CN118023484A - Immersed nozzle bias flow detection method based on crystallizer copper plate temperature thermal image - Google Patents
Immersed nozzle bias flow detection method based on crystallizer copper plate temperature thermal image Download PDFInfo
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- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 title claims abstract description 114
- 229910052802 copper Inorganic materials 0.000 title claims abstract description 114
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- 229910000831 Steel Inorganic materials 0.000 claims description 12
- 239000010959 steel Substances 0.000 claims description 12
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- 238000009851 ferrous metallurgy Methods 0.000 abstract description 2
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- 239000002893 slag Substances 0.000 description 5
- 208000029154 Narrow face Diseases 0.000 description 3
- 230000010259 detection of temperature stimulus Effects 0.000 description 3
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Abstract
The invention provides a method for detecting bias current of a submerged nozzle based on a crystallizer copper plate temperature thermal image, and belongs to the technical field of ferrous metallurgy continuous casting detection. Firstly, acquiring the temperature of a thermocouple by a crystallizer temperature monitoring system, calculating the temperature of a non-measuring point, and drawing a temperature thermal image; secondly, splitting the temperature arrays of the two wide-surface copper plates, and drawing a temperature difference thermal image; thirdly, performing threshold segmentation on the outer arc wide surface temperature difference value array and the inner arc wide surface temperature difference value array to obtain a binary image of the temperature difference thermal image; finally, calculating the area percentage of the red area of the binary image, and detecting and judging whether the submerged nozzle generates bias flow and which side the bias flow faces. The method plays a good role in predicting the drift current of the water gap in production, and can avoid the influence caused by technological operations such as the change of the pull speed, the width adjustment of a casting blank and the like; whether the bias current occurs in the width direction of the immersed nozzle of the crystallizer can be accurately and efficiently judged in real time.
Description
Technical Field
The invention belongs to the technical field of ferrous metallurgy continuous casting detection, and relates to a submerged nozzle bias current detection method based on a crystallizer copper plate temperature thermogram.
Background
In the continuous casting production process, the submerged nozzle mainly plays a role in connecting the tundish and the crystallizer. The abnormal bias flow of the submerged nozzle means that the flow formed after molten steel enters the crystallizer is asymmetric, so that the interface of the steel slag of the crystallizer presents unstable fluctuation, the heat transfer and melting of a slag layer and the consumption of the liquid slag are not facilitated, and slag rolling and air suction can be caused when serious. In addition, the flow field distribution that the molten steel drift can cause in the crystallizer is uneven, and unilateral flow is too strong, and drift one side flow impact near the primary green body shell, inclusion and bubble also are difficult to come up to the slag layer, lead to casting blank surface quality decline and bonding steel leakage. However, the working condition of the crystallizer is very bad, and the flow of molten steel occurs in the crystallizer, so that the quality defects and steel leakage of various casting blanks are easily caused, and therefore, the development of a detection method for the drift of the submerged nozzle has important significance for continuous casting production.
The crystallizer online monitoring system detects the temperature of the copper plate of the crystallizer through the thermocouple, can generate a thermal image in real time, and intuitively reflects the heat transfer condition of four surfaces of the crystallizer. The bias flow of the immersed nozzle in the crystallizer can cause uneven distribution of a flow field and a temperature field, the flow of a single side is too strong, one side of the bias flow can impact a primary blank shell close to one side, the temperature of the blank shell is increased, the temperature of the surface of a casting blank is increased along with the temperature increase of the blank shell, and the temperature gradient from the blank shell to a copper plate is increased; therefore, the heat flow introduced into the copper plate is increased, and finally the measured temperature of the thermocouple of the bias current side copper plate and the temperature of the local area of the thermal image graph are increased.
