Improved self-learning method for threading speed of hot continuous rolling unit
By adopting a segmented self-learning method and limiting the amount of strip biting speed in the hot strip mill, the production instability caused by traditional self-learning was solved, more stable strip threading speed control was achieved, and the stability and consistency of production were improved.
Patent Information
- Application Number
- CN202511060160.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-30
- Publication Date
- 2025-11-28
AI Technical Summary
Traditional hot strip mill self-learning methods for strip threading speed can easily cause system instability and lead to production instability when the speed drop compensation is unreasonable or the L2 setting value is inaccurate.
A segmented self-learning method is adopted. By setting different self-learning dead zone thresholds and introducing a limit on the speed of steel biting and looping quantity, the looper self-learning quantity is calculated to avoid production instability caused by traditional self-learning.
It improves the production stability of the hot strip mill by accurately correcting the threading speed through self-learning, ensuring the stability and consistency of the production process.
Smart Images
Figure CN121028524A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of industrial automation, in particular to an improved hot tandem mill threading speed self-learning method. BACKGROUND
[0002] In hot tandem rolling, the process automation L2 level sets the reduction, speed and interstand loop angle of each stand during production according to the process requirements and the original size of the slab through model calculation, and the L1 level controls the corresponding equipment action according to the set value issued by the second level. The traditional short-term self-learning is to linearly calculate the compensation amount of the set value of the next piece of strip of the same specification according to the deviation between the actual loop angle value and the set angle value of the loop when the actual speed of the last piece of strip is restored after the speed drop during the biting of the strip. This method is prone to cause false learning and lead to system instability when the speed drop compensation is unreasonable or the L2 set value is inaccurate. SUMMARY
[0003] To solve the above technical problems existing in the prior art, an improved hot tandem mill threading speed self-learning method is provided. The technical solution is as follows:
[0004] An improved hot tandem mill threading speed self-learning method, the method comprising:
[0005] S1, when the stand F(i+1) bites the strip, start calculating the loop amount L between the stands F(i) and F(i+1) due to the speed drop of the F(i+1) rolling mill during the biting of the strip dyn(i+1) ;
[0006] S2, when the actual speed of the F(i+1) stand is restored to the L2 set value V thr(i+1) , detect the actual angle θ act(i) of the loop LP(i) and calculate the deviation Δθ act(i) between the actual angle θ set(i) and the set angle θ i of the loop LP(i);
[0007] S3, when |L dyn(i+1) |>L0, it is determined that the speed drop compensation is not suitable, and an alarm is given, and the threading speed self-learning amount Δβ i (n) of the F(i) stand of the current piece of strip Strip(n) is 0; wherein L0 is a speed drop loop amount threshold value allowed for self-learning;
[0008] S4, when |L dyn(i+1) |≤L0, and when M1≤Δθ i ≤M2, the threading speed self-learning amount Δβ i (n) of the F(i) stand of the current piece of strip is 0;
[0009] When |L dyn(i+1)When |≤L0, and when Δθ i When M2 > Δβ i (n) is Kp i ×Δθ i ;
[0010] When |L dyn(i+1) When |≤L0, and when Δθ i When <M1, Δβ i (n) is Kn i ×Δθ i ;
[0011] Where M1 is the lower limit deviation threshold for what is considered a reasonable setting angle, and M2 is the upper limit deviation threshold for what is considered a reasonable setting angle; Kp i Kn is the velocity self-learning gain at high angles. i The velocity self-learning gain at low angles;
[0012] S5. Calculate the self-learning value Δβ of the F(i) frame threading speed for this piece of steel. i (n) The total self-learning quantity β accumulated in rack F(i) i (n+1);
[0013] S6. When rolling strip(n+1) of the same specification in the next block, adjust the setting speed of the F(i) stand for threading.
[0014] The amount of nesting in S1
[0015] Among them, V act(i+1) V represents the actual speed of the F(i+1) rolling mill. thr(i+1) The set threading speed for the F(i+1) mill.
[0016] The deviation Δθ in S2 i For: Δθ i =θ act(i) -θ set(i) .
[0017] In S3, L0 is related to the main drive speed characteristics of the rolling mill, and L0 is 30mm.
[0018] In S4, M1 ranges from -3.5 to -2, and M2 ranges from 2 to 3; Kp i The value is -0.05, Kn i The value is -0.05; Δθ i When <M1, the angle is low, Δθ i When M2 is greater than M2, the angle is higher.
[0019] The total self-learning quantity β of rack F(i) in S5 i (n+1) is:
[0020] β i (n+1)=β i (n)+Δβ i (n)
[0021] Wherein, β i (n) is the cumulative self-learning quantity of the previous n pieces of steel.
