CN113113088A - Converter carbon content index evaluation and temperature analysis method based on artificial intelligence - Google Patents
Converter carbon content index evaluation and temperature analysis method based on artificial intelligence Download PDFInfo
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Abstract
The invention discloses a converter carbon content index evaluation and temperature analysis method based on artificial intelligence, which solves the problem that the converter blowing process depends on more artificial experiences in the prior art. The invention comprises the following steps: extracting the flame characteristics of a furnace mouth, constructing a flame area correction evaluation model, constructing a preliminary estimation model of the carbon content index of the converter according to the flame characteristics, and evaluating the carbon content index of the converter; obtaining a carbon content index sequence in the converting process based on the final carbon content evaluation model, analyzing the corresponding relation between the molten steel carbon content index and the converting end point, and judging the converting end point of the converter; presetting a tapping range as [ T1, T2], and detecting whether the temperature of molten steel at the end point of the converter is within the tapping temperature range; and constructing different temperature analysis models and adjusting the temperature of the molten steel. The technology obtains the carbon content index of the molten steel, accurately judges the blowing end point of the converter, and adjusts the temperature of the molten steel according to the temperature analysis model so as to ensure that the tapping temperature range is met.
Description
Technical Field
The invention relates to the field of artificial intelligence computer vision processing, in particular to a converter carbon content index evaluation and temperature analysis method based on artificial intelligence.
Background
At present, the method mainly depends on the experience of a flame observer to change the flame in the converter and the smelting sound, and utilizes common detection methods such as stable carbon measurement, rapid analysis of sampling in front of the converter and the like to judge the end point.
Disclosure of Invention
The invention overcomes the problem that the converter blowing process depends on more manual experiences in the prior art, and provides the converter carbon content index evaluation and temperature analysis method based on artificial intelligence, which has high practicability and accurate tapping temperature.
The technical scheme of the invention is to provide an artificial intelligence-based converter carbon content index assessment and temperature analysis method, which comprises the following steps: comprises the following steps:
acquiring a flame image of a converter mouth in real time through a CCD camera which is erected in front of a converter and is provided with high temperature and dustproof protection, and shooting flame pictures of the converter mouth in different converting periods on site by using a camera;
step two, processing the flame image of the furnace mouth, extracting the flame characteristics of the furnace mouth, constructing a flame area correction evaluation model, establishing a converter carbon content index preliminary estimation model according to the flame characteristics, and evaluating the converter carbon content index, wherein the flame characteristics comprise the flame ratio and the flame brightness;
establishing a carbon content index adjustment model as a final carbon content index evaluation model to estimate the carbon content index of the molten steel;
fourthly, a carbon content index sequence in the converting process can be obtained based on the final carbon content evaluation model, the corresponding relation between the carbon content index of the molten steel and the converting end point is analyzed according to the carbon content index sequence, the converting end point of the converter is judged, and when the converting end point is reached in the steelmaking process of the converter, an oxygen supply system of an oxygen lance of the converter stops blowing oxygen into the converter;
step five, presetting the tapping range of a certain steel type as [ T1, T2], if the system detects that the temperature of the molten steel at the end point of the converter is in the tapping temperature range, the tapping condition is met, and tilting the furnace body to pour the molten steel into a ladle for a subsequent steel casting process;
and step six, if the temperature of the molten steel of the converter does not meet the tapping temperature, constructing different temperature analysis models and adjusting the temperature of the molten steel.
In the first step, the camera acquisition frame rate is 20 frames per second, and the camera acquires working images of converter blowing every 5 seconds.
The second step comprises the following steps:
step 2.1, the specific process of calculating the flame ratio is as follows: firstly, adding pixels with the pixel value of 1 in the image, wherein the sum of the pixels is the flame area in the image and is recorded as ScC is 1,2 … N, c is an image frame, ScRepresenting the flame area in the image of the frame c, wherein the flame area correction model specifically comprises the following steps:
in the formula, O0Average oxygen content in the whole image acquisition time, wherein O is real-time oxygen content; y is0Is obtained by empirical statistics according to the maximum furnace age of the converter, and Y is the real furnace of the converter when the flame image is detectedAge;
step 2.2, constructing a flame ratio analysis model for calculating the flame ratio, wherein the flame ratio analysis model has the functions of:
wherein m and n are the size of the image, S'cFlame area, τ, estimated for the modelcThe ratio of flame in the selected c frame image is calculated; taking the flame binary image as an ROI (region of interest), multiplying the ROI with an original RGB (red, green and blue) image to obtain an RGB image only containing flame, finally calculating the brightness information of the flame, and after acquiring the corresponding RGB flame image, the flame brightness analysis method specifically comprises the following steps: firstly, normalizing the R, G and B values of the RGB image:
then, the maximum value of the normalized R ', G ', B ' is obtained, and the obtained maximum value is the brightness value of the flame image:
Cmax＝max(R’,G’,B’)
Vi,j＝Cmax
step 2.2, establishing a flame brightness analysis model according to the brightness information of the image:
where ρ iscRepresenting flame brightness in the c-th frame imageThe greater the value of the model function, the higher the degree of brightness of the flame.