The invention patent CN115592083A provides a drift current detection method of a submerged nozzle of a slab crystallizer based on real-time acquisition of water temperature data and water flow data of the crystallizer. According to the collected water temperature and water flow data, a preset ratio judgment formula is adopted, and the calculated ratio of the inner arc side heat change value to the outer arc side heat change value of the wide surface of the copper plate of the crystallizer is used as a wide surface deviation judgment value; and taking the ratio of the calculated left heat change value to the right heat change value of the narrow surface of the copper plate of the crystallizer as a narrow surface deviation judgment value. And generating a bias flow detection result of the immersed nozzle of the crystallizer according to the preset allowable standard value, the wide surface deviation judgment value and the narrow surface deviation judgment value. The method has a certain effect on predicting the drift current of the water gap in production, has less data and parameters for online detection, is simple in calculation formula and is easy to realize. However, there are many factors affecting the water inlet and outlet temperatures and the water quantity of the wide and narrow surfaces of the copper plate of the crystallizer, and the process operations such as drawing speed change, casting blank width adjustment and the like can cause great fluctuation of the water outlet temperature of the copper plate. That is, the variation of the water temperature difference and the water quantity cannot be explained that the drift of the submerged entry nozzle is necessarily generated, so that the drift of the submerged entry nozzle is judged by a preset ratio judgment formula, and the accuracy and the reliability of the method are questionable.
Disclosure of Invention
The invention aims to provide a submerged nozzle bias flow detection method based on a crystallizer copper plate temperature thermal image, which can detect the submerged nozzle bias flow in real time and on line and provides a reliable and effective submerged nozzle bias flow detection method for a continuous casting process, thereby improving the quality of casting blanks.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
A method for detecting bias current of a submerged nozzle based on a crystallizer copper plate temperature thermal image comprises the steps of firstly, obtaining thermocouple temperature by a crystallizer temperature monitoring system, calculating non-measuring point temperature by a polynomial interpolation method, and drawing an outer arc wide copper plate and an inner arc wide copper plate temperature thermal image by one-to-one correspondence of temperature data and RGB values of colors. Secondly, splitting the temperature arrays of the two wide copper plates, subtracting the temperature arrays of the low temperature side from the temperature arrays of the high temperature side to obtain two temperature difference arrays, and drawing a temperature difference thermal image. Thirdly, threshold segmentation is carried out on the outer arc wide surface temperature difference value array and the inner arc wide surface temperature difference value array, so that a binary image of the temperature difference thermal image is obtained. Finally, calculating the area percentage of the red area of the binary image, and detecting and judging whether the submerged nozzle generates a bias flow and which side the bias flow faces to, so as to solve the technical problem that the bias flow of the submerged nozzle of the crystallizer cannot be accurately judged in the prior art. The method specifically comprises the following steps:
First step, real-time detection of temperature of crystallizer and thermal image drawing
The crystallizer of the slab continuous casting machine can be regarded as a cuboid with four copper plates without bottoms, and comprises two wide copper plates and two narrow copper plates, wherein the two wide copper plates and the two narrow copper plates are respectively an outer arc wide copper plate, a right narrow copper plate, an inner arc wide copper plate and a left narrow copper plate, the front part of the cuboid is the outer arc wide copper plate, the right rear part of the cuboid is the inner arc wide copper plate, the right side of the cuboid is the right narrow copper plate, and the left side of the cuboid is the left narrow copper plate. A total of 72 thermocouples were arranged on the four copper plates, and the temperature of each thermocouple was collected and recorded in a positionally arranged array of temperatures once per second. The point where the thermocouple is located is a measuring point, and the interpolation point is a non-measuring point. And calculating the temperature of the non-measuring point position according to the rows and the columns respectively by adopting a polynomial interpolation algorithm, so as to obtain the temperature two-dimensional distribution of the whole crystallizer copper plate. The outer arc wide surface temperature array T OuterWide(xi,j) and the inner arc wide surface temperature array T InnerWide(yi,j) can be obtained according to the two-dimensional distribution of the temperatures of the two wide surface copper plates. The two wide-surface copper plates are an outer arc wide-surface copper plate and an inner arc wide-surface copper plate. Where x i,j represents the temperature data of the ith row and jth column of the outer arc wide face temperature array, and y i,j represents the temperature data of the ith row and jth column of the inner arc wide face temperature array. The temperature array is as follows:
Outer arc broad temperature array T OuterWide(xi,j) is:
inner arc broad temperature array T InnerWide(yi,j) is:
where m represents a row index number and 2n represents a column index number.
