CN114417236A - Data evaluation-based quality optimization control method for steel rolled product - Google Patents

Data evaluation-based quality optimization control method for steel rolled product Download PDF

Info

Publication number
CN114417236A
CN114417236A CN202210052368.7A CN202210052368A CN114417236A CN 114417236 A CN114417236 A CN 114417236A CN 202210052368 A CN202210052368 A CN 202210052368A CN 114417236 A CN114417236 A CN 114417236A
Authority
CN
China
Prior art keywords
value
rolling
calculated
data
model
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.)
Granted
Application number
CN202210052368.7A
Other languages
Chinese (zh)
Other versions
CN114417236B (en
Inventor
彭文
刘烁
陈世译
武文腾
李越朗
韩璧保
张鸿源
孙杰
邸洪双
张殿华
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northeastern University China
Original Assignee
Northeastern University China
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Northeastern University China filed Critical Northeastern University China
Priority to CN202210052368.7A priority Critical patent/CN114417236B/en
Publication of CN114417236A publication Critical patent/CN114417236A/en
Application granted granted Critical
Publication of CN114417236B publication Critical patent/CN114417236B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B3/00—Rolling materials of special alloys so far as the composition of the alloy requires or permits special rolling methods or sequences ; Rolling of aluminium, copper, zinc or other non-ferrous metals
    • B21B3/02—Rolling special iron alloys, e.g. stainless steel
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Computational Mathematics (AREA)
  • Operations Research (AREA)
  • Mathematical Optimization (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Mathematical Analysis (AREA)
  • Data Mining & Analysis (AREA)
  • Game Theory and Decision Science (AREA)
  • Algebra (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Mechanical Engineering (AREA)
  • Manufacturing & Machinery (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Control Of Metal Rolling (AREA)

Abstract

The invention belongs to the technical field of steel rolling, and particularly relates to a quality optimization control method of a steel rolled product based on data evaluation. According to the method, data evaluation is carried out on the samples on the basis through data acquisition and processing of multiple samples, and the samples which are applicable to model correction and have the highest accuracy are screened out and used as source data; the correction of the model core parameters, such as roll gap, rolling force and rolling speed, directly related to the product quality in the steel production process is realized through model recalculation; in the correction process, the smooth coefficient is optimally selected, the correction efficiency is guaranteed, meanwhile, the prediction precision of the model is improved, the prediction result of the model is enabled to be faster and more accurate to be close to an actual measurement value, the control effect of quality indexes such as the thickness of steel products is improved, and the purpose of improving the high-quality control of the products in the same batch is finally achieved.

