A hot rolling temperature control method for improving the uniformity of the structure of oriented silicon steel sheets

Abstract

The present application relates to the technical field of hot rolling temperature control, and specifically to a hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel plate. The method comprises: by setting each time series comparison interval, constructing the change response value of various rolling parameters in the interval based on the change of various rolling parameters in the interval; constructing the state parameter of the rolling temperature offset in each interval based on the similarity of all the change response values ​​in different intervals and the difference in the change trend of temperature data; constructing the offset characteristic value based on the temperature difference in different intervals; adjusting the feedback error based on the offset characteristic value of the monitoring control point; controlling the hot rolling temperature in combination with the feedback control algorithm, avoiding the problem of large error caused by the feedback error due to the influence of the offset characteristic, and effectively adjusting the feedback control of the hot rolling temperature, thereby improving the uniformity of the structure of the oriented silicon steel plate during the hot rolling process.

Description

Technical Field

[0001] The present application relates to the technical field of hot rolling temperature control, and in particular to a hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet. Background Art

[0002] As a key material for the manufacture of iron cores of power equipment such as transformers, the consistency of the magnetic properties of oriented silicon steel sheets plays a vital role in the operating efficiency and stability of the equipment. The uniformity of the structure is directly related to the key magnetic indicators of the silicon steel sheet, such as magnetic permeability and iron loss. Once the structure is uneven, the magnetic properties of different areas of the silicon steel sheet will be different, which will cause uneven distribution of local magnetic flux density during the operation of the transformer. In view of this, improving the uniformity of the structure of oriented silicon steel sheets has become one of the core links in the silicon steel sheet preparation process.

[0003] In the preparation process of oriented silicon steel sheets, the control of hot rolling temperature is a key factor affecting its structural uniformity. The hot rolling process covers the heating link before rolling, temperature regulation during rolling, and temperature control during final rolling. Among them, during the rolling process, the unstable changes in rolling temperature may be caused by the advancement of different rolling sequences. Traditional control methods mainly rely on feedforward control and feedback control to realize the temperature control analysis of the hot rolling process. However, when using traditional control methods to control the hot rolling process, the large deviation of the rolling temperature feedback response caused by the changes in parameters such as rolling speed and tension in the actual rolling process is not fully considered. This deviation then causes the hot rolling temperature to shift in the temperature analysis at different stages, affecting the uniformity of rolling temperature control in different regions during the rolling process of oriented silicon steel sheets using pre-penetration water control technology, and ultimately there is the disadvantage of regional structural unevenness of silicon steel sheets. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a hot rolling temperature control method for improving the uniformity of the structure of oriented silicon steel sheets to solve the existing problems.

[0005] A hot rolling temperature control method for improving the uniformity of the structure of oriented silicon steel sheets in the present application adopts the following technical solution:

[0006] An embodiment of the present application provides a hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet, the method comprising the following steps:

[0007] Collect various rolling parameter data during the hot rolling process of oriented silicon steel sheets, including temperature data, speed data and tension data;

[0008] The data collection time is divided into time intervals by preset time intervals, and each progressive interval is constructed based on each time interval, which is recorded as each time series comparison interval; based on the change of various rolling parameter data in each time series comparison interval, the change response value of various rolling parameter data in each time series comparison interval is constructed;

[0009] Calculate the first similarity between different time series comparison intervals based on the similarity between all the change response values ​​in different time series comparison intervals; calculate the first difference between different time series comparison intervals based on the difference between the temperature data change trends in different time series comparison intervals;

[0010] Based on the change response value of the temperature data in each time series comparison interval, combined with the first similarity and the first difference, construct the state parameter of the rolling temperature offset in each time series comparison interval, and combine the difference between the numerical ranges of the temperature data in different time series comparison intervals to construct the offset characteristic value of the hot rolling temperature response change at the current time point;

[0011] The preset time point is used as the monitoring control point; based on the offset characteristic value of the monitoring control point, combined with the difference between the temperature data of the monitoring control point and the ideal temperature value, the feedback error of the monitoring control point is constructed; and the hot rolling temperature is controlled in combination with the feedback control algorithm.