And drawing a crystallizer copper plate thermal image graph corresponding to the numerical value of the outer arc wide surface temperature array, the numerical value of the inner arc wide surface temperature array and the RGB value of each color one by one, and further visually showing the two-dimensional distribution of the temperature of the crystallizer copper plate in the casting production process.
Second step, splitting the temperature array of the wide-surface thermal image
The wide-surface copper plate temperature array comprises an outer arc wide-surface temperature array and an inner arc wide-surface temperature array. In order to draw the temperature difference diagrams of the left side and the right side of the center line of the inner arc wide surface and the outer arc wide surface, the temperature array of the wide surface copper plate needs to be equally divided into two arrays from the center line of the wide surface copper plate. Since the broad-side temperature array needs to be referred to as a difference along the two sides of the center line, the arrays on the right side of the center line need to be arranged in a row-column reverse order. The array on the left of the center line of the outer arc broad temperature array is an outer arc broad left temperature array, the array on the right of the center line of the outer arc broad temperature array is an outer arc broad right temperature array, the array on the left of the center line of the inner arc broad temperature array is an inner arc broad left temperature array, and the array on the right of the center line of the inner arc broad temperature array is an inner arc broad right temperature array. The four temperature arrays are as follows:
The outer arc broad left side temperature array T OuterLeft(ai,j) is:
wherein a i,j＝xi,j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the temperature array on the left side of the outer arc broad surface;
the outer arc broad right side temperature array T OuterRight(bi,j) is:
Wherein b i,j＝xi,2n+1-j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the temperature array on the right side of the outer arc broad surface;
Inner arc broad left side temperature array T InnerLeft(ci,j) is:
Wherein, c i,j＝yi,j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the temperature array on the left side of the inner arc broad surface;
Inner arc broad right side temperature array T InnerRight(di,j) is:
Wherein d i,j＝yi,2n+1-j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the temperature array on the right side of the inner arc broad surface.
Third step, four temperature array average value calculation
The four temperature arrays comprise an outer arc wide left side temperature array, an outer arc wide right side temperature array, an inner arc wide left side temperature array and an inner arc wide right side temperature array. Calculating the average temperature of the four temperature arrays, wherein the average temperature calculation formula is as follows:
outer arc broad left side temperature array average temperature:
outer arc broad right side temperature array average temperature:
inner arc broad left side temperature array average temperature:
inner arc broad right side temperature array average temperature:
Wherein H OuterLeft、HOuterRight represents the average temperature of the temperature array on the left side of the outer arc broad surface and the average temperature of the temperature array on the right side of the outer arc broad surface respectively; h InnerLeft、HInnerRight represents the inner arc broad left side temperature array average temperature and the inner arc broad right side temperature array average temperature, respectively. For the same broad temperature array, the side with the higher average temperature is called a high temperature side temperature array, and the side with the lower average temperature is called a low temperature side temperature array. The broad face temperature array comprises an outer arc broad face temperature array and an inner arc broad face temperature array.
Fourth step, thermal image visualization of wide-surface temperature difference
For the same wide-face temperature array, subtracting the low-temperature side temperature array from the high-temperature side temperature array to obtain two temperature difference value arrays: outer arc temperature difference array D Outer(pi,j), inner arc temperature difference array D Inner(qi,j). The outer arc broad surface right side average temperature is higher than the left side average temperature, the inner arc broad surface left side average temperature is higher than the right side average temperature, then there are:
Wherein, p ij＝bi,j-ai,j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the outer arc temperature difference value array;
Wherein q ij＝ci,j-di,j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the inner arc temperature difference array.
