High-precision slag thickness detection method and device based on kalman filtering

Abstract

The present application relates to the technical field of protection slag thickness measurement, and particularly relates to a high-precision protection slag thickness detection method and device based on Kalman filtering, wherein the method predicts the thickness of the protection slag in a crystallization tank through a protection slag adding flowmeter, simultaneously detects the thickness of the protection slag in the crystallization tank by using an infrared sensor, and then corrects the sensor measurement value by using the gain of Kalman filtering; the device comprises an infrared measurement module, a protection slag adding flow measurement module, a first calculation module, a second calculation module and a third calculation module; the detection method and device continuously update the Kalman filtering optimization function, so that the protection slag thickness measurement value in the crystallization tank is closer to the true value, and the accurate measurement of the protection slag thickness value in the crystallization tank is effectively realized, and more accurate data is provided for the control of the protection slag thickness of the continuous casting process.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of protective slag thickness measurement, and in particular to a high-precision protective slag thickness detection method and device based on Kalman filtering. BACKGROUND

[0002] The protective slag in the crystallization pool is an important additive material in the continuous casting billet production process of the metallurgical industry. Its dosage control directly affects the quality of the billet and its production cost, so it has high requirements for its addition amount and performance. At present, other countries in the world have adopted advanced technology and process to control the dosage of protective slag in order to save production cost and improve the quality of the billet. The continuous casting production practice of domestic and foreign steel plants shows that even if the selected protective slag is correct, the thin thickness of the protective slag in the crystallization pool will lead to "red leakage". The surface temperature of the protective liquid slag is usually required to be about 400 degrees Celsius in gray state, and the non-surface temperature is about 800 degrees Celsius in red state. After many experiments, adjustments and controls, the protective liquid slag layer is usually 7-15 mm thick, and the original slag layer is 15-20 mm thick. The total thickness of the protective slag is 30-50 mm, and the slag surface is uniform, which is beneficial to the stable formation of the "three-layer structure" of the protective slag surface.

[0003] Chinese Patent No. CN110595419A discloses a protective slag liquid slag layer thickness measurement system and method, which comprises a control device, a moving device and a carbon concentration sensor. The carbon concentration sensor is electrically connected with the control device, and is arranged at the moving end of the moving device. The moving device vertically inserts the carbon concentration sensor into the liquid surface of the protective slag layer, and makes the probe of the carbon concentration sensor pass through the protective slag layer and extend into the steel liquid below the liquid surface. The carbon concentration sensor collects the carbon concentration information of different positions from the protective slag layer to the steel liquid in real time and transmits it to the control device. As can be seen from the above technical solution, the protective slag liquid slag layer thickness measurement system and method has the following problems: the detection method using the carbon concentration sensor measures the protective slag thickness of the measurement point by vertically inserting the carbon concentration sensor into the liquid surface of the protective slag layer. However, the sensor detection is a contact measurement, which destroys the integrity of the protective slag layer and affects the quality of the continuous casting steel billet. SUMMARY

[0004] Therefore, the present application provides a high-precision protective slag thickness detection method and device based on Kalman filtering to overcome the problem of contact measurement destroying the integrity of the protective slag layer in the prior art.

[0005] To achieve the above-mentioned purpose, on the one hand, the present application provides a high-precision protective slag thickness detection method based on Kalman filtering, which comprises:

[0006] Step S1, set an infrared camera to obtain an infrared image radiated from a region of a mold flux layer on an inner wall of a crystallization tank, and calibrate a height value of the inner wall of the crystallization tank and a detection range of the infrared camera;

[0007] Step S2, set a liquid level controller to control a molten steel liquid surface height to be stable at a fixed height, and the fixed height is within a height range of a measurement region of the infrared camera;

[0008] Step S3, perform a gray scale processing on the infrared image, and identify a position of an interface line between a gray scale image of the inner wall of the crystallization tank and a gray scale image of the mold flux in the infrared image through a jump identification algorithm;

[0009] Step S4, calculate a first measured thickness value of the mold flux according to the height value corresponding to the interface line and the molten steel liquid surface height;

[0010] Step S5, set a mold flux flow meter in a mold flux conveying pipeline of a mold flux feeder to measure a volume of the mold flux added into the crystallization tank;

[0011] Step S6, calculate a second measured thickness value of the mold flux in the crystallization tank according to a previously measured area of the molten steel in the crystallization tank and the volume of the mold flux in the crystallization tank;

[0012] Step S7, determine an optimization function of Kalman filtering;

[0013] Step S8, input the first measured thickness value and the second measured thickness value into the optimization function of Kalman filtering to obtain a corrected thickness value of the mold flux in the crystallization tank, and update the optimization function according to the corrected thickness value;

[0014] Step S9, periodically control the infrared camera to obtain the infrared image, control the mold flux flow meter to obtain the volume of the mold flux added into the crystallization tank, and repeat the steps S3 to S8 to obtain the corrected thickness value of the mold flux in the crystallization tank in each period.