[0012] In one embodiment, the progressive intervals are constructed in such a manner that a time period from the first time interval to the ith time interval is taken as the ith progressive interval.

[0013] In one embodiment, the process of obtaining the change response values ​​of various rolling parameter data in each time series comparison interval is as follows:

[0014] A first-order difference sequence of a sequence composed of each rolling parameter data in each time series comparison interval is obtained, and the mean of all elements in the first-order difference sequence is used as a change response value of each rolling parameter data in each time series comparison interval.

[0015] In one embodiment, the process of obtaining the first similarity between the different time series comparison intervals is as follows:

[0016] Constructing effective contrast feature vectors for each time series contrast interval based on the change response value;

[0017] The similarity between any time series comparison interval and the effective comparison feature vectors of other time series comparison intervals is calculated by a similarity algorithm and recorded as the first similarity.

[0018] In one embodiment, the effective comparison feature vector is a vector composed of the change response values ​​of all types of rolling parameter data in each time series comparison interval.

[0019] In one embodiment, the process of obtaining the first difference between different time series comparison intervals is as follows:

[0020] All temperature data in each time series comparison interval are used as input of the trend detection algorithm, and the output is the trend statistics of all temperature data in each time series comparison interval; the difference between the trend statistics of any time series comparison interval and other time series comparison intervals is calculated and recorded as the first difference.

[0021] In one embodiment, the expression of the state parameter of the rolling temperature deviation in each time series comparison interval is:

[0022]

[0023] In the formula, Indicates The state parameters of rolling temperature deviation within a time series comparison interval; Indicates The response value of the temperature data change within a time series comparison interval; Indicates and The first difference between the time series comparison intervals; Indicates and The first similarity between the time series comparison intervals; represents an exponential function with a natural constant as base; Indicates the number of time series comparison intervals.

[0024] In one embodiment, the expression of the offset characteristic value of the hot rolling temperature response change at the current time point is:

[0025]

[0026] In the formula, The offset characteristic value representing the hot rolling temperature response change at the current time point; and Respectively represent and The range of all temperature data within a time series comparison interval; Indicates The state parameters of rolling temperature deviation within a time series comparison interval; Indicates the number of time series comparison intervals.

[0027] In one embodiment, the process of obtaining the feedback error of the monitoring control point is:

[0028] The difference between the temperature data of each monitoring control point and the ideal temperature value of hot rolling of silicon steel plate is calculated and recorded as the first difference; the feedback error of the monitoring control point is positively correlated with the first difference and negatively correlated with the offset characteristic value of the hot rolling temperature response change of the monitoring control point.

[0029] In one embodiment, the hot rolling temperature is controlled by combining a feedback control algorithm, specifically:

[0030] The feedback error of the monitoring control point is used as the feedback error in the PI feedback control loop in the PI feedback control algorithm, and the hot rolling temperature is adjusted according to the PI feedback control algorithm.

[0031] This application has at least the following beneficial effects:

[0032] This application aims at the problem of the influence of the offset characteristics of hot rolling temperature control in the actual silicon steel plate rolling production practice, and proposes a hot rolling temperature control method for improving the uniformity of the structure of oriented silicon steel plates;

[0033] (1) Considering that the actual rolling conditions in different time periods may be different during the actual rolling process of silicon steel sheets, the rolling parameter data changes in different time periods are analyzed. By analyzing the time series change characteristics of different rolling parameters in the rolling process of silicon steel sheets, the time intervals of the time series intervals of temperature changes in the hot rolling temperature control process are comprehensively determined. The beneficial effect is that the parameters in the time interval with temperature offset characteristics are effectively compared in time series changes;

