Breakout prediction method and operation method of continuous casting machine

Abstract

The breakout prediction method of the present invention includes steps of inputting dimensions of a slab withdrawn from a mold in a continuous casting machine; detecting a temperature of the mold by a plurality of thermometers embedded in the mold; performing an interpolation process on detection temperatures detected by the plurality of thermometers according to the dimensions of the slab; calculating a temperature change amount by comparing with a temperature before a first period; obtaining a standard deviation of the temperature change amount for each thermometer within a second period; normalizing the temperature change by dividing the temperature change amount by the standard deviation; calculating, based on the temperature change amount calculated by performing the interpolation process, a component in a direction orthogonal to an influence coefficient vector obtained from principal component analysis as a deviation degree from a normal operation when no breakout has occurred; and predicting the breakout based on the deviation degree.

Description

Technical Field

[0001] The present invention relates to a breakout prediction method and an operation method of a continuous casting machine.

Background Art

[0002] Patent Documents 1 and 2 disclose a method of detecting characteristic temperature behavior using a temperature sensor disposed in a mold and predicting a breakout. Further, Patent Document 3 discloses a method of calculating a degree of deviation from the mold temperature during normal operation in which a breakout has not occurred, and predicting a breakout based on the calculated degree of deviation.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] The form of a breakout is roughly classified into two forms: a restrictive breakout and a foreign matter entrapment breakout. In a restrictive breakout, the cast slab is stuck to the mold, and relatively large temperature fluctuations are often observed. Regarding a foreign matter entrapment breakout, since it occurs when casting proceeds with foreign matters such as powder adhering to the surface of the cast slab, it has characteristics such as an extremely small change range and magnitude of the mold temperature change.

[0005] In the methods disclosed in Patent Documents 1 and 2, the temporal change and spatial distribution of the mold temperature are detected by the characteristic temperature when breakout actually occurs. Binding breakouts and foreign object entrapment breakouts are detected by different methods. Binding breakouts with relatively large temperature changes are determined by the temperature relationship at different casting heights (usually high temperature in the casting direction, high temperature in the downward casting direction during abnormal times). However, since foreign object entrapment breakouts have small temperature changes and are difficult to detect, they are determined by the magnitude of temperature fluctuations and the downward propagation of temperature changes. However, since the mold temperature does not operate with a constant distribution and the temperature changes depending on the internal situation of the mold, there are many false detections and often detection omissions occur.

[0006] Also, in the method disclosed in Patent Document 3, breakout is predicted from the degree of deviation from the mold temperature during normal operation. Therefore, when the slab adheres to the mold like a binding breakout and relatively large temperature fluctuations are observed, it can be detected sufficiently. On the other hand, foreign object entrapment breakouts that occur when casting proceeds with foreign substances such as powder adhering to the surface of the slab have extremely small change widths and magnitudes of the mold temperature change and could not be detected sufficiently.

[0007] The present invention has been made in view of the above problems, and its object is to provide a breakout prediction method and an operation method for a continuous casting machine capable of detecting foreign object entrapment breakouts with small change widths and temperature fluctuations of the mold temperature with sufficient detection accuracy.

Means for Solving the Problems

[0008] In order to solve the above-described problems and achieve the object, (1) The breakout prediction method according to the present invention includes: a step of inputting the dimensions of a slab withdrawn from a mold in a continuous casting machine; a step of detecting the temperature of the mold by a plurality of thermometers embedded in the mold; a step of performing interpolation processing on the detected temperatures detected by the plurality of thermometers according to the dimensions of the slab; a step of calculating a temperature change amount by comparing with the temperature before the first period; a step of obtaining a standard deviation of the temperature change amount for each of the thermometers within the second period; a step of normalizing the temperature change by dividing the temperature change amount by the standard deviation; a step of calculating, as a deviation degree from normal operation when no breakout has occurred, a component in a direction orthogonal to the influence coefficient vector obtained from principal component analysis based on the temperature change amount calculated by performing the interpolation processing; and a step of predicting the breakout based on the deviation degree.

[0009] (2) The breakout prediction method according to the present invention, in the invention of (1) above, in the step of performing the interpolation processing, for each detected temperature of each of the plurality of thermometers, interpolation processing is performed at the center point of each of a plurality of calculation cells equally divided according to the dimensions of the slab to calculate the temperature.

[0010] (3) The breakout prediction method according to the present invention, in the invention of (2) above, the number of the calculation cells is kept constant even when the dimensions of the slab are changed.

[0011] (4) The breakout prediction method according to the present invention, in the invention of (2) or (3) above, in the step of calculating as the deviation degree, the average value of the temperatures of each of the plurality of calculation cells located at the same position from the upper end of the mold in the casting direction of the molten steel with respect to the mold is obtained, the difference from the average value is obtained for the temperature of each of the plurality of calculation cells, and the deviation degree is calculated using the influence coefficient vector from the obtained difference.

