• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The present invention relates to a cooling control method of a hot rolled steel sheet, which is arranged in the order of a finish rolling mill, a runout table, and a winding machine, and includes a thermometer between a rear end of the hot finishing rolling mill and a front end of the winding machine. A method of controlling temperature, comprising: a. Obtaining a learning coefficient of the new learning method using the performance FDT, the performance CT, and the mail order speed of the previous field; b. Calculating CT by inputting an arbitrary virtual water pattern from a target FDT of the steel sheet using an exact temperature equation; c. Comparing the computed CT with the target CT to change the virtual jet pattern; d. Finding the optimal water pattern to minimize the difference between the target CT and the calculated CT in the virtual water pattern through the repetition of steps b and c; e. Feed forward control of the winding temperature; f. Controlling main water quantity by performing feedback control to compensate for a difference between a target CT and the target CT during the feedforward control; g. Obtaining new learning coefficients using the results FDT, results CT and mailing speed, and preparing for the next hot rolled steel sheet; The main point of the method of controlling the winding temperature of the hot rolled sheet through learning considering the phase transformation, characterized in that consisting of.
    公开号:KR20020052723A
    申请号:KR1020000082160
    申请日:2000-12-26
    公开日:2002-07-04
    发明作者:한흥남
    申请人:이구택；주식회사 포스코；
    IPC主号:
    专利说明:

    Coiling temperature control method of hot strip using learning method}
    [5] The present invention relates to a cooling control method of a hot rolled steel sheet, and more particularly, to a basic equation and a learning method in a cooling control model required for hitting a target winding temperature of a hot rolled steel sheet.
    [6] The present invention, unlike the existing method, to prevent the fall of the coiling temperature hit ratio of the first change in the set change caused by a large number of divisions of the learning coefficient with respect to the steel grade, thickness, target finish rolling temperature and target winding temperature, phase transformation and Providing a rigid model that considers heat transfer in the thickness direction, and based on this, a new learning method related to water cooling is introduced, and ultimately, a large number of classifications for steel grade, thickness, target finish rolling temperature, and target winding temperature in the existing learning method are introduced. By providing a method of eliminating, it is to provide a method of improving the accuracy of the winding temperature control when changing the set defined in the existing model through the learning using the immediately preceding field at all times during hot rolled steel sheet work.
    [7] During hot rolling, the steel sheet, which is a hot rolled steel, is rolled, cooled, and wound into a winding machine to produce a general coil. In producing such a hot rolled steel sheet, the coil must be cooled to a suitable temperature. The steel sheet in current hot rolling systems is cooled by, for example, the cooling system shown by FIG. As shown in the drawing, in a hot rolling system, the steel sheet is rolled in a finish rolling mill and then rolled by a winder after passing through a run out table. A cooling system for cooling the steel sheet at a suitable temperature is arranged along the runout table. In addition, in the cooling system, an entrance thermometer for measuring the temperature of the steel sheet to be cooled and an exit thermometer for measuring the temperature of the steel sheet after being cooled at the cooling stand outlet are installed. The cooling system is arranged above and below with the runout table in between. Each part consists of a water cooling part which cools a steel plate by pouring water into a steel plate, and an air cooling part which cools a steel plate with air. The water cooling section and the air cooling section disposed on the upper and lower portions of the cooling system are respectively divided into cooling banks. Each cooling bank can control the cooling capacity for cooling the steel sheet.
    [8] On the other hand, the existing winding temperature control is the target finish rolling temperature (or cooling inlet temperature) (hereinafter referred to as 'FDT') and cooling exit temperature (or target winding temperature) (hereinafter referred to as just 'CT') depending on the steel grade. To set FDT and CT accordingly. For this purpose, the air volume is calculated using the target FDT, the target CT, and the target plate speed (V P ), the required water cooling amount is calculated, and the number of main banks is determined. The basic equation and the learning coefficient of water-cooled heat transfer are involved in the calculation method of water-cooled drop, which is explained in more detail as follows.
    [9] Depending on the use of each bank, the temperature drop due to water cooling per bank is calculated using the Lumped method, which ignores the temperature gradient in the thickness direction, and the basic equation, which ignores the phase transformation heating. The total water cooling amount Tw in the runout table representing this calculation is shown in Equation (1).
    [10] Tw = 1000 lbank (Qiu + Qid) / (3600 Vp Cp ρHf)
    [11] (lbank: length of i-th bank, Hf: coil thickness, Vp: plate speed, Cp: specific heat of steel plate, ρ: density of steel plate, Qiu: heat flux of i-th upper bank, Qid: heat flux of i-th lower bank)
    [12] The upper and lower heat flux values for each bank used in Equation 1 are expressed as follows.