Description

Data evaluation-based quality optimization control method for steel rolled product
Technical Field
The invention belongs to the technical field of steel rolling, and particularly relates to a quality optimization control method of a steel rolled product based on data evaluation.
Background
The thickness of the product is an important quality index in the steel production process, the high-precision head thickness control precision is the basis of the full-length thickness control of the strip steel, and the high-precision head thickness is beneficial to quickly entering the subsequent thickness automatic control (AGC) process, so that the full-length thickness control precision of the strip steel is ensured. In the actual production process of the plate strip, a thickness gauge on a rolling line is generally installed at the outlet of a last rack, due to the limitations of installation environment, investment cost and the like, the thickness gauge is generally not installed between serial racks, so that the thickness of the head of the strip steel cannot be measured in real time, after measurement data are obtained, the head of the strip steel finishes the production process, and the thickness of the head of the strip steel is determined and cannot be corrected; if the head thickness is abnormal, head quality objections in a large length range can occur, and therefore the head control precision of the full length of the strip steel is influenced. If the adjustment is not carried out in time, the thickness control precision of the next strip steel is also influenced, thereby influencing the product quality of the whole production batch. In the actual production process, when the strip steel passes through the measuring instrument, the obtained actual detection data related to the thickness is collected and stored through the data acquisition system, model correction is carried out based on the actual measurement process data, key parameters in the model are optimized and adjusted, and the thickness control precision of the product can be improved. However, in the actual production process, the accuracy of the acquired data is easily affected by the threading stability, so that the quality of model correction is directly affected, and therefore, the data must be evaluated, and the production process data with the highest quality obtained by screening can be further used for model correction.
In the aspect of controlling the rolling thickness of the plate strip, a great deal of research work is done by predecessors; chinese patent CN101804420B proposes a method for controlling the finish rolling thickness in the production of hot rolled sheets, and an AGC series double-loop system is designed aiming at the process of entering closed-loop control to realize the AGC automatic roll gap control function; chinese patent CN104741388A proposes a hot continuous rolling finish rolling thickness control method, introduces Smith estimation compensation into a monitoring AGC control system, and uses a GM method to directly carry out soft measurement on a roll gap of a rolling mill, thereby obviously improving the response rolling speed, stability and control precision of the control system. According to the basic rolling theory, the high bud (hot working process, 2013,42(11) and 92-95) optimizes and corrects the thickness control model, and improves the thickness prediction precision. The Wangjian (Chinese and south university journal (natural science edition), 2014, 45(10), 3398 and 3407) uses a self-learning method to research a self-learning module of a preset model of a hot continuous rolling finishing mill set of a certain plant, improves the rolling force forecasting precision and provides a basis for thickness control; the influence of the sampling rule of the self-learning model of the thickness control system on the control precision of the head thickness is analyzed by Qianjin science (Henan metallurgy, 2019,27(153), 49-53), and the self-learning effect of the model is improved by modifying the time of the thickness self-learning head sampling.
In the research process, the thickness control precision is improved in an AGC system optimization mode, the thickness control can be implemented only according to the deviation of the measured value of the thickness after the strip steel passes through the thickness gauge and enters a thickness control closed loop, and the thickness precision control effect on the head of the strip steel is difficult to guarantee; the control of the head thickness is realized by a model correction mode, but in the traditional model correction process, the data collected in the production process are not screened and processed, the actually measured data in the production process is easily influenced by the external environment, larger deviation exists, the actual production state is difficult to reflect, if the data with larger error is used for model correction, the opposite effect can be generated, the thickness control precision caused by the method cannot be improved, but can be reduced, the model is finally not suitable for use, the thickness control effect is unstable, and the quality control of products is not used.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a data evaluation-based quality optimization control method for a steel rolling product, which is applied to the process of finish rolling of an intermediate steel billet to prepare a finished strip steel, and as shown in figure 1, the flow of the specific scheme is as follows:
step 1: and determining the number l of samples in the rolling process after threading is finished, and determining the number N of target sampling points contained in each sample.
The invention collects 1 sample in the threading process, and collects l samples in the rolling process after the threading is finished. Each sample contains N target sampling points, the value of N being determined according to the dimensions of the intermediate billet of the steel product to be rolled and the dimensions of the finished strip. Specifically, the value of N may be determined by:
Figure BDA0003474779710000021
the number l of samples of the rolling process after the threading is finished can be 4-8, and an optimal sample is selected from the samples in the subsequent steps and is calculated by using the data measured value.
Step 2: the strip steel starts a threading process through the frames in sequence, and actual measurement data generated in the production process are collected and stored:
step 2.1: acquiring and storing data of the head of the strip steel in the threading process:
the number of stands used for rolling is set to be n. Before the start of finish rolling, the model setting system calculates the rolling schedule, i.e., the set values of parameters such as reduction, rolling speed, roll gap, rolling force, etc., of each stand in accordance with the requirements of the production target values.
After that the threading and rolling process is started. The head of the strip steel passes through the No. 1 stand, the threading process begins, and the strip steel sequentially passes through the stands and the measuring instruments arranged at the stands along the rolling direction.
In the threading process, according to a fixed data sampling period, sequentially counting sampling points of the head of the strip steel after passing through a measuring instrument, and simultaneously collecting and recording actual measurement data of the sampling points measured by the measuring instrument; and when the number of the sampling points passing through the measuring instrument is equal to the number N of the target sampling points, calculating the average value of the collected measured data, and storing the average value of the measured data as a measured value as a measured data sample in the threading process.
In order to avoid the fluctuation of measured values caused by steel biting impact when strip steel enters each rack in the threading process, the first m (m can be 3 to 5) sampling points passing through a measuring instrument are not collected, but are recorded and stored from the m +1 sampling point, and the sample in the threading process contains N sampling points (namely the m +1 to the m + N sampling points).
And sequentially carrying out the acquisition and storage processes at each stand until the head of the strip steel passes through the last measuring instrument, and finishing the acquisition and storage processes at the last measuring instrument.
In the threading process, the collected and stored data at least comprises the rolling speed of the strip steel head passing through the measuring instruments at each stand in the threading process, and also comprises the roll gap and the rolling force at each stand, the loop angle between the stands and the like.
Step 2.2: acquiring and storing strip steel data in the rolling process after strip threading is finished:
the head of the strip steel passes through the last measuring instrument, finishes the threading process and data acquisition and storage in the threading process, and starts to acquire and store the actual measured data of the strip steel in the rolling process after the threading process is finished: counting the sampling points passing through the measuring instrument according to a fixed data sampling period, simultaneously collecting and recording the measured data of the sampling points measured by the measuring instrument, respectively calculating the average value of all the measured data in the current sample when the counting of the sampling points passing through the measuring instrument is equal to the number N of target sampling points, and storing the average value of the measured data in the current sample as a measured value, wherein the number of the samples is recorded as 1; and then, resetting the sampling point, restarting counting, repeating the sample number by +1, and so on until the data acquisition and storage process of the samples is completed.
