adhesion thickness is calculated using the initial thickness of the refractory material after the thickness has changed due to delamination damage, an accurate value cannot be obtained. In this embodiment, therefore, minimum thickness data, which is calculated based on past thickness data and indicates the minimum thickness, is used. More specifically, the thickness data of the refractory material after its construction is stored in the storage unit 11 as a measurement history in chronological order, and the calculation unit 12 identifies the minimum value of the refractory material's thickness (minimum thickness) by comparing it with past thickness data. For example, in the example in Figure 4, suppose the initial thickness of the refractory material was the minimum thickness (left figure), but delamination damage occurs. In this case, the calculation unit 12 updates the minimum thickness (right figure). By updating the minimum thickness in this way, even if delamination damage occurs, the calculation unit 12 can accurately calculate the adhesion thickness data by comparing the minimum thickness data with the thickness data.

[0027] The calculation unit 12 predicts the lifespan of the refractory by inputting the adhesion thickness data into the lifespan estimation model. Before executing the process of predicting the lifespan of the refractory, the calculation unit 12 generates a lifespan estimation model and stores it in the storage unit 11. In this embodiment, the lifespan estimation model is a model generated by machine learning, with past adhesion thickness data as input and lifespan data indicating the lifespan of past refractory materials as output. In other words, the lifespan estimation model in this embodiment is a trained model generated by machine learning using training data in which accumulated past adhesion thickness data is used as explanatory variables and lifespan data indicating the lifespan of refractory materials corresponding to that adhesion thickness data is used as the objective variable. The lifespan data may be classified (labeled), for example, by whether or not there is delamination after one charge. Here, the method for generating the lifespan estimation model is not limited to a specific method, and for example, any of the following first to fourth methods may be executed.

[0028] (First method) As explanatory variables, grayscale images of thickness data and minimum thickness data (unfolded diagrams) may be used. In other words, a lifetime estimation model may be generated by machine learning using images of thickness data and minimum thickness data as further input. In this case, the model is designed to distinguish between the thickness of the invasive phase and the healthy phase, which is expected to improve the accuracy of lifetime prediction. Here, a convolutional neural network may be used as the machine learning method.

[0029] (Second method) As explanatory variables, a combination of multiple (e.g., three) grayscale images of unfolded diagrams of thickness data and minimum thickness data may be used. In other words, the input image may be an image created by combining multiple images of thickness data and minimum thickness data for multiple charges. Here, a convolutional neural network may be used as the machine learning method.

[0030] (Third method) As explanatory variables, surface temperature data of refractories and operational data used in steel manufacturing may be used. In other words, a life estimation model may be generated by machine learning using the surface temperature of refractories and operational data used in manufacturing metal products from molten metal as further inputs. It is thought that inputting, for example, the highest temperature as surface temperature data of refractories will improve the accuracy of life prediction. Also, it is thought that inputting, for example, the components of molten steel that are significantly involved in chemical reactions with refractories as operational data will improve the accuracy of life prediction. Here, a convolutional neural network may be used as the machine learning method.

[0031] (Fourth method) The mean and standard deviation calculated from the thickness of the refractory material at each measurement point may be used as explanatory variables. In other words, a lifetime estimation model may be generated by machine learning using the mean and standard deviation calculated from the thickness data at each measurement point of the refractory material as input. Here, a gradient boosting tree may be used as the machine learning method.

[0032] Here, the data processing device 10 may acquire a lifetime estimation model generated by another device and store it in the storage unit 11, but it is preferable that the calculation unit 12 has a function (learning function) to generate lifetime estimation models, as in this embodiment. By having a learning function in the calculation unit 12, the lifetime estimation model stored in the storage unit 11 can be updated, thereby improving performance.

[0033] The performance of the lifetime estimation model may be evaluated by the error in the predicted values, for example, using test data. The mean absolute error (MAE) may be used as the error in the predicted values ​​for evaluation. If the magnitude of the error in the predicted values ​​is greater than a predetermined threshold, the performance may be deemed insufficient, and the calculation unit 12 may retrain the lifetime estimation model. The threshold may be determined according to the required prediction performance. Furthermore, the lifetime estimation model may be evaluated by recall and precision. Here, recall is an index representing the proportion of events that actually delaminated that the model successfully determined to delaminate. Precision is an index representing the proportion of events that actually delaminated that the model predicted to delaminate.

[0034] The machine learning methods are not limited to convolutional neural networks and gradient boosting trees. Furthermore, the lifetime estimation model may be a combination of multiple models. Additionally, data from various sensors may be added as explanatory variables.

[0035] The data processing device 10 may be implemented as, for example, a computer. The computer may include a storage device such as memory, a control device such as a CPU (central processing unit), and other processors. In this case, the storage unit 11 may be implemented as the storage device of the computer. The arithmetic unit 12 may be implemented by the control device of the computer by executing a program. The program may be stored in the storage device and read out by the control device when it is to be executed.