And drawing a temperature difference thermal image graph of the crystallizer wide-surface copper plate according to the outer arc temperature difference array, the inner arc temperature difference array and the RGB value of each color in a one-to-one correspondence mode. The crystallizer wide-surface copper plate comprises an outer arc wide-surface copper plate and an inner arc wide-surface copper plate.
Fifth step, temperature difference thermal image threshold segmentation
And (3) generating a binary image by using a threshold segmentation algorithm to obtain a temperature difference thermal image of the crystallizer wide-surface copper plate obtained in the fourth step. Wherein a temperature difference greater than a threshold T deg.c is shown as red and a temperature difference less than the threshold T deg.c is shown as white. The threshold T DEG C range is 2 ℃ to 5 ℃.
The threshold segmentation algorithm is:
the binary array U (U i,j) corresponding to the outer arc wide-surface temperature difference array is as follows:
The binary array V (V i,j) corresponding to the inner arc wide-face temperature difference array is as follows:
Wherein u i,j is the binary representation mode of the thermal image graph of the temperature difference of the outer arc broad face, and the value is 0 or 1; v i,j is the binary representation mode of the inner arc wide surface temperature difference thermal image, and the value is 0 or 1; u i,j and v i,j are red in the binary image when they take 1, and u i,j and v i,j are white in the binary image when they take 0.
Sixth step, calculating the proportion of the temperature difference area and discriminating the drift of the water gap
And (6.1) calculating the area of the area with the temperature greater than T ℃ according to the binary image generated by the thermal image, and dividing the area by the effective area of the crystallizer copper plate contacted with molten steel to obtain the area percentage of the high-temperature area with the temperature greater than T ℃. The effective area is the contact area of molten steel and a copper plate in production.
(6.2) When the high temperature sides of the two wide surfaces are respectively close to different narrow surfaces, or the high temperature sides of the two wide surfaces are close to the same narrow surface, and the area percentage of the high temperature areas of the two wide surfaces is less than X%, judging that the water gap is not biased in the wide surface direction; the X% is between 40% and 60%. The high temperature sides of the two wide sides are the sides of the wide copper plate corresponding to the high temperature side temperature array in the third step.
And (6.3) when the high temperature sides of the two wide surfaces are close to the same narrow surface, and the area percentage of one high temperature area of the two wide surfaces is larger than X%, judging that the water gap generates bias flow in the width direction, and sending bias flow detection result information to the site.
The beneficial effects of the invention are as follows:
The method has good effect on predicting the drift current of the water gap in production, and can avoid the influence caused by technological operations such as the change of the pull speed, the width adjustment of a casting blank and the like. The method can accurately and efficiently judge whether the bias current occurs in the width direction of the immersed nozzle of the crystallizer in real time.
Drawings
FIG. 1 is a flow chart of a method for detecting drift of a submerged nozzle based on a temperature thermogram of a crystallizer copper plate.
Fig. 2 is a schematic diagram of the arrangement of four copper plates and thermocouples of the crystallizer.
FIG. 3 is a thermal image of the temperature of a crystallizer copper plate; FIG. 3 (a) is a thermal image of the outer arc wide copper plate; fig. 3 (b) is a thermal image of the inner arc wide copper plate.
FIG. 4 is a thermal image of a wide area temperature difference; FIG. 4 (a) is a thermal image of the temperature difference of the inner arc broad face; fig. 4 (b) is a thermal image of the temperature difference of the broad face of the outer arc.
FIG. 5 is a binary image of a wide area temperature differential thermogram. FIG. 5 (a) is a binary image of the inner arc wide temperature difference thermogram; FIG. 5 (b) is a binary image of the outer arc wide temperature difference thermogram.
Detailed Description
The invention will now be further illustrated by, but not limited to, specific examples in conjunction with the accompanying drawings.