[0015] Further, in the step S1, a cooling and heat insulation device is arranged between the infrared camera and the crystallization tank to physically isolate the infrared camera from the crystallization tank and cool the infrared camera;

[0016] The detection range of the infrared camera is calibrated to be 0-70 mm, and a resolution value thereof is 1 mm;

[0017] The method for calibrating the height value of the inner wall of the crystallization tank is that positions of each point on the infrared image obtained by the infrared camera are corresponded to the height value of the inner wall of the crystallization tank, so that the height value of the inner wall of the crystallization tank corresponding to any point on the infrared image is determined.

[0018] Further, in step S2, the liquid level of the molten steel is kept at a fixed height by a fuzzy PID algorithm.

[0019] Further, in step S4, the computer converts the infrared image into a gray scale image by gray scale processing, and identifies the position of the boundary line between the gray scale image of the inner wall of the crystallization tank and the gray scale image of the slag of the protective liquid in the infrared image according to a jump identification algorithm to determine the height value of the protective slag corresponding to the boundary line.

[0020] The jump identification algorithm determines the position of the boundary line by identifying the gray scale value of each point on the infrared image and the difference between the gray scale values of adjacent points to determine the gray scale jump point.

[0021] Further, in step S6, the consumption speed of the protective slag is set in advance, and the protective slag flow meter periodically acquires the volume of the protective slag in a measurement period, and the volume of the protective slag in the crystallization tank is determined by the following formula:

[0022] V A = V0+ V1-V2

[0023] wherein V A is the volume of the protective slag in the current crystallization tank, V0 is the volume of the protective slag in the crystallization tank calculated in the previous measurement period, V1 is the volume of the protective slag in the current measurement period acquired by the protective slag flow meter periodically, and V2 is the consumption volume of the protective slag in the current measurement period, V2 = v α × T, v α is the consumption speed of the protective slag set in advance, and T is the length of the measurement period set in advance.

[0024] Further, in step S6, the second measurement thickness value is determined by the following formula:

[0025]

[0026] wherein H is the second measurement thickness value, V A is the volume of the protective slag in the current crystallization tank, every 10 sec is taken as a detection period, S is the area of the crystallization tank, η is the thickness compensation coefficient of the protective slag, ε is the addition volume of the protective slag in a detection period, φ is the consumption volume of the protective slag in a detection period, K is a constant coefficient, and is related to the consumption amount of the protective slag, is the distribution uniformity coefficient of the protective slag in the crystallization tank, which is determined by the corresponding addition equipment.

[0027] Further, in step S7, the optimization function of the Kalman filter is represented as:

[0028]

[0029] wherein Z is the corrected thickness value of the current measurement period, Z t Z is the measurement value of the infrared measurement module, K is the second calculated measurement thickness value of the current measurement period, K t K is the Kalman gain, I is the unit matrix, and the input value of the function is the current corrected estimate value of the protective slag Z is the measurement value of the infrared measurement module, t , and the output value is the corrected thickness value of the protective slag

[0030] wherein, The calculation method is as follows:

[0031]

[0032] is expressed as wherein ε is the addition volume of the protective slag in a detection period, and φ is the consumption volume of the protective slag in a detection period, Z is the corrected estimate value of the previous measurement period, F is the factor of the consumption and addition of the protective slag, B is the addition change factor of the protective slag, and U t-1 is the measurement time factor.

[0033] Further, in step S8, the optimization function is updated according to the corrected thickness value, and the update formula is as follows:

[0034] P t =(I-K t X)P t -

[0035] wherein P t is the covariance of the corrected estimate value used for the next optimization function calculation after the update, I is a constant factor, K t is the Kalman gain, X is the unit matrix, and P t - is the covariance of the corrected estimate value of the current measurement period.

[0036] Further, in step S8, the calculation method of the Kalman gain is the same as the calculation method of the covariance of the current measurement period, and the calculation formula is as follows:

[0037] K t =P t-1 X T (XP T-1 X T +R) -1

[0038] wherein K t is the Kalman gain, and Pt-1 X is the optimal prior estimate covariance of the last cycle T I is a unit matrix, R is the observation noise variance of the infrared sensor, P t - P is the covariance of the current measurement cycle t - = FP t-1 F T +Q, Q is the process noise measured by the mold powder flowmeter.

