CN103469137B - Hot galvanizing coating thickness dynamic specification-changing presetting control method - Google Patents
Hot galvanizing coating thickness dynamic specification-changing presetting control method Download PDFInfo
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- 229910000831 Steel Inorganic materials 0.000 claims abstract description 77
- 239000010959 steel Substances 0.000 claims abstract description 76
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 claims abstract description 43
- 239000011701 zinc Substances 0.000 claims abstract description 43
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Abstract
The invention provides a hot galvanizing coating thickness dynamic variable specification preset control method, which is characterized in that a principal component analysis method is applied, a strip steel speed, an air knife pressure and a distance from an air knife to a strip steel surface are used as main influence factors, a coating thickness preset model is established by adopting a logarithm space least square fitting algorithm, long-term self-adaption and short-term self-adaption adjustment of coating thickness are carried out aiming at the influence of variable speed, target thickness and strip steel substrate thickness on the coating thickness in the dynamic variable specification process, a Kalman filtering algorithm and a smooth filtering algorithm are applied in thickness control to carry out online updating calculation on coating thickness model parameters, and the preset precision of the model parameters is improved. The control problems of overlarge coating thickness deviation and coating thickness detection lag caused by the change of the target coating thickness, the strip steel substrate thickness and the strip steel speed are solved, the automatic preset control of the thickness of the zinc coating is realized, the thickness deviation of the zinc coating is reduced, the thickness control precision of the zinc coating is improved, the zinc consumption is reduced, and the zinc coating cost is saved.
Description
Technical Field
The invention belongs to the field of automatic control, and particularly relates to a preset control method for thickness dynamic specification change of a continuous hot-dip galvanizing coating.
Background
In the whole hot galvanizing production process, the thickness control of a zinc layer is a very important link, and the quality of the control directly influences the quality of a product. The thicker plating layer not only wastes raw materials such as zinc ingots, but also influences the service performance of products; the thinner plating layer can affect the corrosion resistance of the product, thereby providing higher technical requirements and control difficulty for the zinc layer thickness control technology. Factors influencing the thickness of the zinc layer are complex, have target characteristics of nonlinearity, time-varying large hysteresis and multivariable, and cause low control accuracy of the system. Especially when the dynamic specification or strip steel speed changes, the air knife distance changes greatly, and the air knife pressure follow-up variation is also large, so that the non-uniform galvanized layer thickness and the large galvanized thickness deviation of discontinuous products before and after the strip steel welding seam are easily caused, the product quality is seriously influenced, and a large amount of zinc raw materials are wasted.
At present, advanced foreign enterprises generally adopt a preset control method, realize dynamic specification change by means of empirical data and a plating thickness preset model, but only aim at a specific production line, so that the method is not universal. The coating thickness control technology in the process of changing specifications of domestic hot galvanizing is relatively laggard, and the main control modes of various large steel mills such as Wu steel, Bao steel, saddle steel and the like still adopt manual operation combined with the traditional PID control. In a hot galvanizing production line, the thickness of a coating is based on actual data measured by a thickness gauge, in order to avoid the influence of temperature and improve the detection precision, the thickness gauge is often installed at a position 100 plus 200m behind a zinc pot, and because an automatic control system is not available, the thickness gauge is completely adjusted manually, and the fluctuation of process parameters is large, when an operator finds that the actual thickness has an error from a target value from the display of the thickness of the coating and then revises the actual thickness again, an unqualified coating product with the thickness of 100 plus 200m is produced. Even in steady production, in order to meet the demand for continuous production, it is sometimes necessary to artificially change the current steady state and make a transition to the steady state under another control target condition. Because the transition change of the air knife control parameters can seriously affect the coating precision and even can cause the result of zinc liquid splashing, the realization of stable and quick automatic transition control during the process of changing the coating thickness into the specification is urgently needed.
Patent application No. 200910131086.0 discloses a BP neural network control method of coating thickness and a process implemented on a PLC. However, the thickness is controlled by a neural network method, which belongs to feedback control and has a certain limitation in the rapidity of control action. The thickness deviation of the coating can not be corrected quickly, the thickness deviation can be detected at intervals, and finally the thickness of the zinc layer of a long section of strip steel is too thick or too thin.
Japanese laid-open patent No. JP10273766 discloses a plating thickness control method for controlling the air knife pressure based on the target plating thickness in a plating thickness setting model equation, which is too complicated in structure and difficult to realize in-line application for determining up to 6 optimal coefficients. The closed-loop control method for the thickness of the zinc layer of the hot-galvanized wire is provided in the introduction document Japan of the closed-loop control for the thickness of the zinc layer of the hot-galvanized wire by Liuhailong and the like, but the closed-loop control method is feedback control based on deviation adjustment, the control action is not timely enough, certain hysteresis exists, the control performance is over dependent on the accuracy of a model, and the control effect is not good when the calculation error of the model is large.
Disclosure of Invention
The invention provides a preset control method for the thickness dynamic specification change of a hot-dip galvanized coating, which aims to solve the control problems caused by overlarge coating thickness deviation and coating thickness detection lag due to the target thickness of the coating, the thickness of a strip steel substrate and the speed change of strip steel in the zinc layer thickness control transition process, thereby improving the response speed of air knife control, effectively inhibiting the influence of interference on the coating thickness in the zinc coating transition process, improving the uniformity of the thickness and reducing the thickness deviation of a hot-dip galvanized coating.