In the rolling process after the threading is finished, the collected and stored data at least comprise the rolling speed, the roll gap, the rolling force and the loop angle of each stand, and the strip steel temperature, the strip steel thickness and the strip steel width of the outlet of the last stand.
And step 3: validity evaluation and screening of data in collected samples
Step 3.1: evaluating the validity of data collected during threading and after threading
If any data in the sample exceeds the range of the validity interval, turning to step 7, giving an alarm by the system, and ending the process; if all the data in the sample are within the validity interval, go to step 3.2.
Step 3.2: and (3) screening the optimal sample from the sample data I obtained in the step 2.2 to be used as a sample for calculation. The following methods can be used for screening the optimal sample:
screening the sample with the minimum sample data fluctuation degree as the optimal sample, wherein the sample data fluctuation degree deltaaimThe calculation formula of (a) is as follows:
Figure BDA0003474779710000031
selecting p data types as indexes for calculating the sample data fluctuation degree, preferably, adopting 4 data types of strip steel temperature, strip steel thickness, strip steel width and loop angle at each stand at the outlet of the last stand as indexes; deltajIs the fluctuation degree of the j-th index in the sample, wherein cact,j,kThe k-th measured value of the j-th index, caim,jA target value of the j index; mjThe number of data measured values corresponding to each index, e.g. the strip temperature at the outlet of the last standThe thickness of the strip steel and the width of the strip steel are respectively only 1 data measured value, and the number of the loops between the racks is n-1 due to the n racks, so that the index of the angle of the loops at each rack has n-1 data measured values.
And 4, step 4: determining a calculated value of a process parameter using data of the selected optimal sample
Step 4.1 determining calculated exit thicknesses for individual racks
Calculating the thickness of the outlet of each rack according to the thickness of the strip steel at the outlet of the last rack, the rolling speed at each rack and the angle of the loop in the optimal sample obtained in the step 3.2;
calculated value h for exit thickness at ith rack with i from 1 to n-1Calculation of iComprises the following steps:
Figure BDA0003474779710000041
in the formula (f)iIs the forward slip value of the ith rack, fnThe forward slip value of the last rack; the forward slip value can be calculated by the model setting system through a calculation method in the prior art.
hn、vnRespectively, the exit strip thickness and the rolling speed, v, at the last stand, i.e. the nth stand, of the optimal sampleiThe rolling speed measured value at the ith frame in the optimal sample is obtained; lθiAccording to the loop angle theta at the ith frame in the optimal sampleiThe length l of the strip steel between the ith frame and the (i + 1) th frame calculated by the measured valueθsAccording to the target loop angle thetasCalculating the target length of the strip steel from the ith frame to the (i + 1) th frame; calculated thickness h of the last rack outletCalculation of nBy direct introduction of hn。
In the method, the length l of the strip steel between two adjacent frames is calculated according to the angle theta of the loop between the two framesθThe method comprises the following steps:
as shown in FIG. 2, the length l of the strip between the two standsθThe distance AB between the highest point of the loop and the outlet of the previous machine frame, the highest point of the loop and the subsequent machine frameThe sum of the rack entry distances BC is not difficult to obtain from the schematic of fig. 2:
Figure BDA0003474779710000042
wherein L is1Is the horizontal distance, L, from the previous frame to the loop fulcrum2Is the height from the loop fulcrum to the rolling plane, L is the horizontal distance between the two frames, RLIs the loop arm length and r is the loop roll radius.
Step 4.2 determining the calculated values of the exit temperatures of the individual racks
Distributing the temperature deviation of the finish rolling outlet to each rack, and calculating the temperature value when the strip steel passes through each rack:
calculated value T of exit temperature of ith rackCalculation of iThe calculation formula is as follows:
Figure BDA0003474779710000043
in the formula, TiIs the rolling temperature set value, T, of the ith stand in the threading process of step 2.1Measured in factFor the strip temperature measurement, T, at the exit of the last stand in the optimal sampleTargetIs the temperature target value of the strip steel at the outlet of the last frame.
Step 4.3 determination of the width of each rack outlet
And (4) neglecting the widening in the finish rolling process, and taking the strip steel width measured value of the outlet of the last stand in the optimal sample as the outlet width value when the strip steel passes through each stand.
Step 4.4 determining the calculated value of the roll gap of each frame
Determining roll gap calculations at each stand using a bounce equation, roll gap calculations S at each standComputingThe calculation formula is as follows:
Figure BDA0003474779710000051
in the formula, hComputingAs desiredThe calculated outlet thickness at the rack is obtained from step 4.1; p0In order to zero the rolling force, M is the rigidity of the rolling mill, and is a value which is determined according to the performance of the rolling mill; pMeasured in factIs the rolling force measurement at the stand to be calculated in the optimal sample.
Step 4.5 determining the calculated rolling force of each stand
Calculating and determining the calculated value P of the rolling force of each stand according to the calculated value P of the outlet thickness of each stand, the calculated value of the outlet temperature of each stand, the measured value of the outlet width of each stand, the measured value of the rolling speed and the like in the steps 4.1 to 4.3Computing(ii) a In the prior art, there are various methods for calculating the rolling force based on these known conditions.
Preferably, the calculated rolling force value of each stand can be determined according to the Sims formula:
Pcomputing＝1.15σslcQPw/1000
In the formula: w is the width of the strip steel at the outlet of the stand to be calculated, mm, which is the width value of the outlet of each stand determined in the step 4.3;
σs-the deformation resistance at the frame to be calculated, MPa;
Figure BDA0003474779710000052
a1～a6-regression coefficients, the values of which depend on the steel grade;
t-the thermodynamic temperature at the gantry to be calculated, dimensionless,
Figure BDA0003474779710000053
TcomputingCalculating the outlet temperature of the rack to be calculated determined in the step 4.2;
Figure BDA0003474779710000054
rate of deformation, s-1，
Figure BDA0003474779710000055
Wherein v is the measured value of the rolling speed at the stand to be calculated in the optimal sample;
epsilon-engineering strain,%,
Figure BDA0003474779710000061
Δ h, the reduction of the rack to be calculated, mm, is the difference between the calculated value of the inlet thickness and the calculated value of the outlet thickness of the rack; for the 1 st frame, the inlet thickness is the initial thickness h of the strip steel0Then, the calculated inlet thickness of each rack is the calculated outlet thickness of the previous rack, and the calculated outlet thickness of each rack is obtained according to the step 4.1;
σ0-at a deformation temperature of 1000 ℃,
Figure BDA0003474779710000062
deformation resistance when epsilon is 0.4, MPa;
lcthe frame to be calculated takes into account the contact arc length after crushing, mm,
Figure BDA0003474779710000063
r' -roll flattening radius of the stand to be calculated, mm;
Figure BDA0003474779710000064
r-the roll radius of the stand to be calculated, mm, is a known quantity;
QP-the stress state influence factor of the gantry to be calculated:
Figure BDA0003474779710000065
hm-the average strip thickness, mm, is the average of the calculated values of the inlet thickness and the outlet thickness at the stand to be calculated;
b0～b4are regression coefficients associated with the gantry.
It should be noted that, in the above steps 4.4 and 4.5, for the convenience of description, the subscript i for distinguishing different stands is not added, but it is understood that the calculated values of the roll gap and the rolling force are calculated for each stand according to the calculated values, the measured values and the known parameters of each stand, respectively.
And 5: determining calculated values of correction coefficients of roll gap model, rolling force model and rolling speed model of each frame
Step 5.1: calculating roll gap model correction coefficient
Using the calculated roll gap S calculated in step 4.4ComputingStep 3.2 roll gap measurement S of the best sample obtained by screeningMeasured in factAnd calculating a roll gap model correction coefficient:
the roll gap model correction coefficient calculation formula is as follows:
Figure BDA0003474779710000066
step 5.2: calculating the correction coefficient of the rolling force model
