CN113621791A - Method for improving heating furnace billet temperature tracking model calculation accuracy based on black box test transverse partition data - Google Patents
Method for improving heating furnace billet temperature tracking model calculation accuracy based on black box test transverse partition data Download PDFInfo
- Publication number
- CN113621791A CN113621791A CN202111005304.3A CN202111005304A CN113621791A CN 113621791 A CN113621791 A CN 113621791A CN 202111005304 A CN202111005304 A CN 202111005304A CN 113621791 A CN113621791 A CN 113621791A
- Authority
- CN
- China
- Prior art keywords
- billet
- temperature
- test
- furnace
- steel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 87
- 238000010438 heat treatment Methods 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000005192 partition Methods 0.000 title claims abstract description 10
- 238000012821 model calculation Methods 0.000 title description 4
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 121
- 239000010959 steel Substances 0.000 claims abstract description 121
- 238000004364 calculation method Methods 0.000 claims abstract description 35
- 230000008859 change Effects 0.000 claims abstract description 14
- 230000004907 flux Effects 0.000 claims abstract description 13
- 230000008569 process Effects 0.000 claims abstract description 7
- 238000007599 discharging Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000002791 soaking Methods 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 238000012937 correction Methods 0.000 description 8
- 230000005855 radiation Effects 0.000 description 8
- 238000004519 manufacturing process Methods 0.000 description 7
- 239000002436 steel type Substances 0.000 description 6
- 238000009826 distribution Methods 0.000 description 5
- 238000002474 experimental method Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000007418 data mining Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000002485 combustion reaction Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000010079 rubber tapping Methods 0.000 description 2
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000013072 incoming material Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
Images
Classifications
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D11/00—Process control or regulation for heat treatments
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D9/00—Heat treatment, e.g. annealing, hardening, quenching or tempering, adapted for particular articles; Furnaces therefor
- C21D9/70—Furnaces for ingots, i.e. soaking pits
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- Materials Engineering (AREA)
- Theoretical Computer Science (AREA)
- Thermal Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Mechanical Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Control Of Heat Treatment Processes (AREA)
Abstract
A method for improving the calculation accuracy of a heating furnace billet temperature tracking model based on black box test transverse partition data belongs to the technical field of heating furnace control. The method comprises the steps of transversely dividing a stepping heating furnace into N regions, wherein N is an integer larger than 1, adopting N identical test steel billets to track and measure the temperature change trend of the steel billets in the heating process of the stepping heating furnace, and respectively obtaining test data of the temperature of the steel billets and the temperature of furnace gas changing along with time in the test process of the N regions in the stepping heating furnace; extracting reference data consisting of furnace gas temperature, lower furnace gas temperature, billet upper surface temperature, billet lower surface temperature and time points; by reference toData determination of C for each layer of the billetp、λ、qu(i)、qd(i) According to the heat flow coefficient q of the upper surface of the steel billetu(i) Heat flux q of the lower surface of the billetd(i) Substituting the formula (formula 1) into the calculation formula of comprehensive thermal emissivity to obtain the comprehensive thermal emissivity epsilon corresponding to the N regionsg. And the calculation accuracy under all furnace conditions is ensured.
Description
Technical Field
The application relates to the technical field of heating furnace control, in particular to a method for improving the calculation accuracy of a heating furnace billet temperature tracking model based on black box test transverse partition data.
Background
The steel billet temperature tracking model is a thermal mathematical model and is used for simulating and calculating the change of a temperature field when a steel billet is heated in a heating furnace. The mathematical model technology of the heating furnace mainly comprises functional models of billet heating curve optimization, billet temperature tracking calculation, furnace temperature set value calculation, combustion optimization control, self-adaptive correction and the like. In actual production, the temperature tracking model calculates the temperature field data of the steel billet according to the current incoming material information (including the size, the type, the incoming temperature and the like of the steel billet) and the current furnace condition information (the current air gas flow, the air gas valve position value, the furnace temperature set value and the like), the furnace temperature set value model calculates the furnace temperature set value according to the temperature field data of the steel billet, then the furnace temperature set value is transmitted to the combustion optimization control model to calculate the optimization parameters and the air gas valve position regulating quantity, and the air gas flow is directly regulated and controlled. Therefore, the calculation accuracy of the billet temperature tracking model ultimately affects the heating quality of the billet and the control level of energy consumption.