Setting alpha and beta as the flame ratio and the proportional relation between the brightness degree and the carbon content index of the flame in the third step, and taking the values as follows: α ═ 0.4, β ═ 0.6; the carbon content index estimation pre-model expression is as follows:
ωc＝exp(ατc+βρc)+A
in the formula, A is a model adjustable parameter, a carbon content index adjustment model is established based on analysis and comparison of converter temperature and tapping temperature, the tapping range of steel grades is set as [ T1, T2], the temperature in the converter is analyzed, a corresponding carbon content adjustment model is established based on the converter temperature, and an accurate carbon content index is obtained; when the temperature of the converter is lower than the minimum value of the optimal temperature range, namely T < T1, the temperature in the converter is too low, so that the predicted value of the carbon content index is low, a carbon content index adjustment function is constructed to ensure the evaluation accuracy of the final carbon content index, and the final evaluation model expression of the carbon content index is as follows:
ω'c＝ωcln(5ΔT'+e)
if the temperature in the converter is T2-T1, the temperature will be
Analyzing the converter temperature as the optimal converter temperature, and constructing a carbon content index adjustment model, wherein the final evaluation model of the carbon content index in the temperature range is as follows:
in the formula, r is a conversion factor and is set to be 0.5; when the temperature in the converter is higher than the maximum value of the optimal temperature range, namely T is greater than T2, the temperature in the converter is considered to be overhigh, and the final carbon content index evaluation model expression is as follows:
aiming at the final carbon content evaluation model established above, and comprising:
wherein, omega'cRepresenting the most accurate carbon content index calculation.
In the fourth step, the carbon content index calculation is carried out on a series of acquired image data to obtain a carbon content index sequence: [ omega ]1,ω2…ωN]Analyzing and judging the converter steelmaking blowing end point according to the obtained carbon content index sequence; assuming that the index range of carbon content when a certain steel product reaches the blowing end point is [ omega 1, omega 2]](ii) a Calculating the carbon-containing index of the converter according to the final carbon content index evaluation model, wherein when the carbon-containing indexes obtained from multiple continuous N frames are all in [ omega 1, omega 2]]When the converter steelmaking reaches the blowing end point, the system sends an instruction to an oxygen supply system of an oxygen lance of the converter to stop blowing oxygen into the converter, and carbon-containing indexes obtained by 5 continuous frames are set to be [ omega 1, omega 2]]If the carbon content index does not change greatly within the range and 10 continuous frames later, the end point of the converting is considered to be reached.
The temperature analysis model meeting the tapping condition in the fifth step is as follows: firstly, judging the end point of a converter according to the carbon content index, analyzing the temperature of the molten steel in the converter when the end point of smelting is reached, comparing the temperature of the molten steel in the converter with the tapping temperature range of steel types, setting the tapping range of the steel types as [ T1, T2], if the system detects that the temperature of the converter is within the tapping temperature range, T2 is not more than T1, the tapping condition is met, the converter body can be inclined, and the molten steel is poured into a ladle for the subsequent steel casting process.