[0012] (5) The breakout prediction method according to the present invention is, in the invention of (4) above, in the step of predicting the breakout, when the individual deviation degree of the calculated calculation cell exceeds a preset first threshold value, or when the total deviation degree of the calculated calculation cells exceeds a preset second threshold value, the breakout is predicted.

[0013] (6) The breakout prediction method according to the present invention is, in any one of the inventions of (1) to (5) above, characterized in that the influence coefficient vector is a sensitivity coefficient vector having the sensitivity coefficients of each of the plurality of temperature change amounts as components.

[0014] (7) The operation method of the continuous casting machine according to the present invention is characterized in that when the breakout is predicted based on the breakout prediction method of any one of the inventions of (1) to (6) above, the casting speed is decreased.

Effect of the Invention

[0015] The breakout prediction method and the operation method of the continuous casting machine according to the present invention have the effect that it is possible to detect a foreign matter biting breakout with a small change width and temperature fluctuation of the mold temperature with sufficient detection accuracy.

Brief Description of the Drawings

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Embodiment for Carrying Out the Invention

[0017] Hereinafter, embodiments of the breakout prediction method and the operation method of the continuous casting machine according to the present invention will be described. Note that the present invention is not limited by this embodiment.

[0018] FIG. 1 is a schematic diagram showing a schematic configuration of a continuous casting machine 1 according to the embodiment. As shown in FIG. 1, the continuous casting machine 1 according to the embodiment includes a tundish 3 into which molten steel 2 is poured, a copper mold 5 that cools the molten steel 2 poured from the tundish 3 through an immersion nozzle 4, a plurality of slab support rolls 7 that convey a semi-solidified slab 6 pulled out from the mold 5, and a determination unit 20 that determines a sign phenomenon of breakout from the detected temperature of a thermometer 8 embedded in the mold 5. In this embodiment, a thermocouple is used as the thermometer 8, but it is not limited thereto.

[0019] FIG. 2 is a perspective view showing a schematic configuration of the mold 5 in the continuous casting machine 1 according to the embodiment 1,1 ~8 m,n in which the thermometer 8 is embedded. As shown in FIG. 2, the mold 5 includes a pair of long-side cooling plates 5a and a pair of short-side cooling plates 5b, and is formed in a substantially rectangular tube shape that penetrates in the vertical direction. Inside the long-side cooling plate 5a and the short-side cooling plate 5b, cooling water channels (not shown) are formed along the inner wall surface, and the molten steel 2 is cooled by circulating cooling water through these cooling water channels.

[0020] Also, inside the long-side cooling plate 5a of the mold 5, the thermometer 81,1 ~8 m,n is embedded at a predetermined depth from the outer wall surface of the long-side cooling plate 5a. In the following description, when not particularly distinguishing between the thermometers 8 1,1 ~8 m,n they are simply referred to as the thermometer 8. In FIG. 2, the thermometer 8 1,1 ~8 m,n is configured in three or more stages in the casting direction A, and the first-stage thermometer 8 1,1 ~8 1,n , the second-stage thermometer 8 2,1 ~8 2,n , and the nth-stage thermometer 8 m,1 ~8 m,n are divided and embedded on the same plane respectively. In the present embodiment, the casting direction A is the direction in which the molten steel 2 is poured from the tundish 3 through the immersion nozzle 4 into the mold 5, and is the same direction as the direction in which the slab 6 is drawn out from the lower end of the mold 5.

[0021] Note that the arrangement of the thermometer 8 shown in FIG. 2 is merely an example for the description of the present invention. Among the pair of long-side cooling plates 5a and the pair of short-side cooling plates 5b of the mold 5, at least one of the pair of long-side cooling plates 5a, at least one of the pair of short-side cooling plates 5b, or all of the pair of long-side cooling plates 5a and the pair of short-side cooling plates 5b may be provided with the thermometer 8. Among them, it is preferable to arrange thermometers on all of the pair of long-side cooling plates 5a and the pair of short-side cooling plates 5b. Further, the thermometer 8 can also be arranged in the mold 5 in a multi-stage arrangement of more than three stages or a single-stage arrangement in the casting direction A.

[0022] Next, the precursor phenomena of breakout will be described. The breakout targeted in the present proposal is roughly classified into two types. They are called restrictive breakout and foreign object entrapment breakout. First, regarding the restrictive breakout, FIG. 3(a) is a diagram for explaining the situation of the molten steel 2 and the solidification shell 10 in the mold 5 in the precursor phenomenon of the restrictive breakout. FIG. 3(b) is a diagram showing the situation of the fracture part 11 of the solidification shell 10 in the precursor phenomenon of the restrictive breakout.