The development of the temperature tracking model of the steel billet in the stepping heating furnace is mainly a model developed based on data mining and statistical theory, and mainly comprises two modules of data acquisition and data mining analysis. The data processing mainly finishes real-time data acquisition to obtain the current heating system of the heating furnace, then the acquired production data are processed into analysis samples through a data mining analysis module and are stored in a database according to certain rules, and the optimal furnace temperature set value of the current state of the heating furnace is obtained from the database during production and the temperature of steel billets in the furnace is predicted. The method is biased to a thermal engineering professional theory, and the furnace temperature decision and the furnace temperature forecast cannot fully consider the actual furnace condition information, so that the purposes of optimal control of the heating furnace, energy conservation and consumption reduction obviously cannot be reasonably realized in the practical production.
The first prior art discloses a heating furnace billet temperature tracking model correction method, which comprises the following steps: tracking, measuring and recording data of the change of the temperature of the steel billet and the temperature of furnace gas along with time in the test process; processing the test data, and extracting reference data for calculating a radiation coefficient reference value; combining the reference data, and iteratively calculating the radiation coefficient reference value of each furnace area; correcting the radiation coefficient reference value by combining the production historical data of the steel billet; and storing the correction results in a correction coefficient table in a classified manner. The method can improve the calculation precision of the billet temperature tracking model, so that the corrected model can accurately simulate the heating process of the billet in the furnace, and has important significance for improving the control precision of the furnace temperature of the heating furnace. The method mainly corrects the radiation coefficient continuously through a model so as to improve the calculation accuracy continuously, but the method has a complex calculation process, and when the furnace temperature data fluctuation is large along with different steel types and different furnace conditions, the coefficient correction generates a large error, so that the calculation accuracy under all the furnace conditions cannot be ensured.
The invention aims to solve the problem that aiming at the defects of the prior art, the method for improving the calculation accuracy of the heating furnace billet temperature tracking model is obtained by processing data based on the furnace information acquired by the black box test.
Disclosure of Invention
In order to solve the problems in the prior art, the application aims to provide a method for improving the calculation accuracy of a heating furnace billet temperature tracking model based on the horizontal partition data of a black box test, so that the heating furnace is always controlled above the optimal working temperature of the billet, a simple method for improving the calculation accuracy of the model is provided, the problems that different steel types have different furnace conditions, and when the furnace temperature data fluctuates greatly, the coefficient correction generates large errors and the like are solved, and the calculation accuracy under all the furnace conditions is ensured.
The application example provides a method for improving the calculation accuracy of a heating furnace steel billet temperature tracking model based on transverse partition data of black box test, which comprises the following steps:
step one, transversely dividing a stepping heating furnace into N regions, wherein N is an integer larger than 1, adopting N identical test steel billets to track and measure the temperature change trend of the steel billets in the heating process of the stepping heating furnace, and respectively obtaining test data of the temperature of the steel billets and the temperature of furnace gas changing along with time in the test process of the N regions in the stepping heating furnace;
step two, processing the test data to obtain trend graphs of the temperature of furnace gas above the test billet, the temperature of furnace gas below the test billet, the temperature of the upper surface of the test billet, the temperature of the lower surface of the test billet, the temperature of the head of the test billet and the temperature of the tail of the test billet along with time and the change of the position in the test billet, and extracting reference data consisting of the temperature of the furnace gas below the test billet, the temperature of the upper surface of the test billet, the temperature of the lower surface of the test billet and time points; determining C of each layer of the billet from the reference datap、λ、qu(i)、qd(i)，CpSpecific heat of each layer of the steel billet, lambda is the heat conduction coefficient of the steel billet, and the heat flow coefficient q of the upper surface of the steel billetu(i) Heat flux q of the lower surface of the billetd(i)；
Thirdly, according to the heat flow coefficient q of the upper surface of the steel billetu(i) Heat flux q of the lower surface of the billetd(i) Substituting the formula (formula 1) into the calculation formula of comprehensive thermal emissivity to obtain the comprehensive thermal emissivity epsilon corresponding to the N regionsg；
In the formula, epsilongQ is steel for comprehensive heat radiation coefficientSurface heat flux density of the blank, FsgIs the surface area of the billet, TgIs the temperature of furnace gas, TsThe surface temperature of the steel billet is shown as a, the thermal diffusivity is shown as a, delta is the coordinate position of the test point, and y is the coordinate axis of the steel billet along the thickness direction.