The specific process of the temperature analysis model which does not meet the tapping condition in the sixth step is as follows:
when T < T1, the temperature of molten steel in the converter is too low after the converter reaches the converting end point, if the molten steel is poured into a ladle, the pouring operation cannot be smoothly carried out, the quality of steel is affected, and in severe cases, the steel cannot be poured, a molten steel temperature raising analysis model is established, the temperature of the molten steel in the converter is raised, and the temperature raising analysis model expression is as follows:
in the formula, k is an adjustable coefficient, m is the mass of the temperature raising agent, the unit is kg, the values of k are different for different temperature raising agents, and an implementer can determine the value of the adjustable coefficient of the model according to the type of the selected temperature raising agent and set the value to be 2;
when T is greater than T2, the molten steel temperature is too high, the molten steel is easy to absorb air, the age of the converter and the alloy yield are reduced, in order to reduce the molten steel temperature and prevent the burning accident caused by the too high molten steel temperature, a function model for reducing the molten steel temperature is constructed, and the function of the cooling model specifically comprises the following steps:
M＝R(T-T2)
wherein M is the adding amount of the coolant in the converter and is kg, R is an adjusting factor, and R is set to be 16.
Compared with the prior art, the converter carbon content index evaluation and temperature analysis method based on artificial intelligence has the following advantages: the method comprises the steps of firstly establishing a converter carbon content index estimation model according to the flame characteristics of a converter mouth, establishing different carbon content index adjustment models in order to prevent the phenomena of low carbon pulling or high carbon pulling caused by inaccurate carbon content index prediction, obtaining a final carbon content index estimation model aiming at different conditions, reducing the error value of the converter carbon content index estimated by a system, accurately identifying a steelmaking end point based on the molten steel carbon content index, and improving the accurate judgment of the system on a smelting end point.
Meanwhile, considering the problems that the casting operation is difficult and the steel quality is reduced due to overhigh and overlow temperature during steel liquid tapping, the temperature of the converter steel can meet the tapping temperature range according to the comparison between the temperature of the converter steel after blowing is stopped and the tapping temperature range, and a temperature analysis model is established, so that the problems of steel product quality reduction, safety accidents and the like caused by the uncomfortable tapping temperature are solved.
The system has higher practicability, can automatically obtain the carbon content index of the molten steel and accurately judge the converting end point of the converter, and simultaneously adjusts the temperature of the molten steel according to the established temperature analysis model so as to ensure that the tapping temperature range is met.
Drawings
Fig. 1 is a schematic structural diagram of the working principle of the present invention.
Detailed Description
The method for evaluating the carbon content index and analyzing the temperature of the converter based on the artificial intelligence is further described by combining the attached drawings and the specific embodiment:
as shown in the figure, in this embodiment, a converter carbon content index evaluation model is first established according to the characteristics of the flame at the furnace mouth, and a final carbon content index evaluation model is established for accurately evaluating the carbon content index of molten steel in consideration of the influence of the temperature in the furnace on the judgment of the carbon content index. And acquiring a carbon content index sequence in the converting process according to the final carbon content index evaluation model, and judging the converting end point of the converter. When the converter steelmaking reaches the converting end point, the system sends an end point control instruction, closes the converter oxygen lance oxygen supply system and stops blowing oxygen into the converter. Meanwhile, the system adopts a thermocouple temperature measuring device to measure the temperature of the molten pool, different models are established aiming at the condition that the temperature in the furnace is too high or too low, the temperature of the molten steel is analyzed and adjusted, so that the temperature of the molten steel can meet the tapping temperature range when the converting process reaches the end point, and the problems of quality reduction of steel products, safety accidents and the like caused by too high or too low tapping temperature are avoided.
The specific main implementation steps are as follows:
firstly, acquiring a flame image of a converter mouth in real time through a CCD camera which is erected in front of a converter and is provided with high temperature and dustproof protection, and shooting flame pictures of the converter mouth in different converting periods on site by using a camera;
secondly, processing a furnace mouth flame image, extracting furnace mouth flame characteristics, constructing a flame area correction evaluation model to ensure that the accurate flame ratio is obtained subsequently, and finally constructing a converter carbon content index preliminary estimation model according to the flame characteristics for evaluating the converter carbon content index;
considering that the carbon content judgment has errors due to overhigh and overlow temperature, establishing a carbon content index adjustment model as a final carbon content index evaluation model to accurately estimate the carbon content index of the molten steel;
fourthly, a carbon content index sequence in the converting process can be obtained based on the final carbon content evaluation model, the converter converting end point is judged according to the carbon content index sequence and the corresponding relation between the molten steel carbon content index and the converting end point is analyzed, and when the converter steelmaking reaches the converting end point, an oxygen supply system of an oxygen lance of the converter stops blowing oxygen into the converter;
and fifthly, tapping can be performed when the tapping temperature ranges of different steel types are different and the molten steel temperature meets the tapping temperature range. Presetting the tapping range of a certain steel grade as [ T1,2], if the system detects that the temperature of the molten steel at the end point of the converter is in the tapping temperature range, the tapping condition is met, the furnace body can be inclined, and the molten steel is poured into a ladle for a subsequent steel casting process;
and step six, if the temperature of the molten steel of the converter does not meet the tapping temperature, constructing different temperature analysis models, and adjusting the temperature of the molten steel to ensure that the temperature of the molten steel reaching the end point is within the tapping temperature range of the steel product, so as to prevent the problems of quality reduction and the like of the steel product caused by overhigh and overlow tapping temperatures.