[0023] As shown in FIGS. 3(a) and 3(b), in the omen phenomenon of restrictive breakout, seizure occurs in the mold 5 due to some factor, and the solidification shell 10 is constrained by the mold 5. On the other hand, since the slab 6 is pulled out from the lower end of the mold 5 in the same direction as the casting direction A shown in FIG. 3(b), a fracture portion 11 of the solidification shell 10 occurs immediately below the seizure. At this fracture portion 11 of the solidification shell 10, the mold 5 comes into contact with the molten steel 2, and further seizure occurs. While repeating the above phenomena, the fracture portion 11 of the solidification shell 10 moves downward, and the solidification shell 10 above the fracture portion 11 becomes thicker. And finally, when the fracture portion 11 passes through the lower end of the mold 5, the molten steel 2 leaks out from the fracture portion 11, and a restrictive breakout occurs.

[0024] Note that at the fracture portion 11, since the molten steel 2 is in contact with the mold 5, the temperature of the mold 5 locally rises. Therefore, for example, as shown by the arrow B in FIG. 3(b), when the fracture portion 11 moving downward passes through the arrangement position of the thermometer 8 m’,1 ~8 m’,n the detected temperature of the thermometer 8 m’,1 ~8 m’,n becomes high. After that, since the solidification shell 10 above the fracture portion 11 is constrained by the mold 5 and continues to be cooled, the detected temperature of the thermometer 8 m’,1 ~8 m’,n monotonically decreases. On the other hand, since the fracture portion 11 propagates not only downward but also laterally, as shown in FIG. 3(b), the fracture portion 11 expands in a V shape. Note that when the fracture portion 11 of the solidification shell 10 occurs below the thermometer 8 m’,1 ~8 m’,n the fracture portion 11 does not pass through at the position of the thermometer 8 m’,1 ~8 m’,n so that only a decrease in the detected temperature of the thermometer 8 m’,1 ~8 m’,n is observed.

[0025] Figure 4(a) shows the temperature distribution of the mold 5 at the moment when seizure occurred. Figure 4(b) shows the temperature distribution of the mold 5 10 seconds after the moment when seizure occurred. From the temperature distributions of the mold 5 shown in Figures 4(a) and 4(b), it can be seen that the V-shaped high temperature part is propagating downward and laterally.

[0026] Next, the foreign body entrapment breakout will be described. FIG. 5 is a diagram showing the foreign body entrapment breakout. In FIG. 5, reference numeral 18 denotes the meniscus (molten steel surface) of the molten steel 2. As shown in FIG. 5(a), mold powder 23 is used as a lubricant to prevent the above-mentioned seizure between the mold 5 and the slab 6 during casting. After being supplied to the molten steel surface from the top of the mold 5, it is subjected to heat from the molten steel 2, becomes molten, and is poured into the gap between the mold 5 and the slab 6, and serves as a lubricant to prevent seizure. However, as shown in FIG. 5(b), the mold powder 23 supplied to the molten steel 2 surface may be drawn into the mold 5 as an unmelted, lumpy foreign body 22 (called a powder bear state). At this time, a thermal resistance that was not originally expected is generated in the gap between the mold 5 and the slab 6. As a result, the solidified shell 10 cannot grow sufficiently at the portion where the foreign body 22 is entrapped, and reaches the lower end of the mold 5 as shown in FIG. 5(c) and FIG. 5(d). At this time, as shown in FIG. 5(e), it is considered that after the foreign object 22 passes the lower end of the mold 5 in a state where the shell thickness is thin locally, the solidified shell 10 cannot withstand the static pressure of the molten steel, so the solidified shell 10 breaks, and the molten steel 2 flows out to the outside. At this time, the temperature distribution in the mold is significantly different from that when seizure occurs. First, since the size of the foreign object 22 to be caught is considered to be about 100 [mm] or less, the mold temperature changes only by the size of the foreign object 22 when the foreign object 22 passes through the inside of the mold 5. In other words, the temperature does not change after the foreign object 22 passes through or around the area where the foreign object 22 passed through from before the occurrence. In addition, the temperature change is relatively small compared to the breakage caused by seizure, so it is generally difficult to detect, and this is a breakout type that is often erroneously detected.

[0027] Therefore, in the breakout prediction method according to the embodiment, in order to accurately detect the breakouts in the above two forms, the temperature change amount of the difference from a certain period before (before the first period) of the thermometer installed inside the mold 5 is used, and the breakout prediction accuracy is improved by normalizing with the standard deviation within a certain period (within the second period) of the temperature change amount. Hereinafter, the breakout prediction method according to the embodiment based on the above technical idea will be described in detail.

[0028] The breakout prediction method according to the embodiment includes a step of inputting the dimensions of the slab 6 withdrawn from the mold 5 in the continuous casting machine 1. Further, the breakout prediction method according to the embodiment includes a step of detecting the temperature of the mold 5 by a plurality of thermometers 8 embedded in the mold 5. Further, the breakout prediction method according to the embodiment includes a step of performing interpolation processing on the detected temperatures detected by the plurality of thermometers 8 according to the dimensions of the slab 6. Further, the breakout prediction method according to the embodiment includes a step of calculating the temperature change amount by comparing with the temperature a certain period before. Further, the breakout prediction method according to the embodiment includes a step of obtaining the standard deviation of the temperature change amount for each thermometer 8 within a certain period. Further, the breakout prediction method according to the embodiment includes a step of normalizing the temperature change by dividing the temperature change amount by the standard deviation. Further, the breakout prediction method according to the embodiment includes a step of calculating, as the degree of deviation from normal operation when no breakout has occurred, the component in the direction orthogonal to the influence coefficient vector obtained from the principal component analysis based on the temperature change amount calculated by performing the interpolation processing. Further, the breakout prediction method according to the embodiment includes a step of predicting a breakout based on the degree of deviation.