[0039] In another aspect, the present application also provides a detection device, comprising:

[0040] An infrared measurement module is arranged near the inner wall of the crystallization tank, which is used to measure the infrared image of the inner wall of the crystallization tank and the mold powder layer in the range of the inner wall of the crystallization tank and the mold powder layer by the infrared camera, and includes a cooling and heat insulation device arranged outside the infrared camera to avoid damage to the infrared camera caused by high temperature;

[0041] A mold powder addition flow measurement module is connected with the mold powder addition device, which is used to obtain the volume of the mold powder added into the crystallization tank within the measurement cycle time;

[0042] A first calculation module is connected with the infrared measurement module, which is used to calculate the first measured thickness value of the mold powder according to the infrared image;

[0043] A second calculation module is connected with the mold powder addition flow measurement module, which is used to calculate the second measured thickness value of the mold powder according to the volume of the mold powder measured by the mold powder flowmeter;

[0044] A third calculation module is connected with the first calculation module and the second calculation module respectively, which is used to calculate the corrected thickness value of the mold powder according to the optimization function of the Kalman filter, and update the optimization function according to the corrected thickness value;

[0045] Wherein, the first measured thickness value is calculated according to the height value corresponding to the boundary line and the liquid level of the molten steel, the second measured thickness value is calculated according to the volume of the mold powder measured by the mold powder flowmeter and the area of the crystallization tank measured in advance, and the input value of the optimization function is the second measured thickness value and the first measured thickness value, and the output value is the corrected thickness value.

[0046] Compared with the prior art, the present application has the beneficial effects that the thickness of the crystallization pool protective slag is detected by the infrared camera, the current value of the protective slag is predicted by the optimal result of the flow meter, and the thickness of the protective slag in the process of adding slag is accurately measured by using the Kalman filter function, the protective slag measurement technology adopted in the present application scheme does not need to substantially contact the protective slag layer, thereby avoiding the damage of the contact protective slag measurement to the protective slag layer, the thickness of the protective slag layer is indirectly calculated through the two different measurement values of the image and the flow meter, and then the optimization function of the Kalman filter is used for optimization and updating, so that the thickness calculated by the optimization function can accurately reflect the thickness measurement value of the protective slag layer.

[0047] Further, the present application sets a cooling and heat insulation device between the infrared camera and the crystallization pool, so as to physically isolate the infrared camera from the crystallization pool and cool the infrared camera, effectively protecting the infrared camera from high temperature, enabling the infrared camera to shoot at a suitable working temperature, and obtaining more accurate images, thereby ensuring the accuracy of the first measured thickness value.

[0048] Further, the present application sets a fuzzy PID algorithm in the molten steel liquid level controller to keep the molten steel liquid surface at a fixed height, and inputs the height value of the fixed height into the computer for storage, which is used to stabilize the molten steel liquid surface at a fixed height and directly obtain the height, thereby simplifying the calculation process of the first measured thickness value and making the first measured thickness value more accurate.

[0049] Further, the present application determines the position of the interface line by processing the infrared image into a gray-scale image, thereby simplifying the processing process of the color image, and since the image contour features obtained by the infrared image are divided by temperature, the measurement of the highest position of the protective slag height is not affected by the determination of the interface line position by the gray-scale image, thereby further ensuring the measurement efficiency of the first measured thickness value.

[0050] Further, the present application determines the current protective slag volume in the crystallization pool through the reading of the protective slag flow meter, and in the measurement period, the protective slag has an amount of addition and consumption, so that the addition amount of the protective slag is accurately measured by the flow meter, thereby improving the accuracy of the second measured thickness value, and since the addition of the second measured thickness value can be used as a reference index for the accuracy of the first measured thickness value, the accuracy of the protective slag thickness detection value of the present application is improved.

[0051] Further, the present application considers the protective slag thickness compensation coefficient when calculating the second measured thickness, different casting environments have different influences on the consumption rate of the protective slag, and the protective slag thickness compensation coefficient is added in the calculation process, thereby reducing the influence of the unstable consumption value of the protective slag on the second measured thickness value.

[0052] Further, the present application corrects the thickness measurement result of the protection slag by the optimization function of Kalman filtering, inputs the first measured thickness value obtained by the infrared measurement module and the second measured thickness value obtained by the protection slag and slag adding flow measurement module into the function for measurement correction, corrects the calculated correction value compared with the first measured thickness value and the second measured thickness value, simultaneously corrects the correction value by using multiple measurement indexes, avoids the error fluctuation of the single measurement value affecting the output detection value, makes the result more accurate, and thus realizes the purpose of accurately measuring the thickness of the protection slag.

[0053] Further, the present application updates the optimization function by using the correction value of the thickness of the protection slag, ensures the accuracy of the correction value of the thickness of the protection slag in the next detection period, can control the amount of the protection slag, can scientifically and effectively adjust the production parameters of the continuous casting machine, and improves the production quality of the billet.