FIG. 1 is a flow chart of a method for detecting drift of a submerged nozzle. As can be seen from fig. 1, a submerged nozzle bias current detection method based on a crystallizer copper plate temperature thermal image is divided into the following six parts: the method comprises the steps of real-time detection of temperature of a crystallizer copper plate, temperature visualization, temperature array splitting of a wide copper plate temperature thermal image, average value calculation of the wide copper plate temperature splitting array, visualization of a wide copper plate temperature difference thermal image, threshold segmentation of the temperature difference thermal image, proportion calculation of a temperature difference region and bias flow judgment of a water gap. The invention will now be described in further detail with reference to the accompanying drawings by means of specific examples.
First step, real-time detection of temperature of crystallizer and thermal image drawing
As shown in FIG. 2, the wide copper plate had a width of 1910mm, the narrow copper plate had a width of 244.5mm, and the copper plate had a thickness of 40mm. 3 rows of thermocouple measuring points are respectively arranged at the positions 210mm, 325mm and 445mm away from the upper opening of the crystallizer, 11 rows of thermocouples are respectively arranged on the outer arc wide copper plate and the inner arc wide copper plate, the distance between every two adjacent rows of thermocouples is 150mm, and three rows of thermocouples are respectively arranged on each wide copper plate; the right narrow-face copper plate and the left narrow-face copper plate are respectively provided with 1 row of thermocouples at the center line, and the two narrow-face copper plates are respectively provided with 3 thermocouples. The total number of the galvanic couples arranged on the four copper plates is 72. The temperature of each thermocouple was collected and recorded once per second in a positionally arranged array of temperatures. The temperature of 11 thermocouples was interpolated 300 points in the transverse direction and 40 points in the longitudinal direction over the broad face of the crystallizer. And carrying out longitudinal and transverse polynomial interpolation on measured temperature data detected by the thermocouple to obtain a temperature value of a non-measuring point position of the crystallizer copper plate, and storing the temperature data of the wide copper plate in a two-dimensional array. Wherein, T OuterWide(xij) and T InnerWide(yij) represent an outer arc broad temperature array and an inner arc broad temperature array, respectively.
Selecting historical temperature data of a steel mill at 2023, 8, 3 and 3 days, wherein the temperature array is as follows:
outer arc broad temperature array T OuterWide(xij) is:
Inner arc broad temperature array T InnerWide(yij) is:
And drawing a thermal image graph of the crystallizer copper plate according to the preset temperature and color correspondence of the wide copper plate temperature array. The display frequency of the thermal image graph of the crystallizer is 1 frame/second, and the real-time requirement of on-site temperature monitoring in the crystallizer is met. Fig. 3 is a thermogram of the temperature of the copper plate of the crystallizer at this moment.
Second step, splitting the temperature array of the wide-surface thermal image
The crystallizer can be regarded as a rectangular matrix body which is surrounded by an outer arc wide surface, a right narrow surface, an inner arc wide surface and a left narrow surface and has no top and no bottom, and in order to draw a temperature difference diagram on the left side and the right side of the center line of the inner arc wide surface and the outer arc wide surface, an outer arc temperature array T OuterWide(xij) and an inner arc temperature array T InnerWide(yij) are respectively split into a left temperature array and a right temperature array. Since the broad-side temperature array is referred to as a difference along the two sides of the center line, the arrays on the right side of the center line need to be arranged in a row-column reverse order.
The outer arc broad left side temperature array T OuterLeft(aij) is:
the outer arc broad right side temperature array T OuterRight(bij) is:
inner arc broad left side temperature array T InnerLeft(cij) is:
inner arc broad right side temperature array T InnerRight(dij) is:
Third step, four temperature array average value calculation
And calculating the average temperatures of the left side and the right side of the middle line of the wide surfaces of the inner arc and the outer arc of the crystallizer. The 4 average temperatures on both sides of the broad face were:
Average temperature of left side of outer arc broad face:
average temperature on right side of outer arc broad face:
average temperature of inner arc broad left side:
Average temperature on right side of inner arc broad face:
therefore, the average temperature of the right side of the inner arc broad surface is higher than the average temperature of the left side of the inner arc broad surface, and the average temperature of the left side of the outer arc broad surface is higher than the average temperature of the right side of the outer arc broad surface.