The specific detailed implementation steps are as follows:
1. the method comprises the steps of firstly, carrying out preliminary rough estimation on a converter steelmaking carbon content index through image characteristics, then obtaining an accurate molten steel carbon content index through a final adjustment model, improving the calculation precision of the carbon content index, and then carrying out regulation and control on the temperature of molten steel which does not meet the tapping temperature so as to ensure that the converter temperature reaching the end point can meet the tapping temperature range of the molten steel.
2. The method comprises the steps of firstly, acquiring a flame image of a converter mouth through a camera, wherein the camera is a CCD camera which is fixed in front of a converter and is provided with high temperature and dustproof protection, and the flame image of the converter mouth of the converter can be acquired in real time so as to be convenient for subsequent flame analysis. In order to reduce the power consumption of the camera, the acquisition frame rate of the camera is set to be 20 frames per second, the camera acquires the images once every 5 seconds, and the images acquired by the camera record the whole process of the converter blowing for the subsequent analysis of the carbon content index at the blowing end point and the like.
3. The flame and the spark at the furnace mouth are an important judgment basis for the carbon content of the molten steel, the characteristics of the flame are extracted, the carbon content of the molten steel is estimated according to the characteristics, the flame brightness degree and the flame ratio are mainly extracted as flame characteristic values of different periods for the flame characteristics, and the index of the carbon content of the molten steel is estimated based on the characteristic values.
4. In order to extract the flame area, the flame image is segmented to extract the flame in the image, and there are many methods for image segmentation: threshold-based segmentation methods, region-based image segmentation, edge-based segmentation algorithms, wavelet transform-based segmentation methods, and the like. And extracting flames by adopting an image segmentation algorithm based on a threshold value.
5. The threshold value is selected and determined by adopting a histogram in the image segmentation process, and the method has the advantages that the contrast between the light and the shade of the flame and the black converter is strong, so that the distribution of background pixels and flame pixels on the image on the histogram can present double-humpness, and the gray value corresponding to the valley bottom between the two peaks is used as the segmentation threshold value T' of the image.
In the formula, T 'is a flame segmentation threshold, and its value selection operator needs to determine according to the gray histogram of the image, and T' is set to 150 according to the gray feature of the flame.
6. Thus, a flame binary image can be obtained.
7. Extracting the flame ratio and the flame brightness characteristics to preliminarily predict the carbon content index of the converter, and firstly calculating the flame ratio, wherein the specific process comprises the following steps of:
1) firstly, adding pixels with pixel value of 1 in the image, wherein the sum of the pixels is the fire in the imageArea of flame, denoted ScC is 1,2 … N, c is an image frame, ScRepresenting the flame area in the image of the c-th frame. Considering that the oxygen content in the converter and the age of the converter can cause certain influence on the flame in the converter, in order to obtain the accurate flame area, the flame area obtained based on the image is corrected based on the oxygen content in the converter and the age of the converter so as to estimate the accurate flame area in the converter and ensure the evaluation precision of the subsequent carbon content index. The flame area correction model specifically comprises the following steps:
in the formula, O0Average oxygen content in the whole image acquisition time, wherein O is real-time oxygen content; y is0The maximum furnace age of the converter can be obtained according to empirical statistics, and Y is the real furnace age of the converter when the flame image is detected.
2) And then constructing a flame ratio analysis model for calculating the flame ratio, wherein the flame ratio analysis model has the functions of:
wherein m and n are the size of the image, S'cFlame area, τ, estimated for the modelcThe ratio of flame in the selected c frame image is shown. Therefore, the flame ratio can be obtained for being used as characteristic data for judging the initial assessment of the converter carbon content index in the follow-up process.