Step four, when the actual steel billet enters the furnace, the model automatically indexes out the comprehensive thermal emissivity epsilon corresponding to the N areas of the slab according to the chemical components contained in the steel billet data and the temperature calculated in the previous periodgCarrying the temperature data into a slab temperature tracking model to calculate furnace gas temperature, lower furnace gas temperature, upper surface temperature of the steel billet and lower surface temperature of the steel billet at the time of discharging the steel billet corresponding to the N areas;
and fifthly, selecting the lowest values of the upper furnace gas temperature, the lower furnace gas temperature, the upper surface temperature of the steel billet and the lower surface temperature of the steel billet which correspond to the N areas and are not at the same time, and determining control targets of the upper furnace gas temperature, the lower furnace gas temperature, the upper surface temperature of the steel billet and the lower surface temperature of the steel billet.
In some examples, C of each layer of the billet is determined from the reference datapThe specific method of lambda is that the reference data of N areas are respectively substituted into the heat conduction differential equation (formula 2) in the billet,
wherein rho is billet density and CpThe specific heat of the steel billet, tau, T and lambda are respectively the heating time, the temperature and the heat conduction coefficient of the steel billet.
In some examples, the heat flow coefficient q of the top surface of the steel billetu(i) Heat flux q of the lower surface of the billetd(i) The calculation formula of (2) is as follows:
wherein,
in the formula,
the Fourier coefficient of the j-th layer of the steel billet is obtained after the steel billet is layered in the thickness direction; lambda [ alpha ]j,j-1 represents the equivalent heat conduction coefficient between the j-1 layer and the j-th layer of the billet, W/(m.K); delta tau is the time interval, s, calculated by the billet temperature model; rho is the density of the steel billet, Kg/m 3; cpThe specific heat of the steel billet is J/(kg.K); delta y is the billet layering thickness, m;
the temperature of the upper surface of the steel billet;
the temperature of the lower surface of the steel billet is shown.
In some examples, the lateral direction of the walking beam furnace is divided into two zones.
In some examples, the walking beam furnace has an effective furnace length of 28600mm and a width of 16500 mm.
In some examples, the length direction of the walking beam furnace sequentially comprises a preheating section, a first heating section, a second heating section and a soaking section.
In some examples, the test billet dimensions are: thickness, width, length 150mm 2100mm 7500 mm.
In some examples, the test steel blank is selected to be Q235 steel, cold.
In some examples, the temperature of the test billet during the test is obtained by embedding thermocouples in the upper surface and the lower surface of the test billet.
In some examples, the buried thermocouple is located 15mm from the surface of the billet.
The method for improving the calculation accuracy of the heating furnace steel billet temperature tracking model based on the black box test transverse partition data has the advantages that:
the comprehensive thermal radiation coefficient epsilon of each subarea is obtained by taking the furnace information acquired by the black box test of the transverse subarea of the heating furnace as the data basegThe method obtains the temperatures of different subareas, determines the lowest temperature of the heating furnace by the temperature, ensures that the heating furnace is always controlled above the optimal working temperature of the steel billet, provides a simple method for improving the calculation accuracy of the model, avoids the problems that the coefficient correction generates larger errors when different steel types and different furnace conditions have larger furnace temperature data fluctuation, and the heating uniformity of the steel billet is influenced due to the different distribution positions of burners in the heating furnace, and the like, and ensures the calculation accuracy under all the furnace conditions.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a coordinate system of differential equation of internal heat conduction of a steel billet;
FIG. 2 is a schematic view of the position of a test billet within a furnace of a heating furnace.