Fourth step, thermal image visualization of wide-surface temperature difference
For the outer arc broad face, the average temperature on the left side is higher than the average temperature on the right side, thus D outer(pi,j)＝TOuterLeft(ai,j)-TOuterRight(bi,j); for the inner arc broadside, the average temperature on the right side is higher than the average temperature on the left side, thus D inner(qi,j)＝TInnerRight(di,j)-TInnerLeft(ci,j). And drawing a temperature difference thermal image graph of the wide copper plate of the crystallizer according to a preset temperature and color corresponding relation. FIG. 4 is a thermal image of the temperature difference of the broad face at this time. Wherein (a) is a temperature difference thermal image of the inner arc broad face and (b) is a temperature difference thermal image of the outer arc broad face.
Fifth step, temperature difference thermal image threshold segmentation
And generating a binary image from the thermal image by using a threshold segmentation algorithm. The threshold T c is set to 3 c according to the actual condition of slab production, and when the temperature difference is greater than 3 c, the region with the temperature difference less than 3 c is displayed as red, and the region with the temperature difference less than 3 c is displayed as white. Fig. 5 (a) and (b) are binary diagrams of thermal image diagrams of the difference between the temperatures of the inner arc broad surface and the outer arc broad surface at this time.
Sixth step, calculating the proportion of the temperature difference area and discriminating the drift of the water gap
The percentage X was set to 50 according to the actual condition of slab production. The red area after the binarization of the temperature difference array obtained by calculating the left side and the right side of the center line of the outer arc broad surface accounts for 63.4% of the effective contact area of the crystallizer copper plate and molten steel; the red area ratio of the binarized temperature difference array obtained by subtracting the left side from the right side of the center line of the inner arc broad surface is 79.7%. The high temperature sides of the two wide surfaces are close to the same narrow surface, so that the water outlet can be judged to be deviated to the left narrow surface in the width direction.
The above examples merely illustrate embodiments of the present invention, but are not to be construed as limiting the scope of the present invention, and it should be noted that detection of the bias flow of the submerged entry nozzle of a slab, billet, or round billet is within the scope of the present invention. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit of the invention, and these are all within the scope of the invention.
Claims (5)
1. The method is characterized in that firstly, a crystallizer temperature monitoring system acquires thermocouple temperature, calculates non-measuring point temperature, and maps an outer arc wide copper plate and an inner arc wide copper plate temperature thermal image according to temperature data and RGB values of colors in a one-to-one correspondence manner; secondly, splitting the temperature arrays of the two wide-face copper plates, subtracting the temperature arrays of the low temperature side from the temperature arrays of the high temperature side to obtain two temperature difference value arrays, and drawing a temperature difference thermal image; thirdly, performing threshold segmentation on the outer arc wide surface temperature difference value array and the inner arc wide surface temperature difference value array to obtain a binary image of the temperature difference thermal image; finally, calculating the area percentage of the red area of the binary image, detecting and judging whether the submerged nozzle generates a bias flow or not, and which side the bias flow faces, and further accurately judging the bias flow of the submerged nozzle of the crystallizer.