    [13] Qiu = fo f2i Tiu fv
    [14] Qid = fo f2i Tid
    [15] (fo: basic heat flux coefficient, f2i: heat flux coefficient of i-th bank, Tiu: water temperature correction coefficient of upper bank, Tid: water temperature correction coefficient of lower bank, fv: plate speed correction coefficient)
    [16] The fo for calculating the heat flux is expressed by the coefficient value of C0 to C8 and the learning coefficient f1 as shown in Equation 3.
    [17] fo = f1 [C0 + C1 Hf + C2 Wf + C3 FDT + C4 CT + C5 Tw + C6 v + C7 (FDT-CT) + C8 (l / v)]
    [18] (Tw: water temperature, Wf: width of steel plate)
    [19] The learning coefficient f1 of the existing cooling model is classified into a thousand or more values according to steel grade, thickness, target FDT, and target CT (learning classification). Therefore, if the steel sheet corresponding to the same learning section is not worked for a long time and then suddenly, the changed environmental factors (for example, air temperature, humidity, valve state, etc.) are not reflected in the learning coefficient of the first set change. The temperature hit ratio will drop. Also, several sets of hot rolled steel plates must be worked on to restore the learning coefficient after the set change. Therefore, it is not possible to secure a high coiling temperature hit ratio by using a large number of distinct values of the learning coefficient of the existing cooling model.
    [20] The present invention has been proposed to solve the problem that the winding temperature hit ratio of the first set change due to the classification of a number of learning coefficients for steel grade, thickness, target finish rolling temperature and target winding temperature by improving the conventional method, winding Winding temperature by introducing a rigid model in consideration of phase transformation and heat transfer in the thickness control model and introducing a new learning technique that eliminates numerous learning divisions for steel grade, thickness, target finish rolling temperature and target winding temperature in the existing learning methods. It is an object of the present invention to provide a method of increasing the accuracy of control.
    [1] 1 is a schematic diagram of a runout table cooling system.
    [2] Figure 2 is a block diagram of a thermometer calculation model that is the basic formula.
    [3] Figure 3 is a flow chart of the learning method for cooling control of the present invention.
    [4] Figure 4 is a comparison of the degree of hit of the first set change by the conventional method and the control method of the present invention.
    [21] Hereinafter, the present invention will be described in more detail with reference to the drawings.
    [22] In order to achieve the above object, the present invention is arranged in the order of finishing mill, run-out table, and winding machine, and controls the temperature of the hot rolled steel sheet in the hot rolling equipment installed between the rear end of the hot finishing rolling mill and the front end of the winding machine. In the way,
    [23] a. Obtaining a learning coefficient of the new learning method using the performance FDT, the performance CT, and the mail order speed of the previous field;
    [24] b. Calculating CT by inputting an arbitrary virtual water pattern from a target FDT of the steel sheet using an exact temperature equation;
    [25] c. Comparing the computed CT with the target CT to change the virtual jet pattern;
    [26] d. Finding the optimal water pattern to minimize the difference between the target CT and the calculated CT in the virtual water pattern through the repetition of steps b and c;
    [27] e. Feed forward control of the winding temperature;
    [28] f. Controlling main water quantity by performing feedback control to compensate for a difference between a target CT and the target CT during the feedforward control;
    [29] g. Obtaining new learning coefficients using the results FDT, results CT and mailing speed, and preparing for the next hot rolled steel sheet; The winding temperature control method of the hot rolled sheet through learning considering the phase transformation, characterized in that consisting of, the process of calculating the exact temperature from the target FDT of the steel sheet
    [30] h. Calculating equilibrium diagrams, specific heat, and transformation calorific value of each phase as a function of temperature through thermodynamic calculation of the composition of the steel sheet;
    [31] i. Determining the fraction of each phase by calculating the phase transformation speed based on the equilibrium diagram obtained from the thermodynamic calculation and the temperature history calculated in step c;
    [32] j. Calculating a temperature based on the phase transformation fraction calculated in step b and the specific heat and transformation calorific value calculated in step a;
    [33] k. Steps b and c are nonlinearly connected, so that it is calculated repeatedly.
    [34] Hereinafter, the present invention will be described in detail.
    [35] First, the present invention is arranged in the order of finishing mill, run out table and winding machine, and is applied in a hot rolling facility in which a thermometer is installed between the rear end of the hot finishing rolling mill and the front end of the winding machine.