[0054] Further, the present application measures the thickness of the protection slag periodically, realizes real-time monitoring of the thickness of the protection slag, and timely feeds back to the slag adding robot, so that the algorithm of the amount of the slag added by the slag adding robot becomes a closed loop control, and reduces the adverse effects on the quality of the billet caused by too much or too little amount of the slag. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 it is the schematic diagram of the high-precision slag thickness detection device based on Kalman filtering of the present application;

[0056] Figure 2 it is the flow chart of the high-precision slag thickness detection method based on Kalman filtering of the present application;

[0057] Figure 3 it is the algorithm flow chart of the optimization function based on Kalman filtering of the present application;

[0058] Figure 1 M: 1, submerged entry nozzle; 2, protection slag layer; 3, crystallization pool; 4, infrared camera; 5, protection slag conveyor; 6, protection slag flow meter; 7, computer; 8, molten steel liquid level controller. DETAILED DESCRIPTION

[0059] In order to make the purpose and advantages of the present application more clear and explicit, the present application is further described below by combining with examples; it should be understood that the specific examples described here are only used for explaining the present application, and are not used for limiting the present application.

[0060] The preferred embodiments of the present application are described below by referring to the drawings. Those skilled in the art should understand that these embodiments are only used for explaining the technical principles of the present application, and are not used for limiting the protection scope of the present application.

[0061] It should be noted that in the description of the present application, the terms of direction or position relationship indicated by "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or position relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0062] In addition, it should be further pointed out that in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected, it can be mechanically connected, or it can be electrically connected, it can be directly connected, or it can be indirectly connected through an intermediate medium, it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0063] Please refer to Figure 1 As shown in the figure, it is a high-precision slag thickness detection device model based on Kalman filtering in the present application, which comprises:

[0064] An infrared measurement module is arranged near the inner wall of the crystallization tank, which is used to measure the infrared image of the crystallization tank inner wall and the protective liquid slag in the range of the protective slag layer of the crystallization tank inner wall through an infrared camera, and comprises a cooling and heat insulation device arranged outside the infrared camera to avoid damage to the infrared camera caused by high temperature;

[0065] A protective slag adding flow measurement module is connected with the protective slag adding device, which is used to obtain the volume of the protective slag added into the crystallization tank in a measurement period;

[0066] A first calculation module is connected with the infrared measurement module, which is used to calculate the first measurement thickness value of the protective slag according to the infrared image;

[0067] A second calculation module is connected with the protective slag adding flow measurement module, which is used to calculate the second measurement thickness value of the protective slag according to the volume of the protective slag measured by the protective slag flow meter;

[0068] A third calculation module is connected with the first calculation module and the second calculation module respectively, which is used to calculate the corrected thickness value of the protective slag according to the optimization function of Kalman filtering, and update the optimization function according to the corrected thickness value;

[0069] Specifically, the infrared measurement module comprises:

[0070] An infrared camera is arranged near the inner wall of the crystallization tank to measure the infrared image of the inner wall of the crystallization tank and the protective liquid slag in the range of the protective slag layer of the inner wall of the crystallization tank by the infrared camera, and the image is transmitted to the computer storage, the purpose is to obtain the image of the position of the boundary line between the gray image of the inner wall of the crystallization tank and the gray image of the protective liquid slag in the infrared image, and to provide a calculation basis for calculating the first measured thickness value of the protective slag according to the height value corresponding to the boundary line and the liquid level height of the molten steel;

[0071] A water-cooled radiator is located between the infrared camera and the wall of the crystallization tank to physically cool the infrared camera, the purpose is to protect the infrared camera, and the detection result of the first measured thickness value of the protective slag calculated according to the infrared image is more accurate;

[0072] The front end of the infrared camera is provided with heat insulation glass to isolate the radiant heat of the molten steel in the crystallization tank to the infrared camera, the purpose is to directly protect the infrared camera, and the detection result of the first measured thickness value of the protective slag calculated according to the infrared image is accurate;

[0073] It can be understood that the specific structure and cooling and heat insulation mode of the cooling and heat insulation device in the application can adopt any one of the prior art, which is not limited here.

[0074] Specifically, the protective slag adding and measuring module comprises:

[0075] A protective slag conveyor is arranged above the crystallization tank, and the conveying port of the protective slag conveyor is arranged above the crystallization tank to uniformly sprinkle the protective slag in the crystallization tank, and it can be understood that the infrared camera is arranged at a position not disturbed by the protective slag conveyor.

[0076] A protective slag flowmeter is connected with the protective slag adding device to obtain the volume of the protective slag added into the crystallization tank in the measurement period, and the measurement result is transmitted to the computer, the purpose is to calculate the second thickness value of the protective slag with the area of the crystallization tank measured in advance.

[0077] Specifically, the first calculation module, the second calculation module and the third calculation module are integrated in the computer, and the computer is provided with a storage area and a calculation area.

[0078] Specifically, the first calculation module comprises:

[0079] A molten steel liquid level controller is connected with the computer to control the liquid level height of the molten steel in the crystallization tank, so that the liquid level height is located in the detection range of the wall calibration of the crystallization tank, the purpose is to calculate the first measured thickness value of the protective slag by the difference between the height value of the boundary line measured by the infrared camera.