Icon: 1-heating a furnace; 2-testing the steel billet; 3-paragraph number 1; 4-paragraph number 2; 5-paragraph number 3; 6-paragraph number 4; 7-sub-segment number 1; 8-subsection number 2; 9-furnace width; 10-test billet length.
Detailed Description
Embodiments of the present application will be described in detail below with reference to examples, but those skilled in the art will appreciate that the following examples are only illustrative of the present application and should not be construed as limiting the scope of the present application. The examples, in which specific conditions are not specified, were conducted under conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products available commercially. The detection method is not particularly described, and the detection is carried out according to national standards or conventional detection methods.
The following specifically explains the method for improving the calculation accuracy of the heating furnace billet temperature tracking model based on the black box test transverse partition data in the embodiment of the present application:
considering that the distribution positions of the burners in the heating furnace are different, the distribution of furnace gas in the same furnace section is different, and finally the heating uniformity of the billet is influenced, so the acquired data can be closer to the real condition of the furnace condition by adopting the method for testing the billet by sections. The following combination is that the heating furnace 1 has four discharging situations in actual production (namely, the heating furnace 1 is transversely divided into two subareas in figure 2, namely, a furnace gas A area 7 (subsection number 1) and a furnace gas B area 8 (subsection number 2) are respectively provided with two test steel billets 10, and the two subareas are uniformly divided from the position of the central line of the furnace), the heating furnace 1 is divided into two areas for experiment, the data comparison is more obvious, the distribution of double discharging and four discharging of the steel billets in the furnace in actual production is also met, and the experiment cost is reduced.
A method for improving the calculation accuracy of a heating furnace billet temperature tracking model based on transverse partition data of black box test tests comprises the following steps:
step one, transversely dividing the walking beam furnace into two areas, tracking and measuring the temperature change trend of the steel billet 2 in the heating process of the walking beam furnace 1 by adopting two identical test steel billets 2, and respectively obtaining the test data of the change of the steel billet temperature and the furnace gas temperature along with time in the test process of the two areas in the walking beam furnace.
Test data: step-by-step heating furnace 1: the effective furnace length is 28600mm, and the furnace width 9 is 16500 mm.
The length direction of the stepping heating furnace sequentially comprises a preheating section, a heating section and a soaking section, and the corresponding sections are (section number 1)3, (section number 2)4, (section number 3)5 and (section number 4) 6.
The material of the test steel billet is Q235 steel and cold billet, and the size of the test steel billet is as follows: thickness, width, test billet length 10-150 mm 2100mm 7500 mm.
The test steel billet obtains the temperature of the steel billet in the test process through black box experiments by embedding thermocouples on the upper surface and the lower surface of the steel billet, wherein the positions of the embedded thermocouples are 15mm away from the surface of the steel billet, and the inventor finds that: the position can reduce the influence of furnace gas temperature, more accurately reflect the temperature of the upper surface and the lower surface of the billet simultaneously, and improve the calculation precision of the model.
And step two, processing the test data to obtain trend graphs of the temperature of furnace gas above the test billet, the temperature of furnace gas below the test billet, the temperature of the upper surface of the test billet, the temperature of the lower surface of the test billet, the temperature of the head of the test billet and the temperature of the tail of the test billet along with time and the change of the position in the test billet, and extracting reference data consisting of the temperature of the furnace gas below the test billet, the temperature of the upper surface of the test billet, the temperature of the lower surface of the test billet and time points from the trend graphs, wherein the reference data refer to table 1.
TABLE 1 Black box experiment table of the change of each temperature of steel billet with time
Carrying out calculation by taking the data into the formula 2 and the formula 3;
determining C of each layer of the billet from the reference datapThe specific method of lambda is that the reference data of the two areas are respectively substituted into the heat conduction differential equation (formula 2) in the billet,
wherein rho is billet density and CpThe specific heat of the steel billet, tau, T and lambda are respectively the heating time, the temperature and the heat conduction coefficient of the steel billet.