Using the calculated value P of rolling force obtained in step 4.5ComputingAnd 3.2, screening the optimal sample to obtain a measured value P of the rolling forceMeasured in factAnd calculating a rolling force model correction coefficient:
the roll force model correction coefficient calculation formula is as follows:
Figure BDA0003474779710000071
step 5.3: calculating rolling speed model correction coefficient
Using the measured value v of the rolling speed in threading obtained in step 2.1Actual measurement-threading procedureAnd 3.2, calculating a rolling speed model correction coefficient according to the rolling speed measured value v in the optimal sample of the rolling process after the threading is finished
Figure BDA0003474779710000072
A calculation formula of the correction coefficient of the rolling speed model is as follows:
Figure BDA0003474779710000073
step 6: update of correction coefficients for each rack model
Step 6.1: calculating a smoothing coefficient alpha according to the deviation degree between the old value of the correction coefficient and the calculated value of the correction coefficient; the calculation formula is as follows:
Figure BDA0003474779710000074
in the formula: deltaoldCorrecting the old value of the coefficient for the model, including the old value of the model correction coefficient of the roll gap, the rolling force and the rolling speed
Figure BDA0003474779710000075
Figure BDA0003474779710000076
And
Figure BDA0003474779710000077
ΔcomputingCalculating the model correction coefficient calculated in the step 5.1-5.3, including
Figure BDA0003474779710000078
And
Figure BDA0003474779710000079
Δmaxthe maximum value of the model correction coefficient is the maximum value of the corresponding model correction coefficient, including roll gap, rolling force and rolling speed
Figure BDA00034747797100000710
Figure BDA00034747797100000711
And
Figure BDA00034747797100000712
Δminfor minimum values of corresponding model correction factors, including roll gap, rolling force, rolling speedMinimum value of model correction coefficient
Figure BDA00034747797100000713
And
Figure BDA00034747797100000714
the correction coefficients of roll gap, rolling force and rolling speed are all deltaComputing≥ΔmaxThen a isComputingReplace and become a new ΔmaxValue, if ΔComputing≤ΔminThen a isComputingReplace and become a new ΔminThe value is then used for the calculation of the smoothing coefficient alpha; omega is a proportionality coefficient, and can be 0.5-0.9.
Step 6.2: model correction coefficient new value calculation
The model coefficient is corrected by adopting a smoothing mode, and the new value delta of the model correction coefficient after smoothingnewCalculated as follows:
Δnew＝Δold+α·(Δcomputing-Δold)
Step 6.3: and transmitting the new value of the model correction coefficient obtained by calculation to a model setting system, adding the roll gap correction coefficient and the setting result of the model setting system to be used as a new roll gap setting value when the next strip steel is subjected to rolling schedule calculation, multiplying the rolling force correction coefficient and the model setting result to be used as a new rolling force setting value, and multiplying the rolling speed model correction coefficient and the model setting result to be used as a new rolling speed setting value for strip steel production.
The calculation and update process of the correction coefficients of the model of each rack in steps 6.1-6.3 can be seen in fig. 4.
For each rolling, the new value of the model correction coefficient of the last time is changed into the old value of the time, and the correction and the updating are carried out once by the method. The model correction coefficient and the maximum and minimum values of the model correction coefficient may be initially set to default values, for example, for a roll gap, the initial model correction coefficient value may be 0 as the initial value
Figure BDA0003474779710000081
Initially of
Figure BDA0003474779710000082
Can be respectively 0.05 and-0.05; for the rolling force and the rolling speed, the initial model correction coefficient is 1, the initial maximum value and the initial minimum value are 1.05 and 0.95 respectively, and then the model correction coefficient is continuously updated in each rolling process, and the rolling schedule is continuously optimized.
And 7: and (6) ending.
According to the method, data evaluation is carried out on the samples on the basis through data acquisition and processing of multiple samples, and the samples which are applicable to model correction and have the highest accuracy are screened out and used as source data; the correction of the model core parameters, such as roll gap, rolling force and rolling speed, directly related to the product quality in the steel production process is realized through model recalculation; in the correction process, the smooth coefficient is optimally selected, the correction efficiency is guaranteed, meanwhile, the prediction precision of the model is improved, the prediction result of the model is enabled to be faster and more accurate to be close to an actual measurement value, the control effect of quality indexes such as the thickness of steel products is improved, and the purpose of improving the high-quality control of the products in the same batch is finally achieved.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a schematic diagram of parameters involved in calculation of the length of strip steel between two adjacent frames.
FIG. 3 is a diagram showing the arrangement of the instruments in the finishing section in the strip rolling process according to the embodiment of the present invention.
FIG. 4 is a flow chart of the calculation of the correction coefficients of the model of each rack according to the present invention.
Fig. 5 is a diagram illustrating the effect of improving the thickness accuracy after the parameter setting is corrected according to the embodiment of the present invention.
Detailed Description
The following examples are given to illustrate the embodiments of the present invention:
this example optimizes the quality of the rolled product in the finish hot continuous rolling process of the type Q235A steel: the hot continuous rolling finish rolling area consists of 7 racks, the 7 th rack is the last rack, the radius of the roller is 380mm, and the distance between the racks is 5000 mm. As shown in FIG. 3, the strip 9 passes through 7 stands (the first stand in the figure) in sequence from the inlet of the finish rolling zone to the outlet of the finish rolling zone, and the threading process is completed. A temperature measuring instrument 1 and a temperature measuring instrument 6 are respectively arranged at an inlet and an outlet of a finish rolling area to measure the temperature of the strip steel, a width measuring instrument 7 is arranged at the outlet of the finish rolling area, namely the outlet of the last rack to measure the actual width of the strip steel, a thickness measuring instrument 8 is also arranged at the outlet of the finish rolling area to measure the actual thickness of the strip steel, a displacement sensor 3, a pressure sensor 4 and a rolling speed sensor 5 are arranged on each rack to respectively measure the roll gap, the rolling force and the roll linear speed (namely the rolling speed) when the strip steel passes through each rack, and an angle encoder is arranged on a loop 10 between adjacent racks to measure the angle of the loop in the production process; the outlet thickness gauge 8 is the last gauge.
The inlet temperature of the current product is 1020 ℃, the thickness of the finish rolling intermediate billet is 32.0mm, and the width of the intermediate billet is 1200 mm; the target value of the angle of the loop is 20 degrees, the target value of the temperature of the finished product strip steel is 880 ℃, the target value of the thickness is 2.00mm, and the target value of the width is 1200 mm; the data sampling period is 200ms, and the threading speed is 10 multiplied by 103mm/s。
The rolling schedule used in the production process is calculated by the model setting system, the production process of the strip steel is started according to the rolling schedule, and the rolling schedule is shown in table 1.
TABLE 1
Parameter name 1 machine frame 2 frame 3 machine frame 4 machine frame 5 machine frame 6 machine frame 7 machine frame
Inlet thickness/mm 32.00 16.65 9.44 5.69 3.90 3.01 2.38
Outlet thickness/mm 16.65 9.44 5.69 3.90 3.01 2.38 2.00
Reduction rate/%) 48.0 43.3 39.7 31.5 22.8 20.9 16.0
Rolling speed/m/s 1.05 2.01 3.32 5.01 6.58 8.42 10.00
Temperature/. degree.C 989.0 971.5 954.3 937.4 920.8 904.5 888.5
Gap/mm between rolls 19.43 11.89 7.93 5.30 3.52 2.78 1.88
Rolling force/kN 24692 22680 21449 16399 11053 10397 7298
Step 1: and determining that the number of samples in the threading process is 1, and the number of samples in the rolling process after the threading is finished is 5.
And determining the number of target sampling points contained in the sample for model correction according to the sizes of the intermediate blank and the finished strip of the strip.
The target number of sampling points contained in each sample is:
Figure BDA0003474779710000091
in order to avoid the fluctuation of measurement values caused by steel biting impact when strip steel enters each rack in the threading process, the first 5 sampling points are not collected, the sampling record is started from the 6 th sampling point and is stored, and each sample contains N which is 20 sampling points (namely, the 6 th sampling point to the 25 th sampling point).
Step 2: the strip steel starts a threading process sequentially through the frames, and actual measurement data generated in the production process are collected and stored.