[0034] (2) Based on the change response value of the temperature data in each time series comparison interval, combined with the first similarity and the first difference, the state parameter of the rolling temperature offset in each time series comparison interval is constructed, and combined with the difference between the numerical ranges of the temperature data in different time series comparison intervals, the offset characteristic value of the hot rolling temperature response change at the current time point is constructed, which has the beneficial effect of accurately reflecting the temperature offset during the hot rolling temperature control process of the silicon steel plate, and then accurately adjusting the feedback error of the hot rolling temperature;

[0035] (3) Considering that the temperature offset during the hot rolling process of silicon steel sheets does not occur instantaneously, a monitoring control point is set during the hot rolling temperature control process. The feedback error is adjusted according to the offset characteristic value of the hot rolling temperature response change at the monitoring control point. The beneficial effect is that the feedback error is avoided from being too large due to the influence of the offset characteristic, and the feedback control of the hot rolling temperature is effectively adjusted, thereby improving the uniformity of the microstructure of the oriented silicon steel sheets during the hot rolling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 A flow chart of a hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet provided in this application;

[0038] Figure 2 Schematic diagram of the process of obtaining the first similarity. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the hot rolling temperature control method for improving the uniformity of the structure of oriented silicon steel sheets proposed in the present application, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0041] A specific scheme of a hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet provided by the present application is described in detail below with reference to the accompanying drawings.

[0042] An embodiment of the present application provides a hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet.

[0043] Specifically, a hot rolling temperature control method for improving the uniformity of the structure of oriented silicon steel sheets is provided as follows. Figure 1 , the method comprises the following steps:

[0044] Step S1, collecting various rolling parameter data during the hot rolling process of the oriented silicon steel sheet, including temperature data, rotation speed data and tension data.

[0045] The preparation process of oriented silicon steel sheets covers multiple links including smelting, continuous casting, hot rolling, normalizing, pickling, cold rolling, annealing and coating treatment. Among them, the hot rolling process plays a key role. The hot rolling unit is responsible for the preliminary rolling of the silicon steel slab after continuous casting to achieve the adjustment and optimization of the thickness and width of the slab. The hot rolling unit is mainly composed of a rough rolling mill and a finishing mill. The oriented silicon steel sheets undergo different rolling sequences during the hot rolling process. By accurately controlling various parameters during the rolling process, silicon steel sheet products with good uniformity of organization and superior performance are obtained.

[0046] In the hot rolling process of silicon steel sheet, the silicon steel slab to be rolled is first heated to the set rolling temperature by a heating furnace. During the rolling process, the rolling temperature of the silicon steel sheet is collected by a sensor, and the rolling temperature is analyzed and controlled. Specifically, the rolling parameter data of the silicon steel slab rolling process, including temperature data, speed data and tension data, are collected by thermocouples, speed sensors and tension sensors.

[0047] During the rolling process, it is necessary to ensure that the temperature of the silicon steel slab in different areas is heated evenly and the operating parameters are stable, thereby ensuring the uniformity of the silicon steel sheet structure. However, in the actual production process, due to the complex engineering production environment, the collected rolling parameter data is easily interfered by environmental noise. Therefore, a median filter is used to perform noise reduction processing on each collected rolling parameter data. Among them, the noise reduction processing process of the median filter is a well-known technology, and the specific process will not be repeated.

[0048] It should be noted that for the denoising processing of rolling parameter data, this application only provides a denoising method. There are many existing denoising methods, and implementers can also use other denoising algorithms to perform denoising processing of rolling parameter data. This application does not make specific restrictions.

[0049] Step S2, divide the data collection time into time intervals by preset time intervals, construct each progressive interval based on each time interval, and record it as each time series comparison interval; construct the change response value of various rolling parameter data in each time series comparison interval based on the change of various rolling parameter data in each time series comparison interval.