Heat flow coefficient q of the upper surface of the billetu(i) Heat flux q of the lower surface of the billetd(i) The calculation formula of (2) is as follows:
wherein,
in the formula,
the Fourier coefficient of the j-th layer of the steel billet is obtained after the steel billet is layered in the thickness direction; lambda [ alpha ]j,j-1Represents the equivalent heat conduction coefficient between the j-1 layer and the j-th layer of the billet, W/(m.K); delta tau is the time interval, s, calculated by the billet temperature model; rho is the density of the steel billet, Kg/m 3; cpThe specific heat of the steel billet is J/(kg.K); delta y is the billet layering thickness, m;
the temperature of the upper surface of the steel billet;
the temperature of the lower surface of the steel billet is shown.
Calculating C of each layer of the billet by the calculationp、λ、qu(i)、qd(i)，CpSpecific heat of each layer of the steel billet, lambda is the heat conduction coefficient of the steel billet, and the heat flow coefficient q of the upper surface of the steel billetu(i) Heat flux q of the lower surface of the billetd(i)。
Thirdly, according to the heat flow coefficient q of the upper surface of the steel billetu(i) Heat flux q of the lower surface of the billetd(i) And substituting the comprehensive emissivity calculation formula (formula 1).
In the formula, epsilongQ is the surface heat flux of the steel billet, FsgIs the surface area of the billet, TgIs the temperature of furnace gas, TsIs the surface temperature of the steel billet, a is heatAnd the diffusivity is delta is the coordinate position of the test point, and y is the coordinate axis of the billet along the thickness direction.
Calculating the comprehensive thermal emissivity epsilon corresponding to the two regionsgSee table 2 for emissivity of each furnace section.
The above-mentioned comprehensive thermal emissivity coefficient epsilongThe method comprehensively considers the factors such as the coordinates of the test points, the surface area of the steel billet, the heat diffusion rate, the heat flow density and the like, can more comprehensively represent the heat flow characteristics of the steel billet, and improves the calculation precision.
TABLE 2 radiation coefficient of each furnace section
Step four, when the actual steel billet enters the furnace, the model automatically indexes out the comprehensive thermal emissivity epsilon corresponding to the two areas of the slab blank according to the chemical components contained in the steel billet data and the temperature calculated in the previous periodgAnd carrying into a slab temperature tracking model to calculate furnace gas temperature, lower furnace gas temperature, upper surface temperature of the steel billet and lower surface temperature of the steel billet corresponding to the two zones at the moment of discharging the steel billet, taking the following example as model data calculated by the zone A, and referring to a table 3 of time-dependent change table of each temperature of the slab temperature tracking model steel billet. The method for calculating the time variation of each temperature of the slab temperature tracking model billet is the prior art which can be directly adopted by related technicians and is not described in detail herein.
TABLE 3 slab temperature tracking model billet temperature variation with time table
And fifthly, selecting the lowest values of the upper furnace gas temperature, the lower furnace gas temperature, the upper surface temperature of the steel billet and the lower surface temperature of the steel billet at different moments corresponding to the area A and the area B, and determining the control targets of the upper furnace gas temperature, the lower furnace gas temperature, the upper surface temperature of the steel billet and the lower surface temperature of the steel billet.
And (3) checking and calculating the precision:
because 11:41 is divided into the moment of billet tapping, the error between the model calculation value and the actual value at the moment can be compared to obtain the accuracy of the model calculation, and the accuracy is as follows:
| upper furnace gas | Bottom furnace gas | Upper surface of | Lower surface | Time of day | |
| 1228.9 | 1226.1 | 1206.1 | 1186.8 | 11:41 | Actual value |
| 1223.5 | 1216.3 | 1206.4 | 1175.6 | 11:41 | Calculated value |
| 5.4 | 9.8 | 0.3 | 11.2 | Error of the measurement |
As can be seen from the data in the table, the error range between the calculated value and the actual value of the model in the area A at the tapping time is within 11.2 degrees, and the temperature change trend of the steel billet in the furnace is accurately predicted. The error range of the calculated value and the actual value of the same B zone model is controlled within 15 degrees, so that when the transverse working conditions of the heating furnace are different, a prepared calculation result can be obtained by the model, and the temperature change trend of the steel billet in the heating furnace can be accurately predicted.