Step 2.1: collection and storage of strip steel head data in threading process
The head of the strip steel passes through a machine frame 1, the threading process starts, and the strip steel sequentially passes through the machine frames and measuring instruments arranged at the machine frames along the rolling direction; according to a fixed data sampling period, sequentially counting sampling points of the head of the strip steel after passing through a measuring instrument, and simultaneously collecting and recording actual measurement data of the sampling points measured by the measuring instrument; when the number of the sampling points passing through the measuring instrument is equal to 20 of the number of the target sampling points, calculating the average value of the collected measured data, and storing the average value of the measured data as a measured value as a measured data sample in the threading process;
and sequentially carrying out the acquisition and storage processes at each stand until the head of the strip steel passes through the last measuring instrument, and finishing the acquisition and storage processes at the last measuring instrument.
The collected sample data of the steel strip head in the strip threading process are shown in table 2. Wherein the loop angle at the 1 st rack refers to the loop angle between the 1 st and 2 nd racks, and so on.
TABLE 2
Parameter name 1 machine frame 2 frame 3 machine frame 4 machine frame 5 machine frame 6 machine frame 7 machine frame
Rolling speed/m/s 1.11 2.02 3.34 5.00 6.56 8.43 10
Gap/mm between rolls 19.43 11.89 7.93 5.30 3.52 2.78 1.88
Rolling force/kN 24002 22122 20889 16291 11088 9982 7302
Angle/degree of movable sleeve 14.6 16.8 17.0 19.4 20.4 18.2 /
Step 2.2: strip steel data acquisition and storage after threading
After the head of the strip steel passes through the last measuring instrument (thickness gauge 8) of the outlet, the actual measurement data of the strip steel in the rolling process after the strip threading is finished is collected and stored: counting the sampling points passing through the measuring instrument according to a fixed data sampling period, simultaneously collecting and recording the measured data of the sampling points measured by the measuring instrument, respectively calculating the average value of all the measured data in the current sample when the counting of the sampling points passing through the measuring instrument is equal to the number N of target sampling points, and storing the average value of the measured data in the current sample as a measured value, wherein the number of the samples is recorded as 1; and then, resetting the sampling point, restarting counting, and counting the number of the samples by +1 until the data acquisition and storage process of 5 samples is completed. The collected 5 sample data of the strip steel in the rolling process after the threading is completed are shown in table 3.
TABLE 3
Figure BDA0003474779710000101
Figure BDA0003474779710000111
And step 3: validity evaluation and screening of data in collected samples
Step 3.1: evaluating the validity of data collected during threading and after threading
If any data in the sample exceeds the range of the validity interval, turning to step 7, giving an alarm by the system, and ending the process; if all the data in the sample are within the validity interval, go to step 3.2.
The range of the validity interval of each process parameter is shown in table 4, and it can be seen that all data of the sample are within the range of the validity interval.
TABLE 4
Figure BDA0003474779710000112
Figure BDA0003474779710000121
Step 3.2: screening an optimal sample from the 5 sample data obtained in the step 2.2 to be used as a sample for calculation; the specific method is to screen the sample with the minimum sample data fluctuation degree as the optimal sample, and the sample data fluctuation degree deltaaimThe calculation formula of (a) is as follows:
Figure BDA0003474779710000122
wherein, p data types are selected as indexes for calculating the fluctuation degree of sample data, and the embodiment adopts the strip steel temperature at the outlet of the last rack,Taking the thickness of the strip steel, the width of the strip steel and 4 data types of the loop angles at all the racks as indexes; deltajIs the fluctuation degree of the j-th index in the sample, wherein cact,j,kThe k-th measured value of the j-th index, caim,jA target value of the j index; mjFor the number of data measured values corresponding to each index, only 1 data measured value is provided for each of the strip temperature, the strip thickness and the strip width at the outlet of the last stand, while since the embodiment has 7 stands and 6 corresponding loops are provided between the stands, the index of the loop angle between the stands has 6 data measured values.
In this embodiment, the data fluctuation degree calculation results of 5 samples are shown in table 5, and the data fluctuation degree corresponding to the sample 5 is 0.018, which is smaller than that of other sample data fluctuation programs, and is selected as the optimal sample.
TABLE 5
Sample number 1 2 3 4 5
Sample data fluctuation degree 0.159 0.112 0.080 0.044 0.018
And 4, step 4: determining a calculated value of a process parameter using data of the selected optimal sample
Step 4.1 determining calculated exit thicknesses for individual racks
Calculating the thickness of the outlet of each stand according to the thickness of the strip steel at the outlet of the last stand, the rolling speed at each stand and the angle of the loop in the optimal sample obtained in the step 3.2; calculation of the exit thickness h for the ith gantry having an i of 1 to 6Calculation of iComprises the following steps:
Figure BDA0003474779710000123
in the formula (f)iIs the forward slip value of the ith rack, f7The forward slip value of the last frame is obtained by calculation according to a model setting system; according to Table 3, h7＝2.00mm，v7＝10.00m/s；viThe rolling speed measured value at the ith frame in the optimal sample is obtained; lθiAccording to the loop angle theta at the ith frame in the optimal sampleiThe length l of the strip steel between the ith frame and the (i + 1) th frame calculated by the measured value20°The target length of the strip steel between the ith frame and the (i + 1) th frame is calculated according to the target loop angle of 20 degrees. Calculated thickness h of the last rack outletCalculation, 7By direct introduction of h7＝2.00mm。
The structure of the loop between the frames and the corresponding parameters are shown in fig. 2. The calculated length of the strip steel between the adjacent frames is shown in table 6:
TABLE 6
Loop number 1 2 3 4 5 6
Length of strip/mm 5007.1 5008.1 5007.4 5008.1 5007.8 5007.7
The specific calculation method of the strip steel length between the adjacent racks comprises the following steps that when the angle of the loop is theta, the strip steel length between the adjacent racks can be calculated according to the geometrical relationship:
Figure BDA0003474779710000131
taking the calculated outlet thickness of the 1 st rack in this embodiment as an example, when the loop angle between the 1 st rack and the 2 nd rack is 19.5 °, the strip steel length between the racks is:
Figure BDA0003474779710000132
when the target loop angle is 20.0 degrees, the length of the strip steel between the frames is as follows:
Figure BDA0003474779710000133
as can be seen from Table 3, v1The calculated 1 st rack exit thickness is 1.15m/s as:
Figure BDA0003474779710000134
step 4.2 determining the calculated values of the exit temperatures of the individual racks
Distributing the temperature deviation of the finish rolling outlet to each rack, and calculating the temperature value when the strip steel passes through each rack; calculated value T of exit temperature of ith rackCalculation of iThe calculation formula is as follows:
Figure BDA0003474779710000135
in the formula, TiIs the rolling temperature set value, T, of the ith stand in the threading process of step 2.1Measured in factThe strip temperature measurement at the exit of the last stand in the optimal sample (sample 5) is 877 ℃ C, T as shown in Table 3TargetThe final stand exit strip temperature target is 880 c in this example. The number n of the racks is 7.
Taking the first frame as an example, according to Table 1, T1The calculated value of the first strip steel outlet temperature of the frame is 989.0 degrees centigrade:
Figure BDA0003474779710000141
step 4.3 determination of the width of each rack outlet
Neglecting the widening in the finish rolling process, and taking the measured value of the finish rolling outlet width in the optimal sample 5 as a calculated value of the outlet width when the strip steel passes through each rack; therefore, the width values of the strip steel outlets of all the racks are as follows:
wcomputing＝wMeasured in fact＝1205mm
Step 4.4 determining the calculated value of the roll gap of each frame
Determining machines using a bounce equationCalculated roll gap at the stand, calculated roll gap at each stand SComputingThe calculation formula is as follows:
Figure BDA0003474779710000142
in the formula, hComputingCalculating the outlet thickness of the rack to be calculated, and obtaining the calculated value from the step 4.1; p0For zero adjustment of rolling force, P in this embodiment08000kN, and M is the mill rigidity, in this embodiment, 6000 kN/mm; pMeasured in factThe rolling force measurement values at the stands to be calculated in the optimum sample are, in this embodiment, the rolling force measurement values of each stand of sample 5 in table 3.