    [36] In the existing cooling control equation model, if the hot rolled steel sheets belonging to a specific learning section are not working for a long time due to the large number of learning divisions caused by the inaccuracy of the basic equation, and suddenly work, the winding temperature hit becomes difficult due to the inaccuracy of the learning coefficient of the first chapter. do. To solve this problem, first of all, the strictness of the basic equation model was improved.
    [37] In the existing model, a large number of steel grades are classified in the learning section to explain only the difference in the degree of water-cooled heat transfer, which is the external heat transfer shape, without considering the difference in phase transformation behavior such as the transformation start and end temperature and the amount of transformation heat generation according to the steel types Was used to vary the amount of water-cooled heat transfer. In addition, the basic equations were made by using the Lumped model, which ignores the temperature gradient in the thickness direction, and a number of thicknesses in the learning section were selected to compensate for the difference in the water-cooled heat transfer coefficient for the steel sheets with different thicknesses. In addition, the classification of target finish rolling temperature and target winding temperature was introduced to reduce the hit rate of the first set change. In the present invention, by laying down the basic equation as a method for analyzing the phase transformation and heat transfer phenomenon in the thickness direction as follows to eliminate the learning division.
    [38] The exact model will now be described.
    [39] The amount of heat generated during the transformation from austenite to ferrite, pearlite and bainite and the specific heat of each phase are very important factors in calculating the temperature change during cooling of the steel. For the thermodynamic calculation, the Fe-C-Mn system was calculated using a sublattice model. In the present invention, austenite, ferrite, and cementite are considered, and free energy change due to magnetism is considered. The equilibrium condition was calculated by para equilibrium close to the actual condition. The calorific value of ferrite transformation was calculated by dividing the enthalpy change in the transformation from austenite to ferrite and untransformed austenite by the mole fraction of ferrite formed, and the calorific value of the ferrite transformation from austenite to ferrite and cementite in the reaction heat during transformation. Obtained by multiplying the fraction of cementite and ferrite. The bainite transformation involves shear transformation, and the free energy change due to the shear transformation is calculated as 600 J / mol.
    [40] Experimentally, it is known that the constant temperature transformation of a solid phase over time at a constant temperature is represented by Equation 4 expressed as follows.
    [41] X = Xe [1-exp (-kt n )]
    [42] Where X is the phase transformation fraction at constant temperature t and the rate constant k and the time index n are the material constants that determine the phase transformation velocity. Xe is the thermodynamically stable fraction of phase at a given temperature and is determined from the equilibrium diagram by thermodynamic analysis. Most of the phase transformation behavior occurs during the cooling process, not at constant temperature, so a new variable, not only time but also temperature, is introduced as the phase transformation variable. The addition law is used to predict the non-constant transformation behavior.
    [43] In order to simulate the cooling behavior when the steel sheet exits the finishing mill and water cooled in the runout table, the one-dimensional abnormal heat transfer equation considering the phase transformation as shown in Equation 5 is considered.
    [44]
    [45] (Where ρ, Cp, kx and x mean density, specific heat, thermal conductivity and length in the thickness direction of the steel sheet)
    [46] Li is the latent heat when the initial austenite phase transforms into the i phase, and Xi is the fraction of the i phase. In the above formula, Cp uses the values obtained through thermodynamic calculations for the type and fraction of phases present at a specific temperature and time, and the values obtained through thermodynamic calculations as well as Li. In order to calculate the amount of transformation in the phase transformation model, temperature change data must be input, so the temperature prediction model and the phase transformation model are connected nonlinearly.
    [47] The configuration of the rigor model for the temperature calculation is shown in FIG.
    [48] The new learning technique will be described below.
    [49] The heat transfer coefficient for the number of upper and lower lines per bank is expressed as a function of the temperature of the steel sheet and the plate speed as shown in Equation 6.
    [50] h U = fk U (T S / T 0 ) a (Vp / Vp 0 ) b , h D = fk D (T S / T 0 ) c (Vp / Vp 0 ) d
    [51] (h U , h D : upper and lower heat transfer coefficients, k U , k D , a, b, c, d: constants determined when runout equipment is determined, T 0 , Vp 0 : reference for normalization Board speed, T S : Surface temperature of steel plate, f: Learning coefficient that changes according to facility conditions and environment)
    [52] Equation 6 is input as the boundary condition of the exact temperature calculation model. The learning coefficient f varies only with the plant situation and environment, regardless of the steel grade, thickness, target finish temperature, and target winding temperature. Therefore, if the learning method is used, the learning division is not necessary, and thus the definition of the first chapter of the set change is eliminated, and thus the reduction in the hitting temperature of the first chapter of the set change defined in the existing winding temperature control model can be prevented. In addition, the rigorous temperature calculation model has the advantage of predicting the phase transformation history in addition to the coiling temperature.