[0029] FIG. 6 is a flowchart showing an example of the procedure of the breakout prediction method according to the embodiment. The breakout prediction method shown in this flowchart is executed by the determination unit 20 shown in FIG. 1. Note that the determination unit 20 has at least the functions of the interpolation process execution means, the normalization means of the temperature change amount, the deviation degree calculation means, and the breakout prediction means in the present invention. Details of each step in the flowchart shown in FIG. 6 will be described later as appropriate.

[0030] In the breakout prediction method according to the embodiment, the determination unit 20 calculates a sensitivity coefficient for the standardized temperature change amount of the thermometers 8 1,1 ~8 m,n during normal operation when no breakout has occurred yet (hereinafter also referred to as normal time) (step S1). Here, this sensitivity coefficient is calculated using the standardized temperature change amount (standardized temperature change amount) obtained by performing interpolation processing so as to cope with different-width casting, thermometer failures, etc., and then standardizing the casting conditions with different conditions such as casting speed, steel type, mold powder 23, etc. in order to commonly process them. Note that since this sensitivity coefficient may change due to changes in the surface state of the mold 5 during operation, it is preferably updated at an appropriate time such as during the casting period. Next, the determination unit 20 continuously detects the temperature T 1,1 ~8 m,n of the mold 5 using the thermometers 8 1,1 ~T m,n (step S2). Next, the determination unit 20 performs interpolation processing on the temperature of the mold 5 at the center points of the calculation cells 12 1,1 ~8 m,n according to the dimensions of the slab 6 withdrawn from the mold 5 (for example, the width of the slab 6 and the thickness of the slab 6) input by the operator using an input device (not shown), which is an input means such as a personal computer provided in the continuous casting machine 1, for the detected temperatures of the thermometers 8 1,1 ~12 k,p (step S3). Next, the determination unit 20 determines the temperature T' 1,1 ~T’ k,pPerform average bias removal on it. That is, the determination unit 20 determines the temperature T’ of the mold 5 obtained by the interpolation process 1,1 ~T’ k,p at T’ 1,1 ~ΔT’ k,p and the calculation cells 12 1,1 ~12 1,p where the distances from the upper end of the mold 5 are the same positions, and the temperature change amounts ΔT’ 1,1 ~ΔT’ 1,p and the calculation cells 12 2,1 ~12 2,p where the distances from the upper end of the mold 5 are the same positions, and the temperature change amounts ΔT’ 2,1 ~ΔT’ 2,p T’ k,1 ~T’ k,p calculate the average value respectively, and the standard deviation of the temperature change amount ΔT’ 1,1 ~ΔT’ k,p for each calculation cell over a certain period. Then, the determination unit 20 calculates the difference from the average value of the temperature change amount ΔT’ 1,1 ~12 1,p of the calculation cells 12 1,1 ~ΔT’ 1,p and divides the difference from the average value of the temperature change amount ΔT’ 、 ~ΔT’ 2,1 ~12 2,p of the calculation cells 12 2,1 ~ΔT’ 2,p by the standard deviation to obtain a standardized temperature change amount (steps S4 to S6). Next, the determination unit 20 calculates the deviation degree using the sensitivity coefficient with respect to the obtained standardized temperature change amount (step S7).

[0031] Also, as one method for obtaining the sensitivity coefficient vector which is the influence coefficient vector, a method using principal component analysis can be considered. Here, the sensitivity coefficient vector which is a vector with the sensitivity coefficient which is the influence coefficient as a component is the thermometer 8 during normal operation 1,1 ~8 m,nIt represents the direction indicating the average behavior of the temperature change amount of the calculation cells obtained by the above interpolation process. And in the vector with the difference from the average value as a component, the component parallel to the direction of the sensitivity coefficient vector is the component of the average behavior, and the component in the direction orthogonal to the direction of the sensitivity coefficient vector is the component of the degree of deviation from the average behavior. Also, the individual deviation degrees of the calculation cells 12 refer to the components of the difference from the average value for each individual calculation cell.

[0032] Next, when the individual deviation degree (individual deviation degree) of the calculated calculation cell 12 exceeds the threshold value Y, or when the total deviation degree (total deviation degree) of the calculated calculation cell 12 exceeds the threshold value X, the determination unit 20 makes a breakout prediction determination (step S8). When it is determined that no breakout is predicted (No in step S8), the determination unit 20 proceeds to step S2.