8. For flame brightness acquisition: in order to facilitate a subsequent system to accurately obtain the brightness degree of flame, a flame binary image is used as an ROI (region of interest), multiplication operation is carried out on the ROI and an original RGB (red, green and blue) image to obtain an RGB image only containing flame, and finally the brightness information of the flame is calculated.
9. After obtaining the corresponding RGB flame images, the flame brightness analysis method specifically includes: firstly, normalizing the R, G and B values of the RGB image:
then, the maximum value of the normalized R ', G ', B ' is obtained, and the obtained maximum value is the brightness value of the flame image:
Cmax＝max(R′，G’，B’)
Vi，j＝Cmax
and finally, constructing a flame brightness analysis model according to the brightness information of the image:
where ρ iscRepresenting the brightness degree of the flame in the image of the c-th frame, wherein the larger the value of the model function is, the higher the brightness degree of the flame is.
Therefore, the brightness of the flame can be calculated, so that a subsequent system can analyze the carbon content index of the converter based on the characteristic data.
10. Along with the change of the blowing time period, the carbon content is continuously reduced when the final stage of blowing is reached, carbon in the steel furnace is exhausted, the chemical reaction tends to be stable, the flame is sparse, the flame is soft, the texture is fine, and therefore the carbon content index in the steel furnace is expressed by adopting the flame proportion and the brightness degree characteristic of the flame.
11. In order to quickly calculate the carbon content index in the converter, a prediction estimation model is firstly constructed according to the flame characteristics, so that the carbon content index of molten steel in the converter is preliminarily estimated. Setting alpha and beta as the flame ratio and the proportional relation between the brightness degree of the flame and the carbon content index, and setting the values as follows: α ═ 0.4 and β ═ 0.6, and the examples can be selected by the practitioner. The carbon content index estimation pre-model expression is as follows:
ωc＝exp(ατc+βpc)+A
in the formula, A is a model adjustable parameter which is selected by an implementer, so that a preliminary estimation model of the carbon content index can be obtained according to the method.
12. Considering that the temperature in the furnace can influence the prediction of the carbon content, the system establishes a carbon content index adjusting module, and aims to obtain an accurate carbon content index of molten steel and prevent the phenomena of carbon pulling low or high and the like caused by inaccurate carbon content estimation from influencing the quality of subsequent steel products. In the whole converter steelmaking process, the temperature requirement is strict, the temperature in the converter is required to be within a proper temperature range, and when the temperature of the converter is within the molten steel tapping temperature range, the detected carbon content index is accurate, so that the carbon content index estimation based on the flame image is not accurate enough; when the temperature in the furnace is too low, the carbon oxidation speed is slow, the flame shrinkage is early, the brightness is low, and the carbon content index estimated according to the flame image is lower than the actual value at the moment.
13. In order to solve the problems, a carbon content index adjustment model is constructed and used for accurately calculating the carbon content index of the converter under different conditions. The final carbon content index adjustment model is established based on analysis and comparison of the converter temperature and the tapping temperature, and a thermocouple temperature measuring device is arranged beside the converter to measure the temperature in the converter. Measuring the temperature T in the furnace through thermocouple temperature measuring equipment, then carrying out contrastive analysis on the temperature T and the tapping temperature of the molten steel, establishing a final carbon content index adjustment model according to the temperature difference, setting the tapping range of a certain steel type as [ T1, T2] according to different tapping temperature ranges of different steel types, analyzing the temperature in the furnace, and establishing a corresponding carbon content adjustment model based on the temperature of the converter so as to obtain an accurate carbon content index. The method for adjusting the final evaluation model of the specific carbon content comprises the following steps:
when the temperature of the converter is lower than the minimum value of the optimal temperature range, namely T is less than T1, the temperature in the converter is too low, so that the predicted value of the carbon content index is low, in order to accurately judge the end point of the converter according to the carbon content index subsequently, a carbon content index adjustment function is constructed to ensure the evaluation precision of the final carbon content index, and then the final evaluation model expression of the carbon content index is as follows:
ω′c＝ωcln(5ΔT′+e)
if the temperature in the converter is T1 which is not less than T2 and not more than T, the temperature in the converter for steelmaking is considered to be in a proper temperature range, the carbon content index detected based on the flame image is more accurate, but in order to reduce the prediction error caused by the flame image and ensure the accuracy of the subsequent end point judgment, the carbon content index is determined to be more accurate
As the optimal converter temperature, analyzing the converter temperature, constructing a carbon content index adjustment model, and calculating the converter carbon content index in the appropriate temperature range more accurately, so that the final evaluation model of the carbon content index in the temperature range is set as follows:
in the formula, r is a conversion factor, and an implementer can select the conversion factor according to the actual situation and set the conversion factor to be 0.5.