    [53] Using the learning method and the strict temperature model, a proper water pattern is derived to set the water level. Next, feed forward control is performed by reflecting the cooling control model of the runout table, and the feed quantity is controlled by performing feedback control to compensate for the difference in the target winding temperature during the feedforward control. When the coil production is finished, the learning coefficient of Equation 6 is obtained by using the performance finishing rolling temperature, the performance winding temperature, and the plate speed at this time, and prepare for the next steel sheet to be worked on.
    [54] 3 is a flowchart of the cooling control method.
    [55] Hereinafter, the present invention will be described in detail through examples.
    [56] ( Example)
    [57] As shown in Figure 1 in the order of finishing mill, runout table and the winding machine, the thickness of the hot rolling equipment installed in the thermometer between the rear end of the hot-rolling mill and the front end of the winder is 3.0mm and the target FDT is 880 o C, When the target CT was hot rolled to SS400 at 620 ° C for a certain amount, the thickness was changed to 4.0mm, width 1100mm, and the target CT was changed to SPFH2 at 580 ° C. The results of control by the cooling control model are shown in FIG. 4. As shown in Figure 4 it can be seen that the cooling control method using the method of the present invention is very effective to greatly increase the winding temperature hit degree of the first set change.
    [58] As described above, the present invention, unlike the existing method, by removing the definition of the first chapter of the set change by increasing the winding temperature hit degree of the first set change defined in the existing model, it is very excellent in improving the poor take-up temperature and the overall take-up temperature hit ratio It works. In addition, the strict temperature model can predict the phase transformation history on the runout table, and it is very effective in analyzing the change in the cooling capacity of the runout table by observing the change of a single learning coefficient.
    权利要求:
    Claims (2)
    [1" claim-type="Currently amended] In the method of controlling the temperature of the hot rolled steel sheet in the hot rolling equipment arranged in the order of the finish rolling mill, run out table and the winding machine, the thermometer between the rear end of the hot finishing rolling mill and the front end of the winding machine,
    a. Obtaining a learning coefficient of the new learning method using the performance finishing rolling temperature, the performance cooling exit temperature, and the mailing speed of the previous field;
    b. Calculating a cooling exit temperature by inputting an arbitrary virtual jet pattern from a target finish rolling temperature of the steel sheet by using an exact temperature calculation formula;
    c. Comparing the calculated cooling exit temperature with a target cooling exit temperature to change the virtual water discharge pattern;
    d. Finding the optimum water pattern to minimize the difference between the target cooling exit temperature and the calculated cooling exit temperature by repeating steps b and c;
    e. Feed forward control of the winding temperature;
    f. Controlling a water supply amount by performing feedback control to compensate for a difference between a target cooling exit temperature during the feedforward control;
    g. Obtaining a new learning coefficient using the final finish rolling temperature, the actual cooling exit temperature, and the sheet speed, and preparing for the next hot rolled steel sheet; Winding temperature control method of the hot rolled sheet through learning considering the phase transformation, characterized in that consisting of.
    [2" claim-type="Currently amended] According to claim 1, The process of calculating the exact temperature from the target finish rolling temperature of the steel sheet,
    h. Calculating equilibrium diagrams, specific heat, and transformation calorific value of each phase as a function of temperature through thermodynamic calculation of the composition of the steel sheet;
    i. Determining the fraction of each phase by calculating the phase transformation speed based on the equilibrium diagram obtained from the thermodynamic calculation and the temperature history calculated in step c;
    j. calculating a temperature based on the phase transformation fraction calculated in step b and the specific heat and transformation calorific value calculated in step a;
    k. Step b and c are nonlinearly connected so that it is calculated repeatedly; Winding temperature control method of the hot rolled sheet through learning considering the phase transformation, characterized in that consisting of.
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    同族专利:
    公开号 | 公开日
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    法律状态:
    2000-12-26|Application filed by 이구택, 주식회사 포스코
    2000-12-26|Priority to KR1020000082160A
    2002-07-04|Publication of KR20020052723A
    优先权:
    申请号 | 申请日 | 专利标题
    KR1020000082160A|KR20020052723A|2000-12-26|2000-12-26|coiling temperature control method of hot strip using learning method|
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