[0036] (Method for generating lifespan estimation models) Figure 6 is a flowchart illustrating the process for generating a life estimation model. The generation of the life estimation model is performed before the execution of the refractory life prediction method. The calculation unit 12 acquires surface reference data (step S11). The calculation unit 12 also acquires past measurement data (step S12). The calculation unit 12 calculates thickness data from the difference between the measurement data and the surface reference data (step S13). The calculation unit 12 compares the minimum thickness data with the thickness data to calculate the adhesion thickness data (step S14). As described above, the minimum thickness data is updated when delamination damage occurs by comparison with past thickness data. The calculation unit 12 also acquires life data indicating the past life of the refractory, corresponding to the past measurement data (step S15). The calculation unit 12 generates a life estimation model using machine learning (step S16). The calculation unit 12 evaluates the generated life estimation model using test data and calculates the error in the predicted value (step S17). If the magnitude of the error is greater than a predetermined threshold (Yes in step S18), the calculation unit 12 determines that the performance is insufficient and returns to the process in step S16 to retrain the lifetime estimation model. If the magnitude of the error is less than or equal to a predetermined threshold (No in step S18), the calculation unit 12 determines that the performance is sufficient and stores the generated lifetime estimation model in the storage unit 11, ending the series of processes.

[0037] (Method for predicting the lifespan of refractory materials) Figure 7 is a flowchart illustrating the processing of the refractory life prediction method according to this embodiment. The calculation unit 12 acquires surface reference data (step S21). The calculation unit 12 also acquires measurement data (step S22). The calculation unit 12 calculates thickness data from the difference between the measurement data and the surface reference data (step S23). The calculation unit 12 compares the minimum thickness data with the thickness data to calculate the adhesion thickness data (step S24). As described above, the minimum thickness data is updated when delamination damage occurs by comparison with past thickness data. The calculation unit 12 also acquires a life estimation model from the storage unit 11 (step S25). The calculation unit 12 predicts the life of the refractory by inputting the adhesion thickness data into the life estimation model (step S26).

[0038] Here, the processing for generating the lifetime estimation model in Figure 6 and the processing for predicting the lifetime in Figure 7 are examples. For example, when the first method described above is adopted, in the processing for generating the lifetime estimation model and predicting the lifetime, the calculation unit 12 further uses thickness data and minimum thickness data as inputs (explanatory variables) to the lifetime estimation model.

[0039] (Examples) The molten steel container 30 has a cylindrical inner surface, a circular opening with a diameter of 4.5 m, and a height of 4 m. A 3D laser scanner was installed at a height of 2.8 m from the opening plane of the molten steel container 30 to measure the surface shape of the refractory. The data processing device 10 calculated the thickness of the refractory as described above, and the lifespan of the refractory was predicted. In generating the lifespan estimation model, 20,000 images of the refractory thickness data were prepared and classified into 14,000 training data and 6,000 test data. Images were labeled based on whether or not delamination occurred after one charge. When the lifespan estimation model was evaluated using the test data, cross-validation confirmed that it was not overfitting, and its usefulness was confirmed.

[0040] As described above, the refractory life prediction method according to this embodiment can take into account (estimate) delamination damage to the refractory material used for lining the molten steel container 30. By estimating delamination damage to the refractory material, the risk of steel leakage is reduced, and it becomes possible to accurately set the timing for repairing the refractory material.

[0041] While embodiments relating to this disclosure have been described based on the drawings and examples, the above embodiments are illustrative of methods for realizing the technical idea of ​​this disclosure and do not specify a particular configuration. Those skilled in the art should note that it will be easy to make various modifications or alterations based on this disclosure. Therefore, it should be noted that these modifications or alterations are within the scope of this disclosure. [Explanation of Symbols]

[0042] 10 Data Processing Devices 11 Storage section 12 Arithmetic section 20 sensors 21. Operations Database 30 Molten steel containers

Claims

1. A method for predicting the lifespan of a refractory material that comes into contact with molten metal while having deposits on the surface lining a molten steel container, The steps include: acquiring measurement data indicating the surface condition of the refractory material measured by the sensor; The steps include: calculating thickness data from the difference between the measurement data and surface reference data indicating a reference surface state, A step of calculating adhesion thickness data that indicates the thickness of the adhesion by comparing minimum thickness data, which is calculated based on past thickness data and indicates the minimum thickness, with the aforementioned thickness data, A method for predicting the lifespan of a refractory material, comprising the step of inputting the aforementioned adhesion thickness data into a lifespan estimation model to predict the lifespan of the refractory material.

2. The method for predicting the lifespan of a refractory material according to claim 1, wherein the lifespan estimation model is a model generated by machine learning, with past adhesion thickness data as input and lifespan data indicating the lifespan of the refractory material in the past as output.

3. The method for predicting the lifespan of a refractory material according to claim 2, wherein the lifespan estimation model is a model generated by machine learning, with the surface temperature of the refractory material and operational data from the manufacturing of metal products from the molten metal as further inputs.

4. The method for predicting the lifespan of a refractory material according to claim 2, wherein the lifespan estimation model is a model generated by machine learning using the thickness data and the minimum thickness data as further inputs.

5. The method for predicting the lifespan of a refractory material according to claim 4, wherein the input image is an image obtained by combining multiple images of the thickness data and the minimum thickness data for multiple charges.

6. The method for predicting the lifespan of a refractory material according to claim 2, wherein the lifespan estimation model is a model generated by machine learning, with the mean and standard deviation calculated from the thickness data at each measurement point of the refractory material as further inputs.

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