The comprehensive thermal radiation coefficient epsilon of each subarea is obtained by taking the furnace information acquired by the black box test of the transverse subarea of the heating furnace as the data basegThe method obtains the temperatures of different subareas, determines the lowest temperature of the heating furnace by the temperature, ensures that the heating furnace is always controlled above the optimal working temperature of the steel billet, provides a simple method for improving the calculation precision of the model, avoids the problems that the coefficient correction generates larger errors when different steel types and different furnace conditions have different furnace temperature data fluctuation, and the heating uniformity of the steel billet is influenced due to different distribution positions of burners in the heating furnace, and the like, and ensures the calculation precision under all furnace conditions. Obtaining the comprehensive thermal emissivity epsilon of each subarea according to a black box testgA reference data set is formed, the accuracy fluctuates up and down due to repeated adjustment of the emissivity is not needed, the applicability of the model is increased, the model is suitable for the control of heating furnaces of all steel types, and the problem of inconsistent control standards caused by the steel types is avoided.
The foregoing is merely exemplary of the present application and is not intended to limit the present application, which may be modified or varied by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method for improving the calculation accuracy of a heating furnace billet temperature tracking model based on black box test transverse partition data is characterized by comprising the following steps: the method comprises the following steps:
step one, transversely dividing a stepping heating furnace into N regions, wherein N is an integer larger than 1, adopting N identical test steel billets to track and measure the temperature change trend of the steel billets in the heating process of the stepping heating furnace, and respectively obtaining test data of the temperature of the steel billets and the temperature of furnace gas changing along with time in the test process of the N regions in the stepping heating furnace;
step two, processing the test data to obtain trend graphs of the temperature of furnace gas above the test billet, the temperature of furnace gas below the test billet, the temperature of the upper surface of the test billet, the temperature of the lower surface of the test billet, the temperature of the head of the test billet and the temperature of the tail of the test billet along with time and the change of the position in the test billet, and extracting reference data consisting of the temperature of the furnace gas below the test billet, the temperature of the upper surface of the test billet, the temperature of the lower surface of the test billet and time points; determining C of each layer of the billet from the reference datap、λ、qu(i)、qd(i)，CpSpecific heat of each layer of the steel billet, lambda is the heat conduction coefficient of the steel billet, and the heat flow coefficient q of the upper surface of the steel billetu(i) Heat flux q of the lower surface of the billetd(i)；
Thirdly, according to the heat flow coefficient q of the upper surface of the steel billetu(i) Heat flux q of the lower surface of the billetd(i) Substituting into a calculation formula (formula 1) of comprehensive thermal emissivity to obtainComprehensive thermal emissivity epsilon corresponding to N regionsg；
In the formula, epsilongQ is the surface heat flux of the steel billet, FsgIs the surface area of the billet, TgIs the temperature of furnace gas, TsThe surface temperature of the steel billet is shown, a is thermal diffusivity, delta is the coordinate position of a test point, and y is the coordinate axis of the steel billet along the thickness direction;
step four, when the actual steel billet enters the furnace, the model automatically indexes out the comprehensive thermal emissivity epsilon corresponding to the N areas of the slab according to the chemical components contained in the steel billet data and the temperature calculated in the previous periodgCarrying the temperature data into a slab temperature tracking model to calculate furnace gas temperature, lower furnace gas temperature, upper surface temperature of the steel billet and lower surface temperature of the steel billet at the time of discharging the steel billet corresponding to the N areas;
and fifthly, selecting the lowest values of the upper furnace gas temperature, the lower furnace gas temperature, the upper surface temperature of the steel billet and the lower surface temperature of the steel billet which correspond to the N areas and are not at the same time, and determining control targets of the upper furnace gas temperature, the lower furnace gas temperature, the upper surface temperature of the steel billet and the lower surface temperature of the steel billet.