When the temperature in the converter is higher than the maximum value of the optimal temperature range, namely T is more than T2, the temperature in the converter is considered to be too high, and a function with a larger compensation degree is constructed and is used for further correcting the carbon content index of the converter to obtain an accurate carbon content index, so that the problems of terminal point judgment error and the like caused by inaccurate evaluation of the carbon content index are prevented. Obtaining the final carbon content index evaluation model expression as follows:
aiming at the final carbon content evaluation model established above, and comprising:
wherein, omega'cRepresenting the most accurate carbon content index calculation.
14. Therefore, the final carbon content index evaluation model can be used for accurately calculating the carbon content index of the converter molten steel, so that the prediction precision of the carbon content index is improved, the converting end point can be further accurately judged in the following process, and meanwhile, the follow-up operators can conveniently know the carbon content index of the converter steel product in real time, and the quality of the steel product is improved.
15. The carbon content index calculation is carried out on a series of collected image data, and a carbon content index sequence can be obtained: [ omega ]1，ω2…ωN]And analyzing and judging the converter steelmaking blowing end point according to the obtained carbon content index sequence.
16. The index of the carbon content at the blowing end point is different for different steel grades, and the index range of the carbon content when a certain steel product reaches the blowing end point is assumed to be [ omega 1, omega 2 ]. And calculating the carbon-containing index of the converter according to the final carbon content index evaluation model, and when the carbon-containing indexes obtained by multiple N frames are all in [ omega 1, omega 2], determining that the steelmaking of the converter reaches the blowing end point, and sending an instruction to an oxygen supply system of an oxygen lance of the converter by the system at the moment to stop blowing oxygen into the converter. And setting the carbon-containing index obtained by 5 continuous frames to be in the [ omega 1, omega 2] range and then setting the carbon-containing index of 10 continuous frames not to be greatly changed, and determining that the converting end point is reached.
17. Because the temperature of the converter molten steel during tapping is a key factor influencing the quality of steel products, in order to obtain high-quality steel products, when the converter reaches the end point, the temperature of the molten steel is analyzed, and different rules are set for analysis and control aiming at the temperature of the molten steel which does not meet the tapping temperature.
18. The temperature analysis model specifically comprises: firstly, judging the end point of a converter according to the carbon content index, analyzing the temperature of the molten steel in the converter when the end point of smelting is reached, comparing the temperature of the molten steel in the converter with the tapping temperature range of steel types, setting the tapping range of the steel types as [ T1, T2], if the system detects that the temperature of the converter is within the tapping temperature range, T is not less than T1 and is not more than T2, the tapping condition is met, the furnace body can be inclined, and the molten steel is poured into a ladle for the subsequent steel casting process. It is explained that the tapping temperature is different for different steel grades, and the operator sets the tapping temperature range according to the steel grade type.
19. When the converter steelmaking reaches the end point, the temperature of the converter does not meet the molten steel tapping temperature, and specific temperature analysis models are constructed to adjust the molten steel temperature aiming at the situation so as to ensure that the temperature of the molten steel during tapping can reach the standard tapping temperature range. The temperature analysis model comprises the following specific processes:
when T is less than T1, the temperature of molten steel in the converter is too low after the converter reaches the converting end point, if the molten steel is poured into a ladle, the pouring operation cannot be smoothly carried out, the quality of steel is influenced, and in serious cases, the steel cannot be poured, a molten steel temperature raising analysis model is established, the temperature of the molten steel in the converter is raised, and the temperature raising analysis model expression is as follows:
in the formula, k is an adjustable coefficient, and m is the mass of the temperature raising agent and the unit is kg. And the value of the model adjustable coefficient can be determined by an implementer according to the type of the selected temperature raising agent and is set to be 2 according to different values of the temperature raising agent k.
When T is more than T2, the temperature of the molten steel is too high, the molten steel is easy to absorb air, the age of the converter and the alloy yield are reduced, in order to reduce the temperature of the molten steel and prevent the burning accident caused by the too high temperature of the molten steel, a function model for reducing the temperature of the molten steel is constructed, and the function of the cooling model is specifically as follows:
M＝R(T-T2)
in the formula, M is the amount of coolant added in the converter in kg, R is an adjustment factor, and the value thereof can be selected by an implementer according to the actual situation, and R is set to 16.