2. The submerged nozzle bias current detection method based on the crystallizer copper plate temperature thermal image is characterized by comprising the following steps of:
first, detecting the temperature of the crystallizer in real time and drawing a thermal image
The crystallizer of the slab continuous casting machine can be regarded as a cuboid structure with four copper plates without bottoms at the upper and lower parts, and comprises two wide copper plates and two narrow copper plates, namely an outer arc wide copper plate, a right narrow copper plate, an inner arc wide copper plate and a left narrow copper plate, wherein the front part of the cuboid is the outer arc wide copper plate, the right rear part is the inner arc wide copper plate, the right side is the right narrow copper plate, and the left side is the left narrow copper plate; arranging a plurality of thermocouples on the four copper plates, collecting the temperature of each thermocouple, and recording the temperature in a temperature array arranged in a position mode every second; the point where the thermocouple is located is a measuring point, and the interpolation point is a non-measuring point; calculating the temperature of the non-measuring point position according to the rows and the columns by adopting a polynomial interpolation algorithm, so as to obtain the temperature two-dimensional distribution of the whole crystallizer copper plate; the outer arc wide surface temperature array T OuterWide(xi,j) and the inner arc wide surface temperature array T InnerWide(yi,j) can be obtained according to two-dimensional temperature distribution of the two wide surface copper plates, wherein x i,j represents the temperature data of the ith row and the jth column of the outer arc wide surface temperature array, and y i,j represents the temperature data of the ith row and the jth column of the inner arc wide surface temperature array; the two wide-surface copper plates are an outer arc wide-surface copper plate and an inner arc wide-surface copper plate; the temperature array is as follows:
Outer arc broad temperature array T OuterWide(xi,j) is:
inner arc broad temperature array T InnerWide(yi,j) is:
Wherein m represents a row index number and 2n represents a column index number;
Drawing a crystallizer copper plate thermal image graph corresponding to the numerical value of the outer arc wide surface temperature array, the numerical value of the inner arc wide surface temperature array and the RGB value of each color one by one, and further visually showing two-dimensional distribution of the temperature of the crystallizer copper plate in the casting production process;
Second, splitting the wide-face temperature thermal image temperature array
The wide-surface copper plate temperature array comprises an outer arc wide-surface temperature array and an inner arc wide-surface temperature array; in order to draw a temperature difference graph of the left side and the right side of the center line of the inner arc wide surface and the outer arc wide surface, equally dividing a wide-surface copper plate temperature array into two arrays from the center line of the wide-surface copper plate temperature array; because the array of the wide temperature is required to be called as a difference along the two sides of the central line, the array on the right side of the central line is arranged in a row and column reversed order; the array on the left of the center line of the outer arc broad temperature array is an outer arc broad left temperature array, the array on the right of the center line of the outer arc broad temperature array is an outer arc broad right temperature array, the array on the left of the center line of the inner arc broad temperature array is an inner arc broad left temperature array, and the array on the right of the center line of the inner arc broad temperature array is an inner arc broad right temperature array; the four temperature arrays are as follows:
The outer arc broad left side temperature array T OuterLeft(ai,j) is:
wherein a i,j＝xi,j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the temperature array on the left side of the outer arc broad surface;
the outer arc broad right side temperature array T OuterRight(bi,j) is:
Wherein b i,j＝xi,2n+1-j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the temperature array on the right side of the outer arc broad surface;
Inner arc broad left side temperature array T InnerLeft(ci,j) is:
Wherein, c i,j＝yi,j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the temperature array on the left side of the inner arc broad surface;
Inner arc broad right side temperature array T InnerRight(di,j) is:
Wherein d i,j＝yi,2n+1-j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the temperature array on the right side of the inner arc broad surface;
Third, calculating the average value of four temperature arrays
The four temperature arrays comprise an outer arc wide left temperature array, an outer arc wide right temperature array, an inner arc wide left temperature array and an inner arc wide right temperature array; calculating the average temperature of the four temperature arrays;
For the same wide-surface temperature array, the side with high average temperature is called a high-temperature side temperature array, and the side with low average temperature is called a low-temperature side temperature array; the broad temperature array comprises an outer arc broad temperature array and an inner arc broad temperature array;