[0033] On the other hand, when it is determined that a breakout is predicted (Yes in step S8), the determination unit 20 automatically decreases the casting speed to a predetermined speed (step S9). In this way, when the determination unit 20 predicts a breakout, by sufficiently reducing the casting speed, a solidification shell 10 with a sufficient thickness can be formed in the mold 5 even at the locations where sticking or foreign object entrapment has occurred, so that the breakout can be avoided. After that, after the determination unit 20 decreases the casting speed to a predetermined value, it returns the processing routine.

[0034] Next, the differences between the case of using temperature and the case of using the temperature change amount for the sensitivity coefficient used in the breakout prediction method according to the embodiment will be described. In a constrictive breakout, the temperature distribution obtained at the time of occurrence occurs in a very large range (about 100 [mm] to 500 [mm]), and the temperature change amount itself is also very large (20 [°C] to 50 [°C]). Therefore, when obtaining the deviation degree from the sensitivity coefficient, the deviation degree is large, and detection can be easily performed. However, in the case of foreign object biting breakout, the mold temperature changes only within the width of the size of the foreign object 22 (100 [mm] or less), and the temperature change amount itself is also very small (20 [°C] or less). Since the temperature distribution takes various distributions even during normal operation, allowing false detection would be necessary to regard the small temperature change occurring during foreign object biting as an abnormality, resulting in low superiority of this method. On the other hand, by obtaining the sensitivity coefficient using the temperature change amount and obtaining the deviation degree, it is possible to increase the deviation degree for a sudden temperature change that does not occur during normal times. The breakout to be detected this time does not depend on constrictive breakout or foreign object biting breakout, and an abnormal state suddenly occurs without warning. Therefore, rather than expressing an abnormal temperature distribution as a deviation degree using temperature as the sensitivity coefficient, it is desirable to express an abnormal temperature change amount as a deviation degree using the temperature change amount as the sensitivity coefficient.

[0035] Next, the temperature change amount used in the calculation is used after being standardized. This standardization method will be described. When the operating conditions change, for example, when the casting speed is increasing or decreasing, or when the molten steel temperature is rising or falling, there is a tendency for an overall change. The overall temperature change accompanying the change in operating conditions becomes an external disturbance when predicting a breakout, which may deteriorate the detection accuracy. Therefore, it is necessary to exclude and use the average value bias.

[0036] As a method for removing the average bias, for example, the average value T of all the detected temperatures T 1,1 ~8 m,n detected by the thermometer 8 1,1 ~T m,n is obtained, and the detected temperature T ave ​1,1 ~T m,n and the average value T for each of them ave and taking the difference therebetween. As other methods for removing the average bias, for example, a thermometer 8 located at the same distance from the upper end of the mold 5 in the casting direction A i,1 ~8 i,n detects the detected temperature T i,1 ~T i,n and obtains the average value T i,ave and takes the difference between each of the detected temperatures T i,1 ~T i,n and the average value T i,ave for each thermometer 8 located at the same distance as described above.

[0037] In addition, due to the oscillation of the mold being cast, the casting speed, and operating conditions such as the steel type and the mold powder 23 being used, the stability of the mold temperature may change. In addition, electromagnetic noise or the like may affect the thermometer being used, resulting in the temperature being regarded as fluctuating. When predicting breakout from the degree of deviation using temperature fluctuations, or when the mold temperature becomes unstable depending on the conditions, false detection is very likely to occur, and thus it is necessary to take measures against this. As standardization, the standard deviation of the amount of temperature change within a certain period for each thermometer 8 is calculated and a process of dividing by the amount of temperature change is performed. Thereby, it is possible to obtain a standardized temperature change amount that enables a certain threshold determination by ignoring the difference in the amount of temperature change caused by the difference in the conditions being processed.

[0038] As another method for obtaining the sensitivity coefficient vector which is the influence coefficient vector, for example, when the overall temperature changes due to fluctuations in the molten steel surface or the like, a method of experimentally obtaining the ease of heat transfer of the molten steel 2 at each individual thermometer 8 1,1 ~8 m,n can be considered.

[0039] On the other hand, at the time of the occurrence of a sign such as sticking leading to breakout, the thermometers 8 i,j1 and 8 i,j2The detected temperature is distributed at positions away from the dashed line indicating the direction of the sensitivity coefficient vector (a line at a 45-degree angle to the right in the example shown in Fig. 7). This is because when seizure leading to breakout occurs, the thermometer 8 near the position of the fracture portion 11 of the solidification shell 10 i,j1 detects the temperature T i,j1 which decreases, and somewhat later, the thermometers 8 i,j1 located on both sides adjacent to it i,j1+1 and the thermometer 8 i,j1―1 detect the temperature T i,j1+1 and the detected temperature T i,j1―1 also decrease.