[0022] When the strip strip(n+1) of the same specification of the next piece is rolled in S6, the set strip speed V thr(i) (n+1) of the F(i) rack needs to be superimposed with the total self-learning quantity β thr_cal(i) (n+1) of the F(i) rack on the basis of the theoretically calculated strip speed V i (n+1) as the final strip speed:
[0023] V thr(i) (n+1)=V thr_cal(i) (n+1)+β i (n+1)。
[0024] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects:
[0025] In the above scheme, the segmented self-learning method of using different self-learning dead zone thresholds above and below the set value is used to calculate the loop self-learning quantity, and the steel biting speed reduction set quantity is introduced as a learning limit, which can avoid the production instability caused by traditional self-learning, thereby improving the stability of production. BRIEF DESCRIPTION OF DRAWINGS
[0026] In order to more clearly illustrate the technical scheme in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.
[0027] Figure 1 is a flow chart of an improved hot continuous rolling mill strip speed self-learning method provided by the embodiments of the present application. DETAILED DESCRIPTION
[0028] The technical scheme in the present application will be described below in conjunction with the drawings.
[0029] In the embodiments of the present application, the words such as "example", "for example" and the like are used to represent an example, an illustration, or a description. Any embodiment or design solution described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design solutions. Rather, the word "example" is used to present a concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.
[0030] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed thereby are consistent when the difference is not emphasized.
[0031] In order to make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail in combination with the drawings and specific embodiments.
[0032] The embodiments of the present application provide an improved strip threading speed self-learning method of a hot continuous rolling mill train. Figure 1 As shown in the flow chart of the improved strip threading speed self-learning method of the hot continuous rolling mill train, the method can include the following steps:
[0033] S1, when the mill F(i+1) bites the steel, start to calculate the loop L between the mills F(i) and F(i+1) due to the speed reduction of the mill F(i+1) when biting the steel dyn(i+1) ;
[0034] S2, when the actual speed of the mill F(i+1) is restored to the set value V thr(i+1) of L2, detect the actual angle θ act(i) of the loop LP(i) and calculate the deviation Δθ act(i) between the actual angle θ set(i) and the set angle θ i of the loop LP(i);
[0035] S3, when |L dyn(i+1) |>L0, it is determined that the speed reduction compensation given is not suitable, and an alarm is given, and the strip threading speed self-learning amount Δβ i (n) of the mill F(i) for the current strip Strip(n) is 0; wherein L0 is a speed reduction loop threshold value allowing self-learning;
[0036] S4, when |L dyn(i+1) |≤L0, and when M1≤Δθ i ≤M2, the strip threading speed self-learning amount Δβ i (n) of the mill F(i) for the current strip Strip(n) is 0;
[0037] When |L dyn(i+1) |≤L0, and when Δθ i >M2, Δβ i(n) is Kp i ×Δθ i ;
[0038] When |L dyn(i+1) When |≤L0, and when Δθ i When <M1, Δβ i (n) is Kn i ×Δθ i ;
[0039] Where M1 is the lower limit deviation threshold for what is considered a reasonable setting angle, and M2 is the upper limit deviation threshold for what is considered a reasonable setting angle; Kp i Kn is the velocity self-learning gain at high angles. i The velocity self-learning gain at low angles;
[0040] S5. Calculate the self-learning value Δβ of the F(i) frame threading speed for this piece of steel. i (n) The total self-learning quantity β accumulated in rack F(i) i (n+1);
[0041] S6. When rolling strip(n+1) of the same specification in the next block, adjust the setting speed of the F(i) stand for threading.
[0042] The amount of nesting in S1
[0043] Among them, V act(i+1) V represents the actual speed of the F(i+1) mill. thr(i+1) The set threading speed for the F(i+1) mill.
[0044] The deviation Δθ in S2 i For: Δθ i =θ act(i) -θ set(i) .
[0045] In S3, L0 is related to the main drive speed characteristics of the rolling mill, and L0 is 30mm.
[0046] In S4, M1 ranges from -3.5 to -2, and M2 ranges from 2 to 3; Kp i The value is -0.05, Kn i The value is -0.05.
[0047] The total self-learning quantity β of rack F(i) in S5 i (n+1) is:
[0048] β i (n+1)=β i (n)+Δβ i (n)
[0049] wherein, β i (n) is the cumulative self-learning amount of the previous n pieces of steel.