[0050] In the production process of silicon steel sheets, each process in the production process of silicon steel sheets needs to be strictly controlled. The change of rolling temperature during the hot rolling of silicon steel slabs will affect the deformation resistance and the flow behavior of the metal, and thus affect the uniformity of the silicon steel slab structure; the temperature deviation during hot rolling may lead to excessive grain growth due to excessive temperature, and the temperature deviation may lead to excessive temperature, which will increase the deformation resistance, resulting in over-hard processing and uneven structure. It can be seen that the effective control of hot rolling temperature during the production of silicon steel sheets is the key to improving the uniformity of the structure of oriented silicon steel sheets.

[0051] In the actual production practice of silicon steel sheet rolling, due to the dynamic nature of the production environment and the complexity of the rolling process, the changes in relevant parameters show significant differences when rolling in different time periods. This difference causes the speed and tension response of the rolling mill to shift to varying degrees during the actual production process of silicon steel sheet rolling, and accordingly, the rolling temperature response during the rolling process will also change. This can lead to the possible shift of the temperature feedback error at different stages of the rolling process, which seriously affects the accurate and effective judgment of the temperature feedback error during the rolling process.

[0052] Therefore, according to various rolling parameter data, the temperature change characteristics during the rolling process are accurately controlled and analyzed. Considering that the actual rolling conditions in different time periods may be different during the actual rolling process of silicon steel plates, the changes in rolling parameter data in different time periods are analyzed, and then the parameter deviation characteristics that appear during the rolling process with the passage of actual production time are effectively judged, specifically:

[0053] (1) Segment the sequence composed of all the same rolling parameter data to obtain subsequences; obtain the time length corresponding to each subsequence, which is recorded as the time characteristic parameter:

[0054] For each type of rolling parameter data collected during the rolling process of silicon steel plates, the sequence composed of the rolling parameter data is taken as input, and the BG (Bottom-Up Grooming) sequence segmentation algorithm is used to obtain the segmentation result of the rolling parameter data in the time series; for each subsequence in the segmentation result, the difference between the maximum and minimum values ​​of the collection time of all elements in the subsequence is used as the time characteristic parameter of the subsequence in the rolling characteristic analysis process of silicon steel plates.

[0055] It should be noted that, for the segmentation of the sequence composed of rolling parameter data, the present application only provides a sequence segmentation method. There are many existing sequence segmentation methods, and the implementer may also use other sequence segmentation algorithms to segment the sequence composed of rolling parameter data. The present application does not make any specific restrictions.

[0056] The purpose of calculating the time characteristic parameters is to determine the accurate time interval for rolling parameter deviation characteristic analysis through the variation characteristics of each rolling parameter data at different time intervals during the rolling process, and then effectively judge the temperature deviation of the rolling process.

[0057] (2) Furthermore, in order to effectively analyze the comparative characteristics of the rolling parameters during the rolling process of the silicon steel sheet, the time interval for time interval division is calculated based on the time characteristic parameters of each subsequence, which is recorded as the first time interval; the data acquisition time is divided by the first time interval to obtain each time interval, and each progressive interval is constructed based on each time interval, which is recorded as each time series comparison interval:

[0058] Calculate the mean of the time characteristic parameters of all subsequences of each rolling parameter data, and use the maximum value of all the mean values ​​of all rolling parameter data as the time interval for dividing the time interval of silicon steel plate hot rolling temperature control analysis , recorded as the first time interval; the data collection time is divided by the first time interval to obtain each time interval. The smaller it is, the more frequent the rolling parameter change characteristics of the silicon steel slab during the silicon steel plate rolling process are, and the more significant the impact of the temperature control deviation of the silicon steel slab may be.

[0059] Furthermore, the time period from the first time interval to the i-th time interval is taken as the i-th progressive interval, and each progressive interval is recorded as each time series comparison interval. The purpose of setting the time series comparison interval is to compare the effective time series changes of the parameters in the time interval with temperature offset characteristics. Specifically, as an example of division, for example, the time length collected up to the current time is = , then the time series comparison intervals of the progressive interval division are , , ; Among them, if the duration is not The last time series comparison interval is .