[0080] A computer connected with the infrared camera and the molten steel level controller, which aims to obtain the molten steel level height, and receive and process the infrared image taken by the infrared camera to obtain the protection slag interface height and calculate the first measured thickness value of the protection slag.

[0081] Specifically, the second calculation module includes:

[0082] A computer connected with the protection slag flow meter, which is used to obtain the volume of the protection slag added in the measurement period, and calculate the volume of the protection slag in the current crystallization pool according to the known protection slag consumption speed, and then calculate the second measured thickness value of the protection slag according to the previously measured area of the crystallization pool.

[0083] Specifically, the third calculation module includes:

[0084] A computer connected with the infrared camera, the molten steel level controller and the protection slag flow, which is used to generate an optimization function of Kalman filtering, and input the first measured thickness value and the second measured thickness value into the function to calculate the corrected thickness value of the protection slag with an error of 3mm, and update the optimization function of Kalman filtering according to the corrected thickness value.

[0085] Please refer to Figure 2 The Kalman algorithm flowchart of the high-precision slag thickness detection method and device based on Kalman filtering in the present application, which includes:

[0086] Step S1, an infrared camera is set to obtain the infrared image radiated by the protection slag layer area of the inner wall of the crystallization pool, and the height value of the inner wall of the crystallization pool is calibrated with the detection range of the infrared camera;

[0087] Step S2, a liquid level controller is set to control the molten steel liquid level height to be stable at a fixed height, and the fixed height is located within the height range of the measurement area of the infrared camera;

[0088] Step S3, the infrared image is subjected to gray scale processing, and the position of the interface between the gray scale image of the inner wall of the crystallization pool and the gray scale image of the protection liquid slag in the infrared image is identified through a jump identification algorithm;

[0089] Step S4, the first measured thickness value of the protection slag is calculated according to the height value corresponding to the interface and the molten steel liquid level height;

[0090] Step S5, a protection slag flow meter is arranged in the protection slag conveying pipeline of the protection slag adding device to measure the volume of the protection slag added into the crystallization pool;

[0091] Step S6, the second measured thickness value of the protection slag in the crystallization pool is calculated according to the previously measured area of the molten steel in the crystallization pool and the volume of the protection slag in the crystallization pool;

[0092] Step S7, determining the optimization function of the Kalman filter;

[0093] Step S8, inputting the first measured thickness value and the second measured thickness value into the optimization function of the Kalman filter, obtaining a corrected thickness value of the protective slag in the crystallization tank, and updating the optimization function according to the corrected thickness value;

[0094] Step S9, periodically controlling the infrared camera to obtain infrared images and controlling the protective slag flow meter to obtain the volume of the protective slag added into the crystallization tank, repeating the steps S3 to S8 to obtain the corrected thickness value of the protective slag in the crystallization tank in each cycle.

[0095] The present application detects the thickness of the protective slag in the crystallization tank through the infrared camera, simultaneously uses the flow meter to predict the current value of the protective slag, and effectively realizes the accurate measurement of the thickness of the protective slag during the addition of the protective slag by using the Kalman filter function. The protective slag measurement technology adopted in the present application does not need to substantially contact the protective slag layer, avoids the damage to the protective slag layer caused by the contact measurement of the protective slag, indirectly calculates the thickness of the protective slag layer through the two different measurement values of the image and the flow meter, and optimizes and updates through the optimization function of the Kalman filter, so as to effectively ensure that the thickness calculated by the optimization function can accurately reflect the thickness measurement value of the protective slag layer.

[0096] Specifically, in step S1, a cooling and heat insulation device is arranged between the infrared camera and the crystallization tank, so as to physically isolate the infrared camera and the crystallization tank and cool the infrared camera.

[0097] The detection range of the calibrated infrared camera is 0-70mm, and the resolution value is 1mm.

[0098] The method for calibrating the height value of the inner wall of the crystallization tank is that the positions of each point on the infrared image obtained by the infrared camera are corresponded to the height value of the inner wall of the crystallization tank, so as to determine the corresponding height value of the inner wall of the crystallization tank by the position of any point on the infrared image.

[0099] The present application sets a cooling and heat insulation device between the infrared camera and the crystallization tank, so as to physically isolate the infrared camera and the crystallization tank and cool the infrared camera, effectively protects the infrared camera from high temperature, makes the infrared camera shoot at a suitable working temperature, and obtains more accurate images, thereby ensuring the accuracy of the first measured thickness value.

[0100] It can be understood that the selection of the infrared camera in the present application meets the above measurement requirements and calibration requirements, and is not specifically limited here; the specific structure and cooling and heat insulation mode of the cooling and heat insulation device can adopt any one of the prior art, the purpose is to protect the infrared camera, so that the image obtained by the infrared camera is more accurate, and the accuracy of the first measured thickness value is ensured, and is not specifically limited here.