2. The method of claim 1, wherein C is determined for each layer of the slab from the reference datapThe specific method of lambda is that the reference data of N areas are respectively substituted into the heat conduction differential equation (formula 2) in the billet,
wherein rho is billet density and CpThe specific heat of the steel billet, tau, T and lambda are respectively the heating time, the temperature and the heat conduction coefficient of the steel billet.
3. The method of claim 1Characterized in that the heat flow coefficient q of the upper surface of the billet isu(i) Heat flux q of the lower surface of the billetd(i) The calculation formula of (2) is as follows:
wherein,
in the formula,
the Fourier coefficient of the j-th layer of the steel billet is obtained after the steel billet is layered in the thickness direction; lambda [ alpha ]j,j-1Represents the equivalent heat conduction coefficient between the j-1 layer and the j-th layer of the billet, W/(m.K); delta tau is the time interval, s, calculated by the billet temperature model; rho is the density of the steel billet, Kg/m 3;Cpthe specific heat of the steel billet is J/(kg.K); delta y is the billet layering thickness, m;
the temperature of the upper surface of the steel billet;
the temperature of the lower surface of the steel billet is shown.
4. The method of claim 1, wherein the lateral direction of the walking-beam furnace is divided into two zones.
5. The method of claim 1, wherein the walking beam furnace has an effective furnace length of 28600mm and a width of 16500 mm.
6. The method according to claim 1, wherein the length direction of the walking beam furnace comprises a preheating section, a heating section and a soaking section in sequence.
7. The method of claim 1, wherein the test billet dimensions are: thickness, width, length 150mm 2100mm 7500 mm.
8. The method of claim 1 wherein said test steel blank is selected from the group consisting of Q235 steel, cold blank.
9. The method of claim 1, wherein the temperature of the test billet during the test is obtained by embedding thermocouples in the upper surface and the lower surface of the test billet.
10. The method of claim 9, wherein the position of the embedded thermocouple is 15mm from the surface of the billet.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111005304.3A CN113621791A (en) | 2021-08-30 | 2021-08-30 | Method for improving heating furnace billet temperature tracking model calculation accuracy based on black box test transverse partition data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111005304.3A CN113621791A (en) | 2021-08-30 | 2021-08-30 | Method for improving heating furnace billet temperature tracking model calculation accuracy based on black box test transverse partition data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113621791A true CN113621791A (en) | 2021-11-09 |
Family
ID=78388321
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111005304.3A Pending CN113621791A (en) | 2021-08-30 | 2021-08-30 | Method for improving heating furnace billet temperature tracking model calculation accuracy based on black box test transverse partition data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113621791A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115094228A (en) * | 2022-05-13 | 2022-09-23 | 燕山大学 | Portable black box full-furnace-condition synchronous temperature measurement array for heating furnace |
| CN115512455A (en) * | 2022-09-22 | 2022-12-23 | 济南汉升节能技术有限公司 | Heating furnace steel billet temperature prediction method integrating mechanism and model migration |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544356A (en) * | 2013-10-30 | 2014-01-29 | 中冶南方（武汉）信息技术工程有限公司 | Heat treatment furnace heating model calculation method based on graphic processing unit (GPU) |
| CN110918655A (en) * | 2019-11-30 | 2020-03-27 | 宝钢特钢韶关有限公司 | Refined heating control method |
| CN111125913A (en) * | 2019-12-25 | 2020-05-08 | 东北大学 | Method and device for identifying total heat absorption rate of heating furnace |
| CN111763819A (en) * | 2020-08-03 | 2020-10-13 | 重庆赛迪热工环保工程技术有限公司 | Control method for heating steel plate of low-temperature roller-hearth heat treatment furnace |
-
2021