[0060] (3) Based on the changes of various rolling parameter data in each time series comparison interval, the change response value of various rolling parameter data in each time series comparison interval is constructed:

[0061] For each divided time series comparison interval, the first-order difference sequence of the sequence composed of each rolling parameter data in the time series comparison interval is obtained, and the mean of all elements in the first-order difference sequence is used as the change response value of each rolling parameter in the hot rolling temperature control process in the time series comparison interval. The larger the change response value, the more significant the change characteristics in the divided time series comparison interval.

[0062] Step S3, calculating the first similarity between different time series comparison intervals based on the similarity between all the change response values ​​in different time series comparison intervals; calculating the first difference between different time series comparison intervals based on the difference between the temperature data change trends in different time series comparison intervals.

[0063] (1) Furthermore, for each divided time series comparison interval, the vector composed of the change response values ​​of all types of rolling parameter data in the time series comparison interval is used as the effective comparison feature vector of the hot rolling temperature control stage. The effective comparison feature vector can be used to comprehensively compare the characteristic differences of parameter changes in the time series comparison intervals with different characteristics during the rolling process. Specifically, for each time series comparison interval, the cosine similarity between the effective comparison feature vectors of the time series comparison interval and other time series comparison intervals is calculated and recorded as the first similarity.

[0064] It should be noted that for the similarity between the effective comparison feature vectors of different time series comparison intervals, this application only provides a similarity calculation method. There are many existing similarity calculation methods. Implementers can also use other similarity algorithms to calculate the similarity between the effective comparison feature vectors of different time series comparison intervals. This application does not make specific restrictions.

[0065] The smaller the cosine similarity is, the more significant the shift feature in the current time series comparison interval is, compared with other time series comparison intervals, after the time series feature division based on all rolling parameters.

[0066] (2) Further, the temperature data changes in different time series comparison intervals during the silicon steel sheet rolling process are compared. Specifically, for each time series comparison interval divided during the silicon steel sheet rolling process, all temperature data within the time series comparison interval are used as input of the Mannkendall trend detection algorithm, and the output is the trend statistics of all temperature data within the time series comparison interval; the difference between the trend statistics of the time series comparison interval and other time series comparison intervals is calculated and recorded as the first difference.

[0067] It should be noted that for the calculation of trend statistics of all temperature data within the time series comparison interval, this application only provides a trend detection method. There are many existing trend detection methods, and implementers can also use other trend detection algorithms to calculate the trend statistics of all temperature data within the time series comparison interval. This application does not make specific restrictions.

[0068] Preferably, in one embodiment of the present application, the first difference may be an absolute value of a difference between trend statistics of the time series comparison interval and other time series comparison intervals;

[0069] In other embodiments of the present application, the first difference may be the square of the difference between the trend statistics of the time series comparison interval and other time series comparison intervals.

[0070] The larger the first difference is, the larger the difference in temperature trend response in the different divided time intervals is, that is, the more significant the offset characteristics of different time stages in the hot rolling temperature control process are.

[0071] Step S4, based on the change response value of the temperature data in each time series comparison interval, combined with the first similarity and the first difference, construct the state parameters of the rolling temperature offset in each time series comparison interval, and combine the difference between the numerical ranges of the temperature data in different time series comparison intervals to construct the offset characteristic value of the hot rolling temperature response change at the current time point.