[0101] Specifically, in step S2, the molten steel liquid level is kept at a fixed height by the fuzzy PID algorithm, and the height value of the fixed height is input into the computer for storage, which is used to directly determine the molten steel liquid level, so that the first measured thickness value calculation is more simple and accurate.

[0102] The present application keeps the molten steel liquid level at a fixed height by setting a fuzzy PID algorithm in the molten steel liquid level controller, and inputs the height value of the fixed height into the computer for storage, which is used to keep the molten steel liquid level at a fixed height and can directly obtain the height, thereby simplifying the first measured thickness value calculation process and making the first measured thickness value more accurate.

[0103] It can be understood that the liquid level control algorithm set in the present application is not unique, and the purpose is to keep the liquid level at a fixed value and input into the computer for storage, and other algorithms that can achieve this function can be used instead.

[0104] Specifically, in step S4, the computer processes the infrared image into a gray image by gray processing, and identifies the position of the boundary line between the crystallization pool inner wall gray image and the protective slag gray image in the infrared image according to the jump recognition algorithm to determine the height value of the protective slag corresponding to the boundary line.

[0105] The jump recognition algorithm determines the gray jump point by identifying the gray value of each point on the infrared image and the difference between the gray values of adjacent points, determines the position of the boundary line, and obtains the first measured thickness value by subtracting the height value corresponding to the boundary line from the molten steel liquid level height.

[0106] The present application determines the position of the boundary line by processing the infrared image into a gray image, simplifies the processing process of the color image, and since the image profile feature obtained by the infrared image is divided by temperature, the measurement of the highest position of the protective slag height is not affected by the determination of the boundary line corresponding to the height position by the gray image, which further ensures the measurement efficiency of the first measured thickness value.

[0107] Specifically, in step S6, the consumption speed of the protective slag is set in advance, and the protective slag flow meter periodically obtains the volume of the protective slag in a measurement period, and the volume of the protective slag in the crystallization pool is determined by the following formula:

[0108] V A = V0+ V1-V2

[0109] V A V is the volume of the protective slag in the current crystallization tank, V0 is the volume of the protective slag in the crystallization tank calculated in the previous measurement period, V1 is the volume of the protective slag periodically obtained by the protective slag flow meter in the current measurement period, V2 is the consumption volume of the protective slag in the current measurement period, V2 = ν α × T, ν α is the preset consumption speed of the protective slag, and T is the preset length of the measurement period.

[0110] The present application determines the volume of the protective slag in the current crystallization tank through the reading of the protective slag flow meter. In the measurement period, the protective slag has an amount of addition and consumption. The accuracy of the second measurement thickness value can be improved by accurately measuring the amount of addition of the protective slag through the flow meter. In addition, the addition of the second measurement thickness value can be used as a reference index for the accuracy of the first measurement thickness value, thereby improving the accuracy of the protective slag thickness detection value of the present application.

[0111] Specifically, in step S6, the second measurement thickness value is determined by the following formula:

[0112]

[0113] V A is the volume of the protective slag in the current crystallization tank, S is the area of the crystallization tank, and η is the protective slag thickness compensation coefficient ε is the addition volume of the protective slag in one detection period, φ is the consumption volume of the protective slag in one detection period, K is a constant coefficient, is the distribution uniformity coefficient of the protective slag in the crystallization tank.

[0114] When the consumption amount of the protective slag is a fixed value, the value of the constant coefficient K is 1, The calculation formula of H S - is the area of the protective slag added by the slag clamp in the detection period.

[0115] The present application considers the protective slag thickness compensation coefficient when calculating the second measurement thickness, different casting environments have different influences on the consumption rate of the protective slag, and the protective slag thickness compensation coefficient is added in the calculation process, so that the influence of the unstable consumption value of the protective slag on the second detection thickness value can be reduced.

[0116] Specifically, in step S7, the optimization function of the Kalman filter is represented as:

[0117]

[0118] wherein, Z is a current thickness estimate of the protective slag, K t Z is a measurement value of the infrared measurement module, Z is a current thickness estimate of the protective slag, K t K is a Kalman gain, X is a unit matrix, and the input value of the function is a current protective slag estimate Z is a measurement value of the infrared measurement module, t , and the output value is a corrected thickness value of the protective slag

[0119] wherein, The calculation method of Z is as follows:

[0120]

[0121] Z is a current thickness estimate of the protective slag, K wherein ε is the addition volume of the protective slag in a detection period, and φ is the consumption volume of the protective slag in a detection period, Z is a current thickness estimate of the protective slag, K Z is an optimal value of the protective slag state in the last measurement period, F is a factor of the consumption and addition of the protective slag, B is an addition change factor, and U t-1 is a measurement time factor;

[0122] wherein, since the protective slag is added at a constant speed, the value of the constant factor B is 0;

[0123] The present application corrects the thickness measurement result of the protective slag by using the optimization function of the Kalman filter, inputs the first measurement thickness value obtained by the infrared measurement module and the second measurement thickness value obtained by the protective slag addition flow measurement module into the function to correct the measurement result, corrects the corrected value compared with the first measurement thickness value and the second measurement thickness value, simultaneously corrects the corrected value by using multiple measurement indexes, avoids the error fluctuation of a single measurement value from affecting the output detection value, makes the result more accurate, and thus realizes the purpose of accurately measuring the thickness of the protective slag.