Taking the first frame as an example, the calculated roll gap of the frame is as follows:
Figure BDA0003474779710000143
step 4.5 determining the calculated rolling force of each stand
According to the Sims formula, the calculated rolling force value of each stand is determined, and the calculated rolling force value P of each standComputingComprises the following steps:
Pcomputing＝1.15σslcQPw/1000
In the formula: w is the width of the strip steel at the outlet of the stand to be calculated, mm, which is the width value of the outlet of each stand determined in the step 4.3;
σs-the deformation resistance at the frame to be calculated, MPa;
Figure BDA0003474779710000144
a1～a6-regression coefficients, the values of which depend on the steel grade;
t-the thermodynamic temperature at the gantry to be calculated, dimensionless,
Figure BDA0003474779710000151
TcomputingCalculating the outlet temperature of the rack to be calculated determined in the step 4.2;
Figure BDA0003474779710000152
rate of deformation, s-1，
Figure BDA0003474779710000153
Wherein v is the measured value of the rolling speed at the stand to be calculated in the optimal sample;
epsilon-engineering strain,%,
Figure BDA0003474779710000154
Δ h, the reduction of the rack to be calculated, mm, is the difference between the calculated value of the inlet thickness and the calculated value of the outlet thickness of the rack; for the 1 st frame, the inlet thickness is the initial thickness h of the strip steel0I.e. the thickness of the finish-rolled intermediate slab; then, the calculated inlet thickness value of each rack is the calculated outlet thickness value of the previous rack, and the calculated outlet thickness value of each rack is obtained according to the step 4.1;
σ0-at a deformation temperature of 1000 ℃,
Figure BDA0003474779710000155
deformation resistance when epsilon is 0.4, MPa; 150.6MPa for the steel grade of this example;
lcthe frame to be calculated takes into account the contact arc length after crushing, mm,
Figure BDA0003474779710000156
r' -roll flattening radius of the stand to be calculated, mm;
Figure BDA0003474779710000157
r-the roll radius of the stand to be calculated, mm;
QPresponse of rack to be calculatedForce state influence coefficient:
Figure BDA0003474779710000158
hm-the average strip thickness, mm, is the average of the calculated values of the inlet thickness and the outlet thickness at the stand to be calculated;
b0～b4are regression coefficients associated with the gantry.
For the steel grade and the stand in this example, the values of the regression coefficients are shown in the following table:
regression coefficient a1 a2 a3 a4 a5 a6
Value taking 2.878 3.665 0.1861 -0.1216 0.3795 1.402
Regression coefficient b0 b1 b2 b3 b4
Value taking 0.8049 -0.3393 0.2488 0.0393 0.0732
According to the steps 4.1-4.5, the calculated values of the process parameters are shown in table 7:
TABLE 7
Parameter name 1 machine frame 2 frame 3 machine frame 4 machine frame 5 machine frame 6 machine frame 7 machine frame
Calculated inlet thickness/mm 32.00 16.73 9.34 5.70 3.85 2.99 2.35
Calculated outlet thickness/mm 16.73 9.34 5.70 3.85 2.99 2.35 2.00
Calculated temperature/. degree.C 988.6 970.6 953.0 935.7 918.7 901.9 885.5
Calculated width/mm 1205 1205 1205 1205 1205 1205 1205
Calculated roll gap/mm 19.45 11.75 7.87 5.29 3.52 2.69 1.92
Calculated rolling force/kN 23002 24238 20071 17201 11020 9982 7487
And 5: determining calculated values of correction coefficients of roll gap model, rolling force model and rolling speed model of each frame
Step 5.1: calculating roll gap model correction coefficient
Using the calculated roll gap S calculated in step 4.4ComputingStep 3.2 roll gap measurement S of the best sample obtained by screeningMeasured in factAnd calculating a roll gap model correction coefficient:
the roll gap model correction coefficient calculation formula is as follows:
Figure BDA0003474779710000161
taking the 1 st frame as an example, the roll gap model correction coefficient is
Figure BDA0003474779710000162
The roll gap model correction coefficient calculation values at the positions of the frames are as follows:
Figure BDA0003474779710000163
step 5.2: calculating the correction coefficient of the rolling force model
Using the calculated value P of rolling force obtained in step 4.5ComputingAnd 3.2, screening the optimal sample to obtain a measured value P of the rolling forceMeasured in factAnd calculating a rolling force model correction coefficient:
the roll force model correction coefficient calculation formula is as follows:
Figure BDA0003474779710000164
taking the 1 st stand as an example, the rolling force model correction coefficient is
Figure BDA0003474779710000165
The calculated value of the correction coefficient of the rolling force model is as follows:
Figure BDA0003474779710000171
step 5.3: calculating rolling speed model correction coefficient
Using the measured value v of the rolling speed in threading obtained in step 2.1Actual measurement-threading procedure(see table 2) and the rolling speed measurement v (see table 3) in the optimal sample of the rolling process after threading obtained in step 3.2, the rolling speed model correction factor is calculated
Figure BDA0003474779710000172
Rolling speed model correction coefficient calculation formula：
Figure BDA0003474779710000173
Taking the 1 st stand as an example, the rolling speed model correction coefficient is
Figure BDA0003474779710000174
The calculated value of the correction coefficient of the rolling speed model is as follows:
Figure BDA0003474779710000175
step 6: model modification coefficient update
Step 6.1: and calculating a smoothing coefficient according to the old value of the correction coefficient and the calculated value of the correction coefficient, wherein the calculation formula is as follows:
Figure BDA0003474779710000176
in the formula: deltaoldFor model correction of old values of coefficients, respectively
Figure BDA0003474779710000177
And
Figure BDA0003474779710000178
ΔcomputingCalculated values of the model correction coefficients calculated in steps 5.1-5.3, respectively representing
Figure BDA0003474779710000179
And
Figure BDA00034747797100001710
Δmaxrespectively represent the maximum values of the corresponding model correction coefficients
Figure BDA00034747797100001711
And
Figure BDA00034747797100001712
Δminrespectively represent the minimum value of the corresponding model correction coefficient
Figure BDA00034747797100001713
And
Figure BDA00034747797100001714
for all three correction coefficients, aComputing≥ΔmaxThen a isComputingReplace and become a new ΔmaxA value; if ΔComputing≤ΔminThen a isComputingReplace and become a new ΔminA value; omega is a proportionality coefficient; taking omega as 0.70;
the old value of the roll gap model correction coefficient in this example
Figure BDA00034747797100001715
The old value of the correction coefficient of the rolling force model in this embodiment
Figure BDA00034747797100001716
In this embodiment, the old value of the correction coefficient of the rolling speed model
Figure BDA00034747797100001717
Taking the smoothing coefficient of the 1 st stand rolling force correction coefficient as an example,
Figure BDA00034747797100001718
calculated value of 1.06, greater than
Figure BDA00034747797100001719
The roll force smoothing coefficient of the 1 st stand thus calculated is:
Figure BDA0003474779710000181
the calculation results of the smoothing coefficients of the roll gaps, the rolling force and the rolling speed of each frame are as follows:
parameter name 1 machine frame 2 frame 3 machine frame 4 machine frame 5 machine frame 6 machine frame 7 machine frame
Coefficient of roll gap smoothness 0.14 0.51 0.41 0.07 0.00 0.45 0.28
Rolling force smoothing coefficient 0.38 0.41 0.35 0.21 0.07 0.07 0.00
Rolling speed smoothing coefficient 0.21 0.14 0.00 0.00 0.00 0.00 0.00
Step 6.2: model correction coefficient new value calculation
The model coefficient is corrected by adopting a smoothing mode, and the new value delta of the smoothed model coefficientnewCalculated as follows:
Δnew＝Δold+α·(Δcomputing-Δold)
Taking the new value of the rolling force correction coefficient of the 1 st stand as an example:
the new value of the correction coefficient obtained by calculation is as follows: α ═ 1.00+0.38 × (1.06-1.00) ═ 1.02
The results of the calculation of the new values of the smoothed model coefficients are shown in the following table:
Figure BDA0003474779710000182
step 6.3: and transmitting the new value of the model correction coefficient obtained by calculation to a model setting system, adding the roll gap correction coefficient and the model setting result as a new roll gap setting value when the next strip steel is subjected to rolling schedule calculation, multiplying the rolling force correction coefficient and the model setting result as a new rolling force setting value, and multiplying the rolling speed and rolling speed model correction coefficient and the model setting result as a new rolling speed setting value for strip steel production.
And 7: and (6) ending.
Fig. 5 shows the measurement results of the deviation between the final stand exit thickness and the target value before and after correcting the set values of the parameters such as the roll gap, the rolling force, and the rolling speed by the method of this embodiment. Compared with the prior correction, the thickness deviation value of the previous sampling point, namely the sampling point corresponding to the head of the strip steel is greatly reduced, and the high-precision head thickness is beneficial to entering the subsequent thickness automatic control (AGC) process quickly, so that the thickness control precision of the full length of the strip steel is ensured.