20. This is done. The system can accurately and quickly predict the carbon content index in the converter converting process, judge the converting end point based on the acquired carbon content index sequence, and control the temperature of the molten steel according to the temperature analysis model so as to ensure that the tapping temperature can be met when the molten steel is tapped, and prevent the problems of steel product quality, safety accidents and the like caused by overhigh and overlow tapping temperature.
Claims (7)
1. A converter carbon content index assessment and temperature analysis method based on artificial intelligence is characterized by comprising the following steps: comprises the following steps:
acquiring a flame image of a converter mouth in real time through a CCD camera which is erected in front of a converter and is provided with high temperature and dustproof protection, and shooting flame pictures of the converter mouth in different converting periods on site by using a camera;
step two, processing the flame image of the furnace mouth, extracting the flame characteristics of the furnace mouth, constructing a flame area correction evaluation model, establishing a converter carbon content index preliminary estimation model according to the flame characteristics, and evaluating the converter carbon content index, wherein the flame characteristics comprise the flame ratio and the flame brightness;
establishing a carbon content index adjustment model as a final carbon content index evaluation model to estimate the carbon content index of the molten steel;
fourthly, a carbon content index sequence in the converting process can be obtained based on the final carbon content evaluation model, the corresponding relation between the carbon content index of the molten steel and the converting end point is analyzed according to the carbon content index sequence, the converting end point of the converter is judged, and when the converting end point is reached in the steelmaking process of the converter, an oxygen supply system of an oxygen lance of the converter stops blowing oxygen into the converter;
step five, presetting the tapping range of a certain steel type as [ T1, T2], if the system detects that the temperature of the molten steel at the end point of the converter is in the tapping temperature range, the tapping condition is met, and tilting the furnace body to pour the molten steel into a ladle for a subsequent steel casting process;
and step six, if the temperature of the molten steel of the converter does not meet the tapping temperature, constructing different temperature analysis models and adjusting the temperature of the molten steel.
2. The method for evaluating the carbon content index and analyzing the temperature of the converter based on the artificial intelligence of claim 1, wherein: in the first step, the camera acquisition frame rate is 20 frames per second, and the camera acquires working images of converter blowing every 5 seconds.
3. The method for evaluating the carbon content index and analyzing the temperature of the converter based on the artificial intelligence of claim 1, wherein: the second step comprises the following steps:
step 2.1, the specific process of calculating the flame ratio is as follows: firstly, adding pixels with the pixel value of 1 in the image, wherein the sum of the pixels is the flame area in the image and is recorded as ScC is 1,2 … N, c is an image frame, ScRepresenting the flame area in the image of the frame c, wherein the flame area correction model specifically comprises the following steps:
in the formula, O0Average oxygen content in the whole image acquisition time, wherein O is real-time oxygen content; y is0The maximum furnace age of the converter is obtained according to empirical statistics, and Y is the real furnace age of the converter when the flame image is detected;
step 2.2, constructing a flame ratio analysis model for calculating the flame ratio, wherein the flame ratio analysis model has the functions of:
wherein m and n are the size of the image, S'cFlame area, τ, estimated for the modelcThe ratio of flame in the selected c frame image is calculated; taking the flame binary image as an ROI (region of interest), multiplying the ROI with the original RGB image to obtain an RGB image only containing flame, finally calculating the brightness information of the flame to obtain the corresponding RGBAfter the flame image, the flame brightness analysis method specifically comprises the following steps: firstly, normalizing the R, G and B values of the RGB image:
then, the maximum value of the normalized R ', G ', B ' is obtained, and the obtained maximum value is the brightness value of the flame image:
Cmax＝max(R’,G’,B’)
Vi,j＝Cmax
step 2.2, establishing a flame brightness analysis model according to the brightness information of the image:
where ρ iscRepresenting the brightness degree of the flame in the image of the c-th frame, wherein the larger the value of the model function is, the higher the brightness degree of the flame is.