- 2021-08-30 CN CN202111005304.3A patent/CN113621791A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544356A (en) * | 2013-10-30 | 2014-01-29 | 中冶南方（武汉）信息技术工程有限公司 | Heat treatment furnace heating model calculation method based on graphic processing unit (GPU) |
| CN110918655A (en) * | 2019-11-30 | 2020-03-27 | 宝钢特钢韶关有限公司 | Refined heating control method |
| CN111125913A (en) * | 2019-12-25 | 2020-05-08 | 东北大学 | Method and device for identifying total heat absorption rate of heating furnace |
| CN111763819A (en) * | 2020-08-03 | 2020-10-13 | 重庆赛迪热工环保工程技术有限公司 | Control method for heating steel plate of low-temperature roller-hearth heat treatment furnace |
Non-Patent Citations (2)
| Title |
|---|
| 裘洪礼 等: "提高加热炉辐射系数精度的计算方法", 《冶金自动化》 * |
| 赵明: "加热炉二级系统设计及数学模型优化", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115094228A (en) * | 2022-05-13 | 2022-09-23 | 燕山大学 | Portable black box full-furnace-condition synchronous temperature measurement array for heating furnace |
| CN115512455A (en) * | 2022-09-22 | 2022-12-23 | 济南汉升节能技术有限公司 | Heating furnace steel billet temperature prediction method integrating mechanism and model migration |
| CN115512455B (en) * | 2022-09-22 | 2023-07-25 | 济南汉升节能技术有限公司 | Heating furnace billet temperature prediction method integrating mechanism and model migration |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109248928B (en) | Dynamic furnace temperature control method for hot rolling heating furnace | |
| CN113621791A (en) | Method for improving heating furnace billet temperature tracking model calculation accuracy based on black box test transverse partition data | |
| CN110826282B (en) | Reflow soldering process simulation model correction method based on heating factors | |
| CN111950176B (en) | Optimization method and optimization device for billet heating model and electronic equipment | |
| CN101509812A (en) | Soft measurement method for billet temperature distribution in smelting and heating-furnace | |
| CN105886751A (en) | Coordinated control system and method for plate temperature of cold-rolled hot-galvanized annealing furnace | |
| CN111488714A (en) | Method for accurately calculating wind speed of hot air reflow soldering nozzle | |
| CN104894362A (en) | Method for setting temperature of heating furnace in cold and hot steel billet mixed loading | |
| CN104289532B (en) | Strip steel watermark point temperature-controlled process | |
| CN108984943B (en) | Heating furnace billet temperature tracking model correction method | |
| CN110307910B (en) | Method and device for acquiring temperature of billet of walking beam heating furnace | |
| CN101139652A (en) | Off-line prediction method for bell-type furnace steel roll annealing process | |
| CN111859822B (en) | Method for predicting generation amount of nitrogen oxides in glass melting furnace | |
| CN117494531B (en) | Medium carbon steel decarburization depth prediction method based on finite element and XGBoost algorithm | |
| CN113609739A (en) | Method for constructing material heat treatment process and microstructure and performance relation database | |
| CN114764525A (en) | Grain size prediction method for multi-pass and multi-step forging forming process of axle | |
| Ryabchikov | Selection of steel strip annealing energy-saving conditions in view of the substandard products share | |
| CN117930780A (en) | Quartz product production quality control method and system | |
| CN112380688A (en) | Method for determining casting blank temperature and feeding temperature, storage medium and processor | |
| CN115512455B (en) | Heating furnace billet temperature prediction method integrating mechanism and model migration | |
| CN112325327B (en) | Proportion control type burner residual oxygen amount correction method and device | |
| Wu et al. | A Modified Johnson–Cook Model for Modeling AA6061-T6 under Different Temperatures Considering Ductile Fracture | |
| CA3142798A1 (en) | Method and apparatus for improving furnace temperature uniformity | |
| CN104531978A (en) | Non-oriented silicon steel heating method and modeling control method of non-oriented silicon steel heating method | |
| CN105234191A (en) | Laminar cooling temperature control method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211109 |
|
| RJ01 | Rejection of invention patent application after publication |