Claims (10)

1. A quality optimization control method of steel rolling products based on data evaluation is characterized by comprising the following steps:
step 1: determining the number l of samples in the rolling process after threading is finished, and determining the number N of target sampling points contained in each sample;
step 2: the strip steel starts a threading process through all the racks in sequence, and actual measurement data generated in the production process are collected and stored;
and step 3: evaluating and screening the effectiveness of the data in the collected sample;
and 4, step 4: determining a process parameter calculation value by using the data of the screened optimal sample;
and 5: determining the calculated values of the correction coefficients of the roll gap model, the rolling force model and the rolling speed model of each frame;
step 6: updating the model correction coefficient of each frame;
and 7: and (6) ending.
2. The method for the optimal control of the quality of steel rolling products based on data evaluation according to claim 1, characterized by comprising the following steps:
step 1: determining the number l of samples in the rolling process after threading is finished, and determining the number N of target sampling points contained in each sample;
step 2: the strip steel starts a threading process through the frames in sequence, and actual measurement data generated in the production process are collected and stored:
step 2.1: acquiring and storing data of the head of the strip steel in the threading process:
the number of the frames adopted for rolling is n;
the head of the strip steel passes through a machine frame 1, the threading process starts, and the strip steel sequentially passes through the machine frames and measuring instruments arranged at the machine frames along the rolling direction;
according to a fixed data sampling period, sequentially counting sampling points of the head of the strip steel after passing through a measuring instrument, and simultaneously collecting and recording actual measurement data of the sampling points measured by the measuring instrument; when the number of sampling points passing through the measuring instrument is equal to the number N of target sampling points, calculating the average value of the collected measured data, and storing the average value of the measured data as a measured value as a measured data sample in the threading process;
the collecting and storing processes are sequentially carried out at all the racks until the head of the strip steel passes through the last measuring instrument, and the collecting and storing processes are also finished at the last measuring instrument;
in the threading process, the collected and stored data comprise the rolling speed of each rack in the threading process;
step 2.2: acquiring and storing strip steel data in the rolling process after strip threading is finished:
after the head of the strip steel passes through the last measuring instrument, the actual measurement data of the strip steel in the rolling process after the strip threading is finished is collected and stored: counting the sampling points passing through the measuring instrument according to a fixed data sampling period, simultaneously collecting and recording the measured data of the sampling points measured by the measuring instrument, respectively calculating the average value of all the measured data in the current sample when the counting of the sampling points passing through the measuring instrument is equal to the number N of target sampling points, and storing the average value of the measured data in the current sample as a measured value, wherein the number of the samples is recorded as 1; then, the sampling point is cleared, counting is restarted, and the number of samples is plus 1 until the data acquisition and storage process of the samples is completed;
in the rolling process after threading, the collected and stored data comprise the rolling speed, the roll gap, the rolling force and the loop angle of each stand, and the strip steel temperature, the strip steel thickness and the strip steel width of the outlet of the last stand;
and step 3: validity evaluation and screening of data in collected samples
Step 3.1: evaluating the validity of data collected during threading and after threading
If any data in the sample exceeds the range of the validity interval, turning to step 7; if all the data in the sample are within the validity interval range, then go to step 3.2;
step 3.2: screening an optimal sample from the sample data obtained in the step 2.2 to be used as a sample for calculation;
and 4, step 4: determining a calculated value of a process parameter using data of the selected optimal sample
Step 4.1 determining calculated exit thicknesses for individual racks
Calculating the thickness of the outlet of each stand according to the thickness of the strip steel at the outlet of the last stand, the rolling speed at each stand and the angle of the loop in the optimal sample obtained in the step 3.2; calculation of exit thickness h for ith gantry having i from 1 to n-1Calculation of iComprises the following steps:
Figure FDA0003474779700000021
in the formula (f)iIs the forward slip value of the ith rack, fnThe forward slip value of the last rack; h isn、vnRespectively the strip thickness and the rolling speed measured value v of the outlet of the last stand in the optimal sampleiThe rolling speed measured value at the ith frame in the optimal sample is obtained;
Figure FDA0003474779700000022
according to the loop angle theta at the ith frame in the optimal sampleiThe length l of the strip steel between the ith frame and the (i + 1) th frame calculated by the measured valueθsAccording to the target loop angle thetasCalculating the target length of the strip steel from the ith frame to the (i + 1) th frame;
calculated thickness h of the last rack outletCalculation of nBy direct introduction of hn；
Step 4.2 determining the calculated values of the exit temperatures of the individual racks
Calculated value T of exit temperature of ith rackCalculation of iThe calculation formula is as follows:
Figure FDA0003474779700000023
in the formula, TiIs the rolling temperature set value, T, of the ith stand in the threading process of step 2.1Measured in factFor the strip temperature measurement, T, at the exit of the last stand in the optimal sampleTargetThe temperature target value of the strip steel at the outlet of the last frame is obtained;
step 4.3 determination of the width of each rack outlet
Taking the strip steel width measurement value of the last rack outlet in the optimal sample as the outlet width value of the strip steel passing through each rack;
step 4.4 determining the calculated value of the roll gap of each frame
Determining roll gap calculations at each stand using a bounce equation, roll gap calculations S at each standComputingThe calculation formula is as follows:
Figure FDA0003474779700000031
in the formula, hComputingCalculating the outlet thickness of the rack to be calculated, and obtaining the calculated value from the step 4.1; p0Zero rolling force is set, and M is the rigidity of the rolling mill; pMeasured in factMeasuring the rolling force of the stand to be calculated in the optimal sample;
step 4.5 determining the calculated rolling force of each stand
Calculating and determining the calculated value P of the rolling force of each rack according to the calculated value of the outlet thickness of each rack, the calculated value of the outlet temperature of each rack and the calculated value of the outlet width of each rack from the step 4.1 to the step 4.3Computing；
And 5: determining calculated values of correction coefficients of roll gap model, rolling force model and rolling speed model of each frame
Step 5.1: calculating roll gap model correction coefficient
Using the calculated roll gap S calculated in step 4.4ComputingStep 3.2 roll gap measurement S of the best sample obtained by screeningMeasured in factAnd calculating a roll gap model correction coefficient:
the roll gap model correction coefficient calculation formula is as follows:
Figure FDA0003474779700000032
step 5.2: calculating the correction coefficient of the rolling force model
Using the calculated value P of rolling force obtained in step 4.5ComputingAnd 3.2, screening the optimal sample to obtain a measured value P of the rolling forceMeasured in factAnd calculating a rolling force model correction coefficient:
the roll force model correction coefficient calculation formula is as follows:
Figure FDA0003474779700000033
step 5.3: calculating rolling speed model correction coefficient
Using the measured value v of the rolling speed in threading obtained in step 2.1Actual measurement-threading procedureAnd 3.2, calculating a rolling speed model correction coefficient according to the rolling speed measured value v in the optimal sample of the rolling process after the threading is finished
Figure FDA0003474779700000034
A calculation formula of the correction coefficient of the rolling speed model is as follows:
Figure FDA0003474779700000035
step 6: update of correction coefficients for each rack model
Step 6.1: calculating a smoothing coefficient according to the deviation degree between the old value of the correction coefficient and the calculated value of the correction coefficient; the calculation formula is as follows:
Figure FDA0003474779700000041
in the formula: deltaoldCorrecting the old value of the coefficient for the model, including the old value of the model correction coefficient of the roll gap, the rolling force and the rolling speed
Figure FDA0003474779700000042
Figure FDA0003474779700000043
And
Figure FDA0003474779700000044
ΔcomputingCalculating the model correction coefficient calculated in the step 5.1-5.3, including
Figure FDA0003474779700000045
And
Figure FDA0003474779700000046
Δmaxthe maximum value of the model correction coefficient is the maximum value of the corresponding model correction coefficient, including roll gap, rolling force and rolling speed
Figure FDA0003474779700000047
Figure FDA0003474779700000048
And
Figure FDA0003474779700000049
Δminfor the corresponding minimum value of model correction coefficient, including the minimum value of model correction coefficient of roll gap, rolling force and rolling speed
Figure FDA00034747797000000410
And
Figure FDA00034747797000000411
for all three correction coefficients, there are: if ΔComputing≥ΔmaxThen a isComputingReplace and become a new ΔmaxA value; if ΔComputing≤ΔminThen a isComputingReplace and become a new ΔminA value; omega is a proportionality coefficient;
step 6.2: model correction coefficient new value calculation
The model coefficient is corrected by adopting a smoothing mode, and the new value delta of the model correction coefficient after smoothingnewCalculated as follows:
Δnew＝Δold+α·(Δcomputing-Δold)
Step 6.3: transmitting the new value of the model correction coefficient obtained by calculation to a model setting system, adding the roll gap correction coefficient and the model setting result as a new roll gap setting value when the next strip steel is subjected to rolling schedule calculation, multiplying the rolling force correction coefficient and the model setting result as a new rolling force setting value, and multiplying the rolling speed model correction coefficient and the model setting result as a new rolling speed setting value for strip steel production;
and 7: and (6) ending.
3. The method for controlling the quality optimization of steel rolling products based on data evaluation according to claim 1, wherein in the step 1, the number N of target sampling points contained in each sample is determined as follows:
Figure FDA00034747797000000412
4. the method for controlling the quality optimization of the steel rolling product based on the data evaluation as claimed in claim 2, wherein in the step 2.1, the first m sampling points are not collected, and the collection and the storage are started from the m +1 th sampling point, and the samples contain N sampling points in total, namely the m +1 th sampling point to the m + N th sampling point.
5. The method for optimizing control of quality of steel rolling products based on data evaluation according to claim 2, wherein the method for screening optimal samples in step 3.2 is as follows:
screening the sample with the minimum sample data fluctuation degree as the optimal sample, wherein the sample data fluctuation degree deltaaimThe calculation formula of (a) is as follows:
Figure FDA0003474779700000051
selecting p data types as indexes for calculating the fluctuation degree of the sample data; deltajIs the fluctuation degree of the j-th index in the sample, wherein cact,j,kThe k-th measured value of the j-th index, caim,jA target value of the j index; mjThe number of data measurement values corresponding to each index.
6. The method of claim 5, wherein 4 data types of the strip temperature, the strip thickness, the strip width, and the loop angle at each stand at the exit of the last stand are selected as indexes, the number of data measurement values corresponding to the strip temperature, the strip thickness, and the strip width at the exit of the last stand is 1, and the number of data measurement values corresponding to the loop angle at each stand is n-1.
7. The method for optimizing control of quality of steel rolling products based on data evaluation as claimed in claim 2, wherein in step 4.1, the length l of the strip steel between two stands is calculated according to the loop angle θ between the two standsθThe method comprises the following steps:
Figure FDA0003474779700000052
whereinL1Is the horizontal distance, L, from the previous frame to the loop fulcrum2Is the height from the loop fulcrum to the rolling plane, L is the horizontal distance between the two frames, RLIs the loop arm length and r is the loop roll radius.
8. The method for optimizing control of quality of steel rolling products based on data evaluation according to claim 4, wherein the first m sampling points are not collected, wherein m is 3-5.
9. The method for controlling the quality of a steel rolled product based on data evaluation according to claim 2, wherein the number of samples/of the rolling process after the threading is completed is 4-8.
10. The method for optimizing control of quality of steel rolled product based on data evaluation as claimed in claim 2 wherein in step 4.5, calculated value of rolling force P for each stand is determinedComputingThe method comprises the following steps:
and determining the calculated rolling force value of each stand according to the Sims formula:
Pcomputing＝1.15σslcQPw/1000
In the formula: w is the width of the strip steel at the outlet of the stand to be calculated, mm, which is the width value of the outlet of each stand determined in the step 4.3; sigmas-the deformation resistance at the frame to be calculated, MPa;
Figure FDA0003474779700000061
a1～a6-regression coefficients, the values of which depend on the steel grade;
t-the thermodynamic temperature at the gantry to be calculated, dimensionless,
Figure FDA0003474779700000062
TcomputingOutlet temperature of desired computer rack determined for step 4.2Calculating a value;
Figure FDA0003474779700000063
rate of deformation, s-1，
Figure FDA0003474779700000064
Wherein v is the measured value of the rolling speed at the stand to be calculated in the optimal sample;
epsilon-engineering strain,%,
Figure FDA0003474779700000065
Δ h, the reduction of the rack to be calculated, mm, is the difference between the calculated value of the inlet thickness and the calculated value of the outlet thickness of the rack; for the 1 st frame, the inlet thickness is the initial thickness h of the strip steel0Then, the calculated inlet thickness of each rack is the calculated outlet thickness of the previous rack, and the calculated outlet thickness of each rack is obtained according to the step 4.1;
σ0-at a deformation temperature of 1000 ℃,
Figure FDA0003474779700000066
deformation resistance when epsilon is 0.4, MPa;
lcthe frame to be calculated takes into account the contact arc length after crushing, mm,
Figure FDA0003474779700000067
r' -roll flattening radius of the stand to be calculated, mm;
Figure FDA0003474779700000068
r-the roll radius of the stand to be calculated, mm;
QP-the stress state influence factor of the gantry to be calculated:
Figure FDA0003474779700000069
hm-the average strip thickness, mm, is the average of the calculated values of the inlet thickness and the outlet thickness at the stand to be calculated;
b0～b4are regression coefficients.
CN202210052368.7A 2022-01-18 2022-01-18 Steel rolling product quality optimization control method based on data evaluation Active CN114417236B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210052368.7A CN114417236B (en) 2022-01-18 2022-01-18 Steel rolling product quality optimization control method based on data evaluation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210052368.7A CN114417236B (en) 2022-01-18 2022-01-18 Steel rolling product quality optimization control method based on data evaluation