4. The method for evaluating the carbon content index and analyzing the temperature of the converter based on the artificial intelligence of claim 1, wherein: setting alpha and beta as the flame ratio and the proportional relation between the brightness degree and the carbon content index of the flame in the third step, and taking the values as follows: α ═ 0.4, β ═ 0.6; the carbon content index estimation pre-model expression is as follows:
ωc＝exp(ατc+βρc)+A
in the formula, A is a model adjustable parameter, a carbon content index adjustment model is established based on analysis and comparison of converter temperature and tapping temperature, the tapping range of steel grades is set as [ T1, T2], the temperature in the converter is analyzed, a corresponding carbon content adjustment model is established based on the converter temperature, and an accurate carbon content index is obtained; when the temperature of the converter is lower than the minimum value of the optimal temperature range, namely T < T1, the temperature in the converter is too low, so that the predicted value of the carbon content index is low, a carbon content index adjustment function is constructed to ensure the evaluation accuracy of the final carbon content index, and the final evaluation model expression of the carbon content index is as follows:
ω'c＝ωcln(5ΔT'+e)
if the temperature in the converter is T2-T1, the temperature will be
Analyzing the converter temperature as the optimal converter temperature, and constructing a carbon content index adjustment model, wherein the final evaluation model of the carbon content index in the temperature range is as follows:
in the formula, r is a conversion factor and is set to be 0.5; when the temperature in the converter is higher than the maximum value of the optimal temperature range, namely T is greater than T2, the temperature in the converter is considered to be overhigh, and the final carbon content index evaluation model expression is as follows:
aiming at the final carbon content evaluation model established above, and comprising:
wherein, omega'cRepresenting the most accurate carbon content index calculation.
5. The method for evaluating the carbon content index and analyzing the temperature of the converter based on the artificial intelligence of claim 1, wherein: in the fourth step, the carbon content index calculation is carried out on a series of acquired image data to obtain a carbon content index sequence: [ omega ]1,ω2…ωN]Analyzing and judging the converter steelmaking blowing end point according to the obtained carbon content index sequence; assuming that the index range of carbon content when a certain steel product reaches the blowing end point is [ omega 1, omega 2]](ii) a Calculating the carbon-containing index of the converter according to the final carbon content index evaluation model, wherein when the carbon-containing indexes obtained from multiple continuous N frames are all in [ omega 1, omega 2]]When the converter steelmaking reaches the blowing end point, the system sends an instruction to an oxygen supply system of an oxygen lance of the converter to stop blowing oxygen into the converter, and carbon-containing indexes obtained by 5 continuous frames are set to be [ omega 1, omega 2]]If the carbon content index does not change greatly within the range and 10 continuous frames later, the end point of the converting is considered to be reached.
6. The method for evaluating the carbon content index and analyzing the temperature of the converter based on the artificial intelligence of claim 1, wherein: the temperature analysis model meeting the tapping condition in the fifth step is as follows: firstly, judging the end point of a converter according to the carbon content index, analyzing the temperature of the molten steel in the converter when the end point of smelting is reached, comparing the temperature of the molten steel in the converter with the tapping temperature range of steel types, setting the tapping range of the steel types as [ T1, T2], if the system detects that the temperature of the converter is within the tapping temperature range, T2 is not more than T1, the tapping condition is met, the converter body can be inclined, and the molten steel is poured into a ladle for the subsequent steel casting process.
7. The method for evaluating the carbon content index and analyzing the temperature of the converter based on the artificial intelligence of claim 1, wherein: the specific process of the temperature analysis model which does not meet the tapping condition in the sixth step is as follows:
when T < T1, the temperature of molten steel in the converter is too low after the converter reaches the converting end point, if the molten steel is poured into a ladle, the pouring operation cannot be smoothly carried out, the quality of steel is affected, and in severe cases, the steel cannot be poured, a molten steel temperature raising analysis model is established, the temperature of the molten steel in the converter is raised, and the temperature raising analysis model expression is as follows:
in the formula, k is an adjustable coefficient, m is the mass of the temperature raising agent, the unit is kg, the values of k are different for different temperature raising agents, and an implementer can determine the value of the adjustable coefficient of the model according to the type of the selected temperature raising agent and set the value to be 2;
when T is greater than T2, the molten steel temperature is too high, the molten steel is easy to absorb air, the age of the converter and the alloy yield are reduced, in order to reduce the molten steel temperature and prevent the burning accident caused by the too high molten steel temperature, a function model for reducing the molten steel temperature is constructed, and the function of the cooling model specifically comprises the following steps:
M＝R(T-T2)
wherein M is the adding amount of the coolant in the converter and is kg, R is an adjusting factor, and R is set to be 16.
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