Optimization method of hot strip rolling production process control system

A technology of production process control and optimization method, applied in rolling force/roll gap control, metal rolling, metal rolling, etc., can solve problems such as production practical considerations, achieve improved control accuracy, control system optimization, considerable economical The effect of value and utility value

Active Publication Date: 2013-04-10

WUHAN IRON & STEEL ENG TECH GROUP

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Problems solved by technology

The traditional method of improving the accuracy of the rolling force model of hot continuous rolling focuses on proposing various load distribution methods and self-learning in a certain part of the model, such as the setting of finishing rolling. Although these methods have achieved good...

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Method used

The present invention has realized the optimization of strip steel hot continuous rolling production process control system by a large amount of rolling history data sampling fitting analysis, and to the optimization of hot continuous rolling rolling force model coefficient, has effecti...

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Abstract

The invention relates to an optimization method of a hot strip rolling production process control system, which belongs to the technical field of hot strip rolling control. The method comprises the following steps of: data collection, de-noising processing, coefficient optimization, verification test, on-line application and the like. According to the coefficient optimization step, through correlation analysis of influence between model coefficients and parameters, the model coefficients are fit, an algorithm error precision is selected, and when a calculation error is less than the algorithm error precision, the coefficient optimization step is stopped to obtain new model coefficients and corresponding offsets. According to the optimization method, through sampling and carrying out fitting analysis on a large number of steel rolling historical data, and optimizing hot continuous rolling force model coefficients, the optimization of the hot strip rolling production process control system is realized, the hot strip rolling production process control accuracy is effectively improved, and considerable economic value and practical value are achieved.

Application Domain

Roll force/gap control deviceMeasuring devices +1

Technology Topic

Coefficient optimizationDe noising +12

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Examples

Experimental program(1)

Example Embodiment

[0034] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] like figure 1 As shown, step a, collecting data. During the rolling process, the historical data collected by the primary machine is stored in the data center, and the .mat files (data files readable by MATLAB) are generated through classification collection, and tt_data_*.mat files are generated. In order to make the data representative, if it is a coefficient for a steel family or steel grade, at least 2000 coils of the same steel family or steel grade must be taken, generally 4000 coils; if the global coefficient optimization is selected, there is no need to distinguish Steel grade.

[0036] Step b. Data denoising. In order to improve the reliability of the data, the selected data is preprocessed, and simple statistical methods are used to eliminate the noise in the data, that is, unreliable data, such as data after manual intervention, inaccurate measurement data, etc.

[0037] Step c, coefficient optimization. For model analysis, analyze the influence between model coefficients and parameters through correlation, fit the model coefficients, and select an algorithm error accuracy. When the calculation error is less than this error accuracy, it stops, and a new model coefficient can be obtained at this time. and corresponding deviations;

[0038] Step c', perform data test on the newly obtained model coefficients, and use the new data set to verify the new model. If the test results meet the preset standards, choose to accept the new model coefficients, otherwise, another data set should be selected for re-simulation. combine;

[0039] Step d. Apply the trusted new model coefficients to the online model.

[0040] The main function of the invention is to optimize the coefficients of the mathematical model of hot continuous rolling. The adopted system is Windows application software based on MATLAB, which is mainly composed of two parts: a HP Proliant DL 580 server and a mathematical model optimization module. The HP server should be configured with Windows Server 2003 or above operating system, Microsoft Sql Server2005 database, Microsoft Visual Studio 2005 compilation environment, and MATLAB R2007B software. The main function of the mathematical model optimization module is to optimize the model coefficients to the optimal values that meet the site conditions and product conditions by fitting and regressing the historical steel rolling process data. like figure 2 As shown, the coefficient k2 of the finishing F1-F7 stands is selected, and the new rolling force model coefficient multiplier is obtained through an iterative algorithm.

[0041] like image 3 As shown, the relationship between the rolling force and its model coefficients was analyzed through the experimental data, and the rolling force coefficient was optimized by regression analysis.

[0042] Assumptions: Take the historical data of n pieces of steel, the same steel type. Let the number of corresponding racks be s.

[0043] Step 1: Set k=1,

[0044] Step 2: Obtain the measured rolling force P,

[0045] P=(p ij ) s×n p ij : The measured rolling force of the i-th stand (i=1,…,s) and the j-th piece of steel (j=1,…,n),

[0046] Calculate the predicted force P' (the predicted force will change with the perturbation of the parameter) P'=F-(F1-F2),

[0047] F: total reduction force (fixed value), F1: bending force on each side (variable value), F2: balance bending force (fixed value),

[0048] Step 3: Calculate the increment △ of the coefficient multiplier,

[0049] Coefficient multiplier Current=1+△ (△ is the increment of the multiplier, obtained by regression analysis)

[0050] △=(X′*X) -1 ×(X′*Y)X: (2n-1)×s matrix Y: (2n-1)×1 matrix,

[0051] X: The error between the predicted value after disturbance and the original predicted value,

[0052] X=(S(i)-S(i-1))′/δ, δ=0.01,

[0053] S=[0.5*PNf diff(PNf)], PNf:s×n matrix, diff(PNf) is the difference matrix of PNf is s×(n-1) matrix,

[0054] PNf = p 11 ′ p 1 ‾ . . . p 1 n ′ p 1 ‾ . . . . . . . . . p s 1 ′ p s ‾ . . . p s 1 ′ p s ‾ s × n p ij ′: the force of the i-th rack on the j-th steel,

[0055] : The i-th stand, the average value of the measured rolling force of n pieces of steel

[0056] Y=(T-S)′,

[0057] T=[0.5*MNf diff(MNf)],

[0058] MNf = p 11 p 1 ‾ . . . p 1 n p 1 ‾ . . . . . . . . . p s 1 p s ‾ . . . p sn p s ‾ s × n p ij : The measured rolling force of the i-th stand and the j-th piece of steel,

[0059] Step 4: If Min(T-S) 2δ, yes, go to step 5, otherwise, go to step 7;

[0060] Step 5: k=k+1;

[0061] Step 6: Determine whether k is greater than N, if so, return to step 2 to step 4, where N is an integer ≤ 5, otherwise, go to step 7;

[0062] Step 7: Return the function value of △ and get the coefficient multiplier Current;

[0063] Step 8: Multiply (Current+1)/2 by the rolling force coefficient to obtain a new rolling force model coefficient.

[0064] The experimental verification of the above methods shows that the predicted value of rolling force after the coefficient optimization is in good agreement with the actual value of the head, and the actual value of the outlet thickness is in good agreement with the target value.

[0065] The present invention realizes the optimization of the production process control system of hot tandem strip rolling by sampling, fitting and analyzing a large amount of historical data of steel rolling, and optimizing the rolling force model coefficient of hot tandem rolling, and effectively improves the production process of hot tandem strip rolling. Control precision, with considerable economic value and practical value. The invention can also be applied to the temperature model, power model and plate shape model of hot continuous rolling.

[0066] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should The technical solutions can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be included in the scope of the claims of the present invention.

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Classification and recommendation of technical efficacy words

Optimal control system
High control precision

Optimization method of hot strip rolling production process control system

AI-Extracted Technical Summary

Problems solved by technology

Method used

Abstract

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Description & Claims & Application Information

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