Publications (2)

Publication Number Publication Date
CN114417236A true CN114417236A (en) 2022-04-29
CN114417236B CN114417236B (en) 2024-09-27

Family

ID=81273906

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210052368.7A Active CN114417236B (en) 2022-01-18 2022-01-18 Steel rolling product quality optimization control method based on data evaluation

Country Status (1)

Country Link
CN (1) CN114417236B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20040044325A (en) * 2002-11-21 2004-05-28 주식회사 포스코 Method for Controlling Thickness of Front Portion of Hot-Rolled Steel Sheet
CN107363105A (en) * 2017-07-20 2017-11-21 首钢集团有限公司 A kind of control method and device of hot rolled coil head thickness
CN112893484A (en) * 2021-01-18 2021-06-04 东北大学 Hot continuous rolling force self-setting method based on grey correlation extraction
CN113102516A (en) * 2021-03-05 2021-07-13 东北大学 Hot continuous rolling strip steel head width prediction method integrating rolling mechanism and deep learning

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20040044325A (en) * 2002-11-21 2004-05-28 주식회사 포스코 Method for Controlling Thickness of Front Portion of Hot-Rolled Steel Sheet
CN107363105A (en) * 2017-07-20 2017-11-21 首钢集团有限公司 A kind of control method and device of hot rolled coil head thickness
CN112893484A (en) * 2021-01-18 2021-06-04 东北大学 Hot continuous rolling force self-setting method based on grey correlation extraction
CN113102516A (en) * 2021-03-05 2021-07-13 东北大学 Hot continuous rolling strip steel head width prediction method integrating rolling mechanism and deep learning

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
钱京学;毛一标;郭德福;: "热轧无取向硅钢首块钢头部厚度超差原因及控制对策", 河南冶金, no. 01, 15 February 2019 (2019-02-15) *

Also Published As

Publication number Publication date
CN114417236B (en) 2024-09-27

Similar Documents

Publication Publication Date Title
JP5685208B2 (en) Control device for hot rolling mill for thin plate and control method for hot rolling mill for thin plate
CN103286142A (en) Dynamic roll gap compensation method during cold continuous rolling flying gauge control
CN104785543B (en) A kind of hot-strip crown feedback control method based on moving average filter
CN109226277B (en) Method for controlling flexible rolling thickness of non-ferrous metal
CN111666653B (en) Online judging method for setting precision of strip steel finish rolling model
CN104324948B (en) A kind of rougher of hot strip mill process rolled piece width control method
CN104998911A (en) Roll gap control method for strip steel subjected to finish rolling of hot continuous rolling mill
CN103934278A (en) Hot-rolling and finish-rolling strip steel thickness control method
CN110802114A (en) Rolling force method for cold-rolled plate strip
CN114818456B (en) Prediction method and optimization method for full-length deformation resistance of cold continuous rolling strip steel
CN107008758A (en) Cold-strip steel high accuracy plate shape surface roughness On-Line Control Method and system
CN112845613A (en) Thickness control method and device for hot-rolled strip steel and terminal equipment
CN112474826B (en) Method for calculating thickness distribution of hot continuous rolling intermediate billet
CN110947772B (en) Data corresponding method for hot continuous rolling process
JP6438753B2 (en) Tandem rolling mill control device and tandem rolling mill control method
CN116108932A (en) Method for establishing fusion model of steel production process data and mechanism
CN108817100B (en) A method of judging that certain breast roller causes strip exit thickness to fluctuate
CN110614274B (en) Improved second flow thickness control method for single-stand reversible rolling mill
CN108655188B (en) Plate shape measurement error compensation method
CN114417236A (en) Data evaluation-based quality optimization control method for steel rolled product
CZ20041031A3 (en) Method for optimizing the production technology of rolled products
CN111482466A (en) Method for setting acceleration of rolling mill
Dian-yao et al. Self-learning and its application to laminar cooling model of hot rolled strip
CN108655176B (en) Self-adaptive calculation method of cold rolling forward slip model for stable rolling
CN112246880B (en) Twenty-high rolling mill strip shape optimization control method based on feedforward-middle shifting compensation

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
GR01 Patent grant
GR01 Patent grant