[0040] From the above considerations, it can be seen that it is possible to determine the occurrence of breakout based on the degree to which the temperature change amounts ΔT 1,1 ~8 m,n of the thermometers 8 1,1 ~ΔT m,n deviate from the dashed line indicating the direction of the sensitivity coefficient vector. In other words, the component in the direction orthogonal to the sensitivity coefficient vector in the temperature change amount vector, which is a vector with ΔT 1,1 ~8 m,n calculated from the detected temperatures of the thermometers 8 1,1 ~ΔT m,n as components, is calculated as the deviation degree. And it can be seen that it is possible to determine the occurrence of breakout based on the calculated deviation degree.

[0041] For example, in Figs. 7 and 8, the deviation degree component, which is the component in the direction orthogonal to the sensitivity coefficient vector in the temperature change amount vector with the temperature change amounts of the detected temperatures of the thermometer 8 i,j1 and the thermometer 8 i,j2 as components, is calculated. And the occurrence of breakout is determined based on the calculated deviation degree component. In Figs. 7 and 8, the direction of the sensitivity coefficient vector is the same as the direction of the first principal component of the normal temperature change amount distribution, and the direction orthogonal to the direction of the sensitivity coefficient vector is the same as the direction of the second principal component of the normal temperature change amount distribution.

[0042] However, the detected temperatures T 1,1 ~T m,nIf the measurement itself is used to predict a breakout, there is a risk of false detection. That is, during unsteady operation, such as when the pouring width when pouring the molten steel 2 into the mold 5, in other words, the width of the slab 6 withdrawn from the lower end of the mold 5, is changed during operation, there is a risk of falsely predicting that a breakout will occur, even though there are no signs that would lead to a breakout.

[0043] FIG. 9(a) shows the temperature gauge 8 in the case where the width (casting width) of the slab 6 removed from the bottom end of the mold 5 is wide. m1,n1 ~8 m1,n1+18 Detection temperature T m1,n1 ~T m1,n1+18 and the temperature T' after the interpolation process m1,n1 ~T' m1,n1+18 FIG. 9(b) is a graph showing the relationship between the temperature and the melting temperature of the thermometer 8 in a case where the width (casting width) of the slab 6 drawn from the bottom end of the mold 5 is narrow. m1,n1 ~8 m1,n1+18 Detection temperature T m1,n1 ~T m1,n1+18 and the temperature T' after the interpolation process m1,n1 ~T' m1,n1+18 9(a) and 9(b), the temperature gauge 8 m1,n1 ~8 m1,n1+18 are disposed at the same distance from the upper end of the mold 5 in the pouring direction A. Also, m1,n1 ~T' m1,n1+18 is a calculation cell 12 equally divided according to the width of the slab 6. m1,n1 ~12 m1,n1+18 At the center point of the thermometer 8 m1,n1 ~8 m1,n1+18 Detection temperature T m1,n1 ~T m1,n1+18 is an estimated temperature of the mold 5 calculated by performing an interpolation process on the temperature of the mold 5. The method of the interpolation process will be described later.

[0044] When the casting width is changed during casting and the state changes from Fig. 9(a) to Fig. 9(b), m1,n1 ~8 m1,n1+18 Detection temperature T m1,n1 ~T m1,n1+18 When focusing on the detection temperature T m1,n1+3 and the detected temperature T m1,n1+15Only the temperature change is large, and no significant temperature change is observed in other detected temperatures. Therefore, in the cases shown in FIGS. 9(a) and 9(b) respectively, if the detected temperature T m1,n1 ~T m1,n1+18 itself is used for breakout prediction, there is a risk of misdetection that a sign leading to a breakout has occurred, deviating from the sensitivity coefficient vector.

[0045] On the other hand, when the casting width is changed during casting and changes from the state of FIG. 9(a) to the state of FIG. 9(b), regarding the temperature T’ m1,n1 ~T’ m1,n1+18 at which the interpolation process is executed while keeping the number (number of cells) of the calculation cells 12 constant even when the dimensions of the slab 6 are changed, the temperature change of the temperature T’ m1,n1 ~T’ m1,n1+18 is small. Therefore, in the cases shown in FIGS. 9(a) and 9(b) respectively, by using the temperature T‘ m1,n1 ~T’ m1,n1+18 at which the interpolation process is executed for breakout prediction, the risk of misdetection of the occurrence of a sign leading to a breakout can be reduced.

[0046] Also, in FIGS. 9(a) and 9(b), the temperature detections of the thermometers 8 m1,n1+7 for detecting the detected temperature T m1,n1+11 respectively, the thermometer 8 m1,n1+12 for detecting the detected temperature T m1,n1+16 respectively, the thermometer 8 m1,n1+7 for detecting the detected temperature T m1,n1+11 respectively, and the thermometer 8 m1,n1+12 for detecting the detected temperature T m1,n1+16 are defective. And even when including such a thermometer 8 with defective temperature detection, if the detected temperature T m1,n1 ~T m1,n1+18 itself is used for breakout prediction, there is a risk of misdetection that a sign of breakout occurrence has occurred, deviating from the sensitivity coefficient vector. On the other hand, for the temperature T‘ m1,n1 ~T’ m1,n1+18Even when the thermometer 8 has a defective temperature detection, by using the estimated temperature of the mold 5 in the section where the temperature detection is defective, the risk of false detection of the occurrence of a sign leading to breakout can be reduced.