fourth step, thermal image visualization of wide-surface temperature difference
For the same wide-face temperature array, subtracting the low-temperature side temperature array from the high-temperature side temperature array to obtain two temperature difference value arrays: an outer arc temperature difference array D Outer(pi,j), an inner arc temperature difference array D Inner(qi,j); let the average temperature on the right side of the outer arc broad face be higher than the average temperature on the left side, the average temperature on the left side of the inner arc broad face be higher than the average temperature on the right side, then there are:
Wherein, p ij＝bi,j-ai,j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the outer arc temperature difference value array;
Wherein, q ij＝ci,j-di,j (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) represents the temperature data of the ith row and the jth column of the inner arc temperature difference value array;
Drawing a temperature difference thermal image map of the crystallizer wide-surface copper plate according to the outer arc temperature difference array, the inner arc temperature difference array and the RGB value of each color in a one-to-one correspondence mode; the crystallizer wide-surface copper plate comprises an outer arc wide-surface copper plate and an inner arc wide-surface copper plate;
fifth step, temperature difference thermal image threshold segmentation
Generating a binary image by using a threshold segmentation algorithm to obtain a temperature difference thermal image of the crystallizer wide-surface copper plate obtained in the fourth step; wherein the temperature difference is greater than a threshold T ℃ and is displayed as red, and the temperature difference is less than the threshold T ℃ and is displayed as white;
The threshold segmentation algorithm is:
the binary array U (U i,j) corresponding to the outer arc wide-surface temperature difference array is as follows:
The binary array V (V i,j) corresponding to the inner arc wide-face temperature difference array is as follows:
Wherein u i,j is the binary representation mode of the thermal image graph of the temperature difference of the outer arc broad face, and the value is 0 or 1; v i,j is the binary representation mode of the inner arc wide surface temperature difference thermal image, and the value is 0 or 1; the binary images corresponding to u i,j and v i,j are red when they are 1, and the binary images corresponding to u i,j and v i,j are white when they are 0;
Sixth step, calculating the proportion of the temperature difference area and discriminating the drift of the water gap
(6.1) Calculating the area of the area with the temperature greater than T ℃ according to the binary image generated by the thermal image, and dividing the area by the effective area of the crystallizer copper plate contacted with molten steel to obtain the area percentage of the high temperature area with the temperature greater than T ℃; the effective area is the contact area of molten steel and a copper plate in production;
(6.2) when the high temperature sides of the two wide surfaces are respectively close to different narrow surfaces, or the high temperature sides of the two wide surfaces are close to the same narrow surface, and the area percentage of the high temperature areas of the two wide surfaces is less than X%, judging that the water gap is not biased in the wide surface direction; the high temperature sides of the two wide surfaces are one side of the wide-surface copper plate corresponding to the high temperature side temperature array in the third step;
and (6.3) when the high temperature sides of the two wide surfaces are close to the same narrow surface, and the area percentage of one high temperature area of the two wide surfaces is larger than X%, judging that the water gap generates bias flow in the width direction, and sending bias flow detection result information to the site.
3. The method for detecting the bias flow of the submerged nozzle based on the temperature thermogram of the copper plate of the crystallizer according to claim 2, wherein in the third step, the average temperature calculation formula of the four temperature arrays is as follows:
outer arc broad left side temperature array average temperature:
outer arc broad right side temperature array average temperature:
inner arc broad left side temperature array average temperature:
inner arc broad right side temperature array average temperature:
Wherein H OuterLeft、HOuterRight represents the average temperature of the temperature array on the left side of the outer arc broad surface and the average temperature of the temperature array on the right side of the outer arc broad surface respectively; h InnerLeft、HInnerRight represents the inner arc broad left side temperature array average temperature and the inner arc broad right side temperature array average temperature, respectively.
4. The method for detecting the bias flow of the submerged nozzle based on the temperature thermogram of the copper plate of the crystallizer according to claim 2, wherein the threshold value T ℃ in the fifth step is 2-5 ℃.
5. The method for detecting the drift current of the submerged nozzle based on the temperature thermogram of the copper plate of the crystallizer according to claim 2, wherein in the sixth step, the percentage of X is between 40 and 60 percent.
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