[0072] (1) Through the above comparative analysis of different parameters in each time series comparison interval and the analysis of temperature change differences in different time series comparison intervals, the state parameters of rolling temperature deviation at different stages during the hot rolling temperature control process of silicon steel sheets are calculated. The specific calculation relationship is:

[0073]

[0074] In the formula, Indicates The state parameters of rolling temperature deviation within a time series comparison interval; Indicates The response value of the temperature data in the hot rolling temperature control process within a time series comparison interval; Indicates and The first difference between the time series comparison intervals; Indicates and The first similarity between the time series comparison intervals; represents an exponential function with a natural constant as base; Indicates the number of time series comparison intervals.

[0075] The larger the state parameter of the rolling temperature offset within the calculated time series comparison interval, the more significant the offset change characteristics of the current time period are through the comparative analysis of the comprehensive response changes at different stages in the hot rolling temperature control process, and the more serious the impact of the offset of the rolling parameter changes on the hot rolling control process.

[0076] (2) Based on the above comparative analysis of the temperature offset characteristics at different stages, the state parameters of the rolling temperature offset in each time series comparison interval are obtained; the time interval division is based on the time series interval division of the progressive interval based on the change characteristics of different rolling parameters in the hot rolling process of the silicon steel plate, and the offset characteristics of the hot rolling control process are comprehensively analyzed to calculate the offset characteristic value of the hot rolling temperature response change at the current time point in the hot rolling temperature control process. , the specific calculation relationship is as follows:

[0077]

[0078] In the formula, The offset characteristic value representing the hot rolling temperature response change at the current time point during the hot rolling temperature control process; and Respectively represent and The range of all temperature data within a time series comparison interval; Indicates The state parameters of rolling temperature deviation within a time series comparison interval; Indicates the number of time series comparison intervals.

[0079] The larger the calculated offset characteristic value of the hot rolling temperature response change, the greater the possibility that the feedback temperature of the silicon steel plate will have an offset characteristic when it runs to the current stage. It should be noted that after the progressive time series comparison interval division based on the determined time interval, if the current time series comparison interval has a significant difference in the temperature deviation range compared with other time series comparison intervals, the greater the confidence of the offset characteristic impact analysis in the current time series interval.

[0080] Step S5, taking the preset time point as the monitoring control point; constructing the feedback error of the monitoring control point based on the offset characteristic value of the monitoring control point and the difference between the temperature data of the monitoring control point and the ideal temperature value; and controlling the hot rolling temperature in combination with the feedback control algorithm.

[0081] Through the above analysis, the relative characteristics of the temperature response offset in different time periods during the hot rolling process of silicon steel plates can be obtained. The focus is on the deviation of the rolling parameter changes in the actual hot rolling control process. The current actual collected temperature will also be offset. Therefore, combined with the rolling parameters in the hot rolling temperature control process up to the current moment, a comparative analysis of the timing offset characteristics is performed, and then the judgment of the feedback error is adjusted in time to reduce the impact of temperature offset on temperature stability control during the control process.

[0082] Furthermore, considering that the temperature deviation of silicon steel sheets during hot rolling does not occur instantaneously, the monitoring control points are set during the hot rolling temperature control process. The specific setting method is: if the time of silicon steel sheets in each hot rolling sequence is , then the time corresponding to the monitoring control points is ,in ,in represents a rounding function; s is the number of monitoring control points, and preferably, in the embodiment of the present application, the value of s is set to 20. As other embodiments of the present application, the implementer can set the value of s according to the actual situation.

[0083] The temperature deviation during the hot rolling process is analyzed at each monitoring control point to obtain the corresponding deviation characteristic value of the hot rolling temperature response change.

[0084] Furthermore, the feedback error is adjusted according to the offset characteristic value of the hot rolling temperature response change. The specific adjustment process is: calculate the difference between the temperature data of each monitoring control point at the corresponding collection time and the set ideal temperature value of the hot rolling of the silicon steel plate, which is recorded as the first difference; the feedback error of each monitoring control point is positively correlated with the first difference, and negatively correlated with the offset characteristic value of the hot rolling temperature response change of the monitoring control point.