[0047] Next, the method of interpolation processing will be described. FIG. 10 shows the thermometers 8 i,1 ~8 i,j and the calculation cells 12 i,1 ~12 i,j at positions having the same distance from the upper end of the mold 5.

[0048] As shown in FIG. 10, the calculation cells 12 i,1 ~12 i,j are obtained by equally dividing, into a certain number of cells, the section of the long-side cooling plate 5a of the mold 5 corresponding to the width of the slab 6 (the section sandwiched between a pair of short-side cooling plates 5b in the width direction of the mold 5) at positions having the same distance from the upper end of the mold 5 for the thermometers 8 i,1 ~8 i,j . Then, the detected temperatures detected by the thermometers 8 i,1 ~8 i,j are linearly interpolated, and the estimated temperature of the mold 5 (long-side cooling plate 5a) at the position of the center point of each of the calculation cells 12 i,1 ~12 i,j is calculated. Note that the number of cells of the calculation cells 12 for interpolation processing may be the same as or different from the number of thermometers 8 in the vertical and horizontal directions, but is made constant regardless of the variation in the casting width during casting.

[0049] The above interpolation process is applicable when obtaining the sensitivity coefficient vector using principal component analysis and when calculating the deviation degree. In this case, principal component analysis is performed using the interpolated temperature instead of the actual detected temperature. Even when the slab width is changed, since the same number of temperature vectors can be used, principal component analysis can be carried out including data with different widths. As a result, it is not necessary to obtain different influence coefficients for each width, and the influence coefficient vector can be determined including data with different slab widths. And for the deviation degree, it can also be calculated using the influence coefficient vector calculated based on the temperature obtained by interpolating the detected temperature. Therefore, breakout prediction for different slab widths is possible based on a unified standard. Furthermore, even when the slab width changes during casting, the risk of false detection regarding the occurrence of signs leading to breakout can also be reduced.

[0050] Note that the temperature change amount is derived from the difference between the temperature detection value a certain time ago and the current value. For simplicity, it can also be substituted with the difference in the detected temperatures of two thermometers with different casting directions at the same position in the width direction (for example, the difference between 8 1,2 and 8 2,2 in the mold thermometer layout diagram of Fig. 2).

[0051] Next, the determination of breakout prediction will be described. Fig. 11(a) is a diagram showing the time-series change of the total deviation degree in a case where sticking (constrained breakout) was confirmed. Fig. 11(b) is a diagram showing the change in the individual deviation degree at the position of each calculation cell 12 in a case where sticking (constrained breakout) was confirmed.

[0052] Also, Fig. 12(a) is a diagram showing the time-series change of the total deviation degree in a case where foreign matter was bitten in (foreign matter biting-in type breakout) was confirmed. Fig. 12(b) is a diagram showing the change in the individual deviation degree at the position of each calculation cell 12 in a case where foreign matter was bitten in (foreign matter biting-in type breakout) was confirmed.

[0053] Figure 13(a) shows a case where, although the total deviation degree has a peak, there is no abnormality in the results of the slab inspection. Figure 13(b) shows the individual deviation degrees in each calculation cell 12 in a case where there is no abnormality in the results of the slab inspection. Note that Figures 13(a) and 13(b) are considered to be cases where noise is added to the measured value of the thermometer 8 due to electromagnetic noise generated near the thermometer.

[0054] Note that the two graphs shown in Figures 11(a), 12(a), and 13(a) are obtained by calculating the total deviation degree separately for the upper and lower parts of the mold 5. Also, in Figures 11, 12, and 13, the abnormal threshold value is such that the total deviation degree is 150 and the individual deviation degree is 20, which is shown by a dashed line in each figure.

[0055] In Figure 11(a), the deviation degree rapidly increases at a certain time during operation. As described above, for the burn-in, temperature fluctuations occur in a relatively large range and the temperature fluctuation width is large. Therefore, it can be seen that the total deviation degree has a value clearly different from the normal state. Also, as shown in Figure 11(b), it can be seen that in some of the calculation cells 12, the deviation degree shows a large value.

[0056] Also, as shown in Figure 12(a), it can be seen that the total deviation degree when foreign object jamming is confirmed shows a deviation degree with a smaller peak compared to Figure 11(a). In Figure 12(b) as well, it can be seen that only two of the calculation cells 12 have a peak in the deviation degree.

[0057] Also, as shown in Figure 13(a), the total deviation degree has a peak. On the other hand, as shown in Figure 13(b), it can be seen that the individual deviation degrees in each calculation cell 12 are all low. Since almost all the thermometers 8 are changing temperature simultaneously, it is presumed that this condition is due to electromagnetic noise generated near the thermometer 8 in the vicinity, and not an actual temperature change.