[0124] Specifically, in step S8, the optimization function is updated according to the corrected thickness value, and the update formula is as follows:

[0125] P t =(I-K t X)P t -

[0126] wherein P t is the updated corrected estimate covariance, I is a constant value 1, K t is a Kalman gain, X is a unit matrix, and P t -covariance of the current measurement period; update P t The purpose is to update the optimization function for the next protective slag thickness correction estimated value operation, to achieve the purpose of high-precision measurement.

[0127] The present application ensures the accuracy of the protective slag thickness correction value of the next detection period by updating the optimization function with the protective slag thickness correction value, can control the amount of protective slag, can scientifically and effectively adjust the production parameters of the continuous casting machine, and improve the production quality of the billet.

[0128] Specifically, in step S8, the calculation method of the Kalman gain and the calculation method of the covariance of the current measurement period, the calculation formula is as follows:

[0129] K t = P t-1 X T (XP T-1 X T +R) -1

[0130] Wherein, K t is the Kalman gain, P t-1 is the optimal priori estimation value covariance of the last period, X T is a unit matrix, R is the observation noise variance of the infrared sensor, P t - is the covariance of the current measurement period, P t - = FP t-1 F T +Q, Q is the process noise measured by the protective slag flowmeter;

[0131] The present application measures the protective slag thickness periodically, monitors the protective slag thickness in real time, and feeds back to the slag adding robot in time, so that the slag adding algorithm of the slag adding robot becomes a closed loop control, reduces the adverse effects on the billet quality caused by excessive or insufficient amount of slag.

[0132] So far, the technical scheme of the present application has been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to related technical features without deviating from the principles of the present application, and the technical scheme after the changes or replacements will fall within the protection scope of the present application.

[0133] The above merely illustrates the preferred embodiments of the present application, and is not used to limit the present application; for those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A high-precision slag thickness detection method based on Kalman filtering, characterized in that, include: Step S1: Set up an infrared camera to acquire infrared images radiated from the protective slag layer area on the inner wall of the crystallization tank, and calibrate the height value of the inner wall of the crystallization tank and the detection range of the infrared camera. Step S2: Set the liquid level controller to control the height of the molten steel to be stable at a fixed height, and the fixed height is within the height range of the measurement area of ​​the infrared camera; Step S3: Perform grayscale processing on the infrared image and use a jump recognition algorithm to identify the position of the boundary line between the grayscale image of the inner wall of the crystallization pool and the grayscale image of the protective liquid residue in the infrared image. Step S4: Calculate the first measured thickness value of the protective slag based on the height value corresponding to the boundary line and the height of the molten steel surface; Step S5: Install a protective slag flow meter in the protective slag conveying pipeline of the protective slag feeder to measure the volume of protective slag added to the crystallization tank; Step S6: Calculate the second measured thickness value of the protective slag in the crystallization pool based on the pre-measured area of ​​the molten steel in the crystallization pool and the volume of the protective slag in the crystallization pool. Step S7, determine the optimization function of the Kalman filter; the optimization function of the Kalman filter is expressed as: ; in, To correct the estimated value, the final corrected thickness value of the protective slag is obtained. These are the measured values ​​from the infrared measurement module. This is the current estimated thickness of the protective slag. For Kalman gain, The function takes an identity matrix as its input value, and the input value is the estimated value of the current protective slag. Measured values ​​from the infrared measurement module The output value is the corrected thickness value of the protective slag; ; in, The calculation method is as follows: for ,in The volume of protective slag added during one testing cycle. The volume of protective slag consumed in one testing cycle. The optimal value for the state of the protective slag in the previous measurement cycle is given by F, where F is a factor of the amount of protective slag consumed and added. It is the slag addition variation factor. For measuring the time factor; Step S8, input the first measured thickness value and the second measured thickness value into the optimization function of the Kalman filter to obtain the corrected thickness value of the protective slag in the crystallization tank, and update the optimization function according to the corrected thickness value; the optimization function is updated according to the corrected thickness value, and the update formula is as follows: ； in, This is the covariance of the corrected estimate used for the next optimization function calculation after the update. The constant coefficient, This is the covariance of the corrected estimate for the current measurement period; The calculation methods for the Kalman gain and the covariance of the current measurement period are as follows: ; in, The covariance is the optimal prior estimate from the previous period. Let R be an identity matrix, and let R be the observation noise variance of the infrared sensor. Q represents the process noise measured by the protective slag flow meter; Step S9: Periodically control the infrared camera to acquire infrared images and control the protective slag flow meter to acquire the volume of protective slag added to the crystallization tank. Repeat steps S3 to S8 to obtain the corrected thickness value of the protective slag in the crystallization tank in each cycle.