[0085] It can be understood that the positive correlation and negative correlation in this application refer to the relationship between the independent variable and the dependent variable. The positive correlation means that the independent variable increases (decreases) as the dependent variable increases (decreases); the negative correlation means that the independent variable decreases (increases) as the dependent variable increases (decreases); the specific positive correlation and negative correlation can be determined according to actual conditions during the application process, and this application does not impose any special restrictions.

[0086] Preferably, in one embodiment of the present application, the expression of the feedback error of each monitoring control point can be: , where Indicates The feedback error of each monitoring control point is Indicates The temperature data of each monitoring control point at the time of collection is The ideal temperature value for hot rolling of silicon steel sheets. Indicates Normalized results of the offset characteristic values ​​of the hot rolling temperature response changes at the monitoring control points.

[0087] In other embodiments of the present application, the expression of the feedback error of each monitoring control point may be: , where For the The normalized value of the inverse of the offset characteristic value of the hot rolling temperature response change of the monitoring control point.

[0088] In the The more significant the offset characteristics of a monitoring control point are, the greater the feedback error caused by the offset characteristics will be. The feedback error can be reduced and the feedback control of the hot rolling temperature can be effectively adjusted.

[0089] Furthermore, the feedback error of the monitoring control point is used as the feedback error in the PI feedback control loop in the PI feedback control algorithm, and the hot rolling temperature is adjusted according to the PI feedback control algorithm, and the amount of pre-watering is adjusted according to the adjusted temperature, that is, the amount of pre-watering is adjusted according to the actual temperature to be adjusted, thereby reducing the influence of the temporal change of temperature offset on the temperature control stability during the rolling of silicon steel sheets. The PI feedback control algorithm and the pre-watering control technology are both well-known technologies, and the specific process will not be repeated.

[0090] It should be noted that for the control of hot rolling temperature, this application only provides a feedback control method. There are many existing feedback control methods, and implementers can also use other feedback control algorithms to control the hot rolling temperature. This application does not make specific restrictions.

[0091] The schematic diagram of the process of obtaining the first similarity is as follows Figure 2 shown.

[0092] In summary, the embodiment of the present application comprehensively determines the time intervals of the time series intervals of the temperature change during the hot rolling temperature control process by analyzing the time series change characteristics of different rolling parameters during the rolling process of the silicon steel sheet. The beneficial effect is that the parameters in the time interval with the temperature offset characteristics are compared for effective time series changes.

[0093] Based on the change response value of the temperature data in each time series comparison interval, combined with the first similarity and the first difference, the state parameter of the rolling temperature offset in each time series comparison interval is constructed, and combined with the difference between the numerical ranges of the temperature data in different time series comparison intervals, the offset characteristic value of the hot rolling temperature response change at the current time point is constructed, which has the beneficial effect of accurately reflecting the temperature offset during the hot rolling temperature control process of the silicon steel plate, and then accurately adjusting the feedback error of the hot rolling temperature;

[0094] Considering that the temperature offset during the hot rolling process of silicon steel plates does not occur instantaneously, a monitoring control point is set during the hot rolling temperature control process. The feedback error is adjusted according to the offset characteristic value of the hot rolling temperature response change when the monitoring control point is set. The beneficial effect is that the feedback error is avoided due to the influence of the offset characteristics. The feedback control of the hot rolling temperature is effectively adjusted, thereby improving the uniformity of the structure of the oriented silicon steel plate during the hot rolling process.