[0050] When the strip strip(n+1) of the same specification of the lower piece in S6 is rolled, the set strip speed V thr(i) (n+1) of the F(i) rack needs to be superimposed with the total self-learning amount β thr_cal(i) (n+1) of the F(i) rack on the basis of the theoretically calculated strip speed V i (n+1) as the final strip speed:
[0051] V thr(i) (n+1) = V thr_cal(i) (n+1) + β i (n+1).
[0052] In the specific application process, assuming that a certain specification is produced, the set loop set angle θ set(3) of the F3 and F4 racks of the finishing mill L2 is 18°, the strip speed of the F3 rack is 3 m / s, the loop amount L d of the F4 main machine is 28 mm when the speed of the F4 rack is restored to the set value after biting the steel, the allowed self-learning speed drop loop amount L0 is 30 mm, the actual value of the loop angle θ act(3) of the F4 rack is 27° when the speed of the F4 rack is restored to the set value, M1 is taken as -2°, M2 is taken as 3°, it is assumed that the current piece of steel is the first piece of opening rolling, the cumulative learning amount β i (n) = 0, then the height self-learning amount Δβ i (n) of the loop LP(3) of the current piece of steel is -0.05×(27-18) = -0.045;
[0053] Then when the next piece of steel billet of the same specification is rolled, the set strip speed V
[0054] V thr(3) (n+1) = V thr_cal(3) (n+1) + β i (n+1) = 3-0.045 = 2.955
[0055] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims
1. An improved method of threading speed self-learning for a hot tandem mill train, characterized by, The method comprises: S1, when the mill F(i+1) bites the steel, start to calculate the loop L between the stands F(i) and F(i+1) due to the speed drop of the mill F(i+1) when biting the steel dyn(i+1) ; S2, when the F(i+1) stand actual speed recovers to the L2 set value V thr(i+1) , the live roll LP(i) actual angle θ act(i) is detected, and the actual angle θ act(i) deviation Δθ set(i) from the LP(i) set angle θ i is calculated; S3, when |L dyn(i+1) |L0, it is determined that the speed drop compensation given is not appropriate, and an alarm is reported, and the F(i) rack strip(n) speed self-learning amount Δβ i (n) is 0; wherein, L0 is the speed drop set amount threshold value allowed to perform self-learning. S4, when |L dyn(i+1) |≤L0, and when M1≤Δθ i ≤M2, the F(i) rack belt speed self-learning amount Δβ i (n) is 0; when |L dyn(i+1) when Δθ i when Δβ i (n) is Kp i × Δθ i ; when |L dyn(i+1) when Δθ i when Δβ i (n) is Kn i × Δθ i ; wherein M1 is a lower limit deviation threshold value considered reasonable for the set angle, M2 is an upper limit deviation threshold value considered reasonable for the set angle; Kp i is a speed self-learning gain for when the angle is high, Kn i is a speed self-learning gain for when the angle is low; S5, calculate the F(i) rack strip speed self-learning amount Δβ of the steel block i (n) accumulate to the F(i) rack total self-learning amount β i (n+1); S6, when the strip steel Strip(n+1) of the same specification of the lower block is rolled, the set strip speed of the F(i) rack is corrected.
2. The improved hot tandem mill entry speed self-learning method according to claim 1, characterized by, The S1 inner sleeve where V act(i+1) is the actual speed of the F(i+1) rolling mill, V thr(i+1) is the set threading speed of the F(i+1) rolling mill.
3. The improved hot tandem mill strip speed self-learning method according to claim 1, characterized in that, The deviation Δθ in S2 is: i Δθ = θ i - θ act(i) - θ set(i) .
4. The improved hot tandem mill threading speed self-learning method according to claim 1, characterized by, In the S3, L0 is related to the main transmission speed characteristic of the rolling mill, and L0 is 30 mm.
5. The improved hot tandem mill strip speed self-learning method according to claim 1, characterized by, In the S4, M1 ranges from -3.5 to -2, and M2 ranges from 2 to 3; Kp i takes a value of -0.05, and Kn i takes a value of -0.
05.
6. The improved hot tandem mill strip speed self-learning method according to claim 1, characterized by, The total self-learning quantity β of the F(i) rack in S5 i (n+1) is: β i (n+1) = β i (n) + Δβ i (n) where β i (n) is the cumulative self-learning amount of n pieces of steel that have been rolled previously.
7. The improved hot tandem mill strip speed self-learning method according to claim 1, characterized by, When the strip strip (n+1) of the same specification as the S6 middle and lower block is rolled, the set strip speed V of the F(i) stand thr(i) (n+1) needs to superimpose the total self-learning amount β of the F(i) stand on the basis of the theoretically calculated strip speed V thr_cal(i) i (n+1) as the final strip speed: V thr(i) (n+1) = V thr_cal(i) (n+1) + β i (n+1).