2. The high-precision slag thickness detection method based on Kalman filtering according to claim 1, characterized in that, In step S1, a cooling and heat insulation device is provided between the infrared camera and the crystallization pool to physically isolate the infrared camera from the crystallization pool and to cool the infrared camera. The detection range of the calibrated infrared camera is 0~70mm, and its resolution is 1mm. The method for calibrating the height of the inner wall of the crystallization pool is as follows: the position of each point on the infrared image acquired by the infrared camera is correlated with the height value of the inner wall of the crystallization pool, so as to determine the height value of the inner wall of the crystallization pool corresponding to the position of any point on the infrared image.

3. The high-precision slag thickness detection method based on Kalman filtering according to claim 2, characterized in that, In step S2, the height of the molten steel surface is maintained at a fixed height using a fuzzy PID algorithm.

4. The high-precision slag thickness detection method based on Kalman filtering according to claim 3, characterized in that, In step S4, the computer processes the infrared image into a grayscale image and identifies the boundary line between the grayscale image of the inner wall of the crystallization pool and the grayscale image of the protective liquid slag in the infrared image according to the jump recognition algorithm to determine the height value of the protective slag corresponding to the boundary line. The jump recognition algorithm determines the gray-level jump point by identifying the gray-level value of each point on the infrared image and the difference in gray-level values ​​between adjacent points, and thus determines the position of the boundary line.

5. The high-precision slag thickness detection method based on Kalman filtering according to claim 4, characterized in that, In step S6, a pre-set consumption rate of the protective slag is used. The protective slag flow meter periodically acquires the volume of the protective slag within a measurement cycle. The volume of the protective slag in the crystallization tank is determined by the following formula: ； in, This represents the current volume of the protective slag in the crystallization pool. This refers to the volume of protective slag in the crystallization tank calculated in the previous measurement cycle. To ensure that the protective slag flow meter periodically obtains the volume of protective slag within the current measurement cycle, This represents the volume of protective slag consumed during the current measurement cycle. The consumption rate of the protective slag is a preset value, and T is the preset measurement cycle duration.

6. The high-precision slag thickness detection method based on Kalman filtering according to claim 5, characterized in that, In step S6, the second measured thickness value is determined by the following formula: ; in, This is the second measured thickness value. The current volume of the protective slag in the crystallization tank is represented by η, with each 10-second interval serving as a detection cycle. S represents the area of ​​the crystallization tank, and η is the protective slag thickness compensation coefficient. The volume of protective slag added during one testing cycle. K represents the volume of protective slag consumed in one testing cycle, where K is a constant coefficient related to the amount of protective slag consumed. The uniformity coefficient of the protective slag distribution in the crystallization tank is determined by the corresponding slag feeding equipment.

7. A detection device for use in the high-precision protective slag thickness detection method based on Kalman filtering as described in any one of claims 1-6, characterized in that, include: An infrared measurement module is installed near the inner wall of the crystallization tank to measure the infrared image of the inner wall of the crystallization tank and the protective liquid slag within the range of the protective slag layer of the inner wall of the crystallization tank using an infrared camera. It also includes a cooling and heat insulation device installed outside the infrared camera to avoid damage to the infrared camera caused by high temperature. A molten steel level control module, which is connected to a computer, is used to control the height of the molten steel level in the crystallization tank; The protective slag addition flow rate measurement module is connected to the protective slag feeder and is used to obtain the volume of protective slag added to the crystallization tank within the measurement cycle time. The first calculation module, which is connected to the infrared measurement module, is used to calculate the first measured thickness value of the protective slag based on the infrared image. The second calculation module is connected to the protective slag addition flow measurement module and is used to calculate the second measured thickness value of the protective slag based on the volume of added protective slag measured by the protective slag flow meter. The third calculation module is connected to the first calculation module and the second calculation module respectively, and is used to calculate the corrected thickness value of the protective slag according to the optimization function of Kalman filtering, and update the optimization function according to the corrected thickness value. The first measured thickness value is calculated based on the height value corresponding to the boundary line and the height of the molten steel surface. The second measured thickness value is calculated based on the volume of added protective slag measured by the protective slag flow meter and the pre-measured area of ​​the crystallization pool. The input values ​​of the optimization function are the second measured thickness value and the first measured thickness value, and the output value is the corrected thickness value.

Images