[0058] Here, in order to detect signs leading to breakout, such as burn-in and foreign object biting, and prevent the detection of normal cases, breakout is predicted under the following two conditions. The first condition is that when the total deviation degree exceeds a preset threshold X (the second threshold), it is considered abnormal. Even in this case, at least one point must be equal to or greater than the preset threshold Y. The second condition is whether there is a deviation degree with a value equal to or greater than a preset threshold Z (the first threshold).

[0059] FIG. 14 is a diagram showing the results of predicting breakout using the breakout prediction method according to the embodiment and the conventional method. As shown in FIG. 14, in the conventional methods (conventional examples) based on Patent Documents 1 and 2, breakout was predicted from the behavior of the temperature embedded inside the mold, but breakout could only be detected with a detection rate of about 50%, and there were many false detections. On the other hand, as shown in FIG. 14, in the breakout prediction method according to the embodiment (invention example), the false detection rate could be significantly reduced. Also, although not detected by the conventional method (conventional example), when judged by the deviation degree using the breakout prediction method according to the embodiment (invention example), abnormalities could sometimes be detected. Further, in the breakout prediction method according to the embodiment (invention example), although breakout did not occur in the actual slab, abnormalities were often discovered, and it became possible to predict breakouts that could not be detected by the conventional method (comparative example).

Industrial Applicability

[0060] The present invention can provide a breakout prediction method and an operation method for a continuous casting machine that can detect foreign object biting breakouts with a small change range and temperature fluctuation of the mold temperature with sufficient detection accuracy.

Explanation of Signs

[0061] 1 Continuous casting machine 2 Molten steel 3 Tundish 4 Immersion Nozzle 5 Mold 6 Cast Slab 7 Cast Slab Support Roll 8 Thermometer 10 Solidification Shell 11 Fracture Part 12 Calculation Cell 18 Meniscus 20 Judgment Unit 22 Foreign Substance 23 Mold Powder

Claims

1. A step of inputting the dimensions of a slab withdrawn from a mold in a continuous casting machine; A step of detecting the temperature of the mold by a plurality of thermometers embedded in the mold; A step of performing interpolation processing on the detected temperatures detected by the plurality of thermometers according to the dimensions of the slab; A step of calculating a temperature change amount by comparing with the temperature calculated by performing the interpolation processing before the first period; A step of obtaining a standard deviation within a second period for the temperature change amount; A step of calculating a standardized temperature change amount by dividing the temperature change amount by the standard deviation; Based on the calculated standardized temperature change amount, a component in a direction orthogonal to the influence coefficient vector obtained from principal component analysis is calculated as a deviation degree from normal operation when breakout has not occurred; A step of predicting the breakout based on the deviation degree; A breakout prediction method characterized by comprising the above.

2. In the step of performing the interpolation processing, For each detected temperature of each of the plurality of thermometers, interpolation processing is performed at the center point of each of a plurality of calculation cells equally divided according to the dimensions of the slab to calculate the temperature. The breakout prediction method according to claim 1.

3. The number of the calculation cells is kept constant even when the dimensions of the slab are changed. The breakout prediction method according to claim 2.

4. In the step of calculating as the deviation degree, The average value of the temperatures of each of the plurality of calculation cells at the same position from the upper end of the mold in the pouring direction of the molten steel into the mold is obtained, the difference from the average value is obtained for the temperature of each of the plurality of calculation cells, and the deviation degree is calculated from the obtained difference using the influence coefficient vector. The breakout prediction method according to claim 2.

5. In the step of calculating as the deviation degree, The average value of the temperatures of each of the plurality of calculation cells at the same position from the upper end of the mold in the pouring direction of the molten steel into the mold is obtained, the difference from the average value is obtained for the temperature of each of the plurality of calculation cells, and the deviation degree is calculated from the obtained difference using the influence coefficient vector. The breakout prediction method according to claim 3.

6. In the step of predicting the breakout, The breakout prediction method according to claim 4, wherein the breakout is predicted when the individual deviation degree of the calculated calculation cell exceeds a preset first threshold value, or when the total deviation degree of the calculated calculation cells exceeds a preset second threshold value.

7. In the step of predicting the breakout, The breakout prediction method according to claim 5, wherein the breakout is predicted when the individual deviation degree of the calculated calculation cell exceeds a preset first threshold value, or when the total deviation degree of the calculated calculation cells exceeds a preset second threshold value.

8. The breakout prediction method according to any one of claims 1 to 7, wherein the influence coefficient vector is a sensitivity coefficient vector having sensitivity coefficients of respective ones of a plurality of the temperature change amounts as components.

9. An operation method of a continuous casting machine, characterized in that when the breakout is predicted based on the breakout prediction method according to any one of claims 1 to 7, the casting speed is decreased.

10. An operation method of a continuous casting machine, characterized in that when the breakout is predicted based on the breakout prediction method according to claim 8, the casting speed is decreased.

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