[0095] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0097] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A hot rolling temperature control method for improving the uniformity of the structure of oriented silicon steel sheets, characterized in that: The method comprises the following steps: Collect various rolling parameter data during the hot rolling process of oriented silicon steel sheets, including temperature data, speed data and tension data; The data collection time is divided into time intervals by preset time intervals, and each progressive interval is constructed based on each time interval, which is recorded as each time series comparison interval; based on the change of various rolling parameter data in each time series comparison interval, the change response value of various rolling parameter data in each time series comparison interval is constructed; Calculate the first similarity between different time series comparison intervals based on the similarity between all the change response values ​​in different time series comparison intervals; calculate the first difference between different time series comparison intervals based on the difference between the temperature data change trends in different time series comparison intervals; Based on the change response value of the temperature data in each time series comparison interval, combined with the first similarity and the first difference, construct the state parameter of the rolling temperature offset in each time series comparison interval, and combine the difference between the numerical ranges of the temperature data in different time series comparison intervals to construct the offset characteristic value of the hot rolling temperature response change at the current time point; The preset time point is used as the monitoring control point; based on the offset characteristic value of the monitoring control point, combined with the difference between the temperature data of the monitoring control point and the ideal temperature value, the feedback error of the monitoring control point is constructed; and the hot rolling temperature is controlled in combination with the feedback control algorithm; The expression of the state parameter of rolling temperature deviation in each time series comparison interval is: In the formula, Indicates The state parameters of rolling temperature deviation within a time series comparison interval; Indicates The response value of the temperature data change within a time series comparison interval; Indicates and The first difference between the time series comparison intervals; Indicates and The first similarity between the time series comparison intervals; represents an exponential function with a natural constant as base; Indicates the number of time series comparison intervals.

2. A hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet as claimed in claim 1, characterized in that: The construction method of each progressive interval is: the time period from the first time interval to the i-th time interval is taken as the i-th progressive interval.

3. A hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet as claimed in claim 1, characterized in that: The process of obtaining the change response values ​​of various rolling parameter data within each time series comparison interval is as follows: A first-order difference sequence of a sequence composed of each rolling parameter data in each time series comparison interval is obtained, and the mean of all elements in the first-order difference sequence is used as a change response value of each rolling parameter data in each time series comparison interval.

4. A hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet as claimed in claim 1, characterized in that: The process of obtaining the first similarity between the different time series comparison intervals is as follows: Constructing effective contrast feature vectors for each time series contrast interval based on the change response value; The similarity between any time series comparison interval and the effective comparison feature vectors of other time series comparison intervals is calculated by a similarity algorithm and recorded as the first similarity.

5. A hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet as claimed in claim 4, characterized in that: The effective comparison feature vector is a vector composed of the change response values ​​of all kinds of rolling parameter data in each time series comparison interval.

6. A hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet as claimed in claim 1, characterized in that: The process of obtaining the first difference between the different time series comparison intervals is as follows: All temperature data in each time series comparison interval are used as input of the trend detection algorithm, and the output is the trend statistics of all temperature data in each time series comparison interval; the difference between the trend statistics of any time series comparison interval and other time series comparison intervals is calculated and recorded as the first difference.

7. A hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet as claimed in claim 1, characterized in that: The expression of the offset characteristic value of the hot rolling temperature response change at the current time point is: In the formula, The offset characteristic value representing the hot rolling temperature response change at the current time point; and Respectively represent and The range of all temperature data within a time series comparison interval; Indicates The state parameters of rolling temperature deviation within a time series comparison interval; Indicates the number of time series comparison intervals.

8. A hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet as claimed in claim 1, characterized in that: The acquisition process of the feedback error of the monitoring control point is: The difference between the temperature data of each monitoring control point and the ideal temperature value of hot rolling of silicon steel plate is calculated and recorded as the first difference; the feedback error of the monitoring control point is positively correlated with the first difference and negatively correlated with the offset characteristic value of the hot rolling temperature response change of the monitoring control point.

9. A hot rolling temperature control method for improving the uniformity of the structure of an oriented silicon steel sheet as claimed in claim 1, characterized in that: The hot rolling temperature is controlled by combining the feedback control algorithm, specifically: The feedback error of the monitoring control point is used as the feedback error in the PI feedback control loop in the PI feedback control algorithm, and the hot rolling temperature is adjusted according to the PI feedback control algorithm.

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