Therefore, the technical solution adopted by the invention is as follows:
a hot dip galvanizing coating thickness dynamic variable specification presetting control method is characterized in that a principal component analysis method is applied, the strip steel speed, the air knife pressure and the distance from the air knife to the strip steel surface are used as main influence factors of the coating thickness, a coating thickness presetting model is established by adopting a logarithmic space least square fitting algorithm, and the presetting control precision of the zinc coating thickness is improved; aiming at the influence of the variable speed, the target thickness and the thickness interference of the strip steel substrate on the thickness of a plating layer, which often occur in the dynamic specification-changing process of the galvanizing production, the long-term self-adaption and short-term self-adaption adjustment of the thickness of the plating layer are carried out, and the self-adaption capability of a galvanizing layer thickness model is improved; in the control of the coating thickness, a Kalman filtering algorithm and a smoothing filtering algorithm are applied to update and calculate the coating thickness model parameters on line, so that the preset precision of the model parameters is improved, and the accuracy and the stability of the control of the coating thickness are ensured; the method comprises the following specific steps:
1. establishing a preset model of plating thickness
The factors influencing the thickness of the galvanized layer are: the blowing pressure of the nozzle, the blowing angle, the distance between the nozzle and the strip steel, the height of the nozzle from the zinc liquid level, the nozzle gap, the strip steel speed, the substrate characteristics, the temperature and the chemical composition of the zinc liquid and the like. However, from the perspective of an actual galvanizing production process, some factors have relatively small influence on the thickness of a plating layer, and three factors, namely air knife pressure P, strip steel linear velocity V and distance D between an air knife and the surface of strip steel, which have large influence on the thickness CW of the plating layer are selected from the perspective of principal component analysis and online application of a model, so that a plating layer thickness preset model is established:
in the formula: k is a model constant; x is the number of1、x2、x3Is a model coefficient and can be obtained by regression analysis of actual measurement data.
According to the actual process parameters, the linear preset model equation of the nonlinear model conversion logarithmic space is as follows:
lnP＝lnK+x1lnV+x2lnD+x3lnCW(2)
2. long-term self-adaptive adjustment method for preset model parameters
Due to the complexity of the galvanizing production process, the model precision has certain limitation, and when system process parameters, such as target plating thickness and speed, change greatly, the interference factor which can be predicted for the whole system can seriously influence the model prediction precision. In addition, the model is also influenced by environmental factors and human factors, the galvanizing quality control is influenced by different seasonal temperatures, the initial model coefficient is not suitable for the requirements of the actual production process, and the model deviation is larger.
Therefore, on the basis of a coating thickness preset model, a long-term adaptive adjustment method of preset model parameters is added and put into setting calculation, and the tracking of the specification change of a hot galvanizing product can be realized, so that the forecasting precision of the set model is improved. And after a batch of strip steel with the same specification is galvanized, updating the preset model parameters with the same specification. The measured data in the model are the average values of the whole volume and form K data sets QkEach matrix is divided into N numbers according to the thickness of a zinc layer, and Q is the number1To QNThe new and old data replacement formula is realized according to the first-in first-out principle as follows:
matrix QkThe column average of (c):
in the formula:is the average value of the air knife pressure,Is the average linear speed of the strip steel,The average value of the distance from the air knife to the surface of the strip steel,the average value of the thickness of the plating layer.
When the linear preset model equation (2) of the conversion logarithmic space is a linearized equation, the long-term preset model is as follows:
P＝exp(lnKl+x1llnV+x2llnD+x3llnCW)(5)
in the formula, x1l，x2l，x3lIs a long term preset model parameter; klModel constants are preset for long periods.
Will be provided withIs substituted into the above equation and K is addedlSubstituting 1 to obtain:
Pk＝Kl+x1l·Vk+x2l·Dk+x3l·CWk(6)
the statistical matrix changes to Bk＝Ak·xk(7)
Wherein, <math>
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here, the parameter xkThe parameters are preset by applying a least square method to obtain,
in the long-term preset model, the system parameter update has self-adaptive capacity for the control variable, and the system parameter update is automatically corrected along with the change of the data set, namely, in the running process of the system, the input and output data of the system are continuously measured, the parameter value in the model is continuously preset and corrected according to the data, the online update of the model parameter adopts an exponential smoothing filter algorithm, and the formula is as follows:
xp＝αxn+(1-α)xn-1(9)
in the formula,xn-1the model parameter value at the previous moment is taken as the model parameter value; x is the number ofnThe model parameter value at the current moment is taken as the model parameter value; x is the number ofpIs an updated model parameter value; α is a smoothing coefficient (empirical value of 0.85).
3. Short-term adaptive adjustment method for preset model parameters
In long-term production practice, the control action of the long-term preset model on the distance deviation between the strip steel and the air knife is delayed, and the deviation of the coating thickness of the strip steel head can be caused.
Assuming that the plating thickness change of the surface of the strip steel is very small relative to the change of input variables such as the distance between the cutters, the pressure and the speed of the strip steel in the same current coiled strip steel, the galvanizing process can be regarded as a linear change process, namely, the nonlinear control in the galvanizing process is processed by adopting a linear control algorithm. The short-term preset control method firstly replaces an absolute variable in a formula (1) with a variable, and then differentiates two sides of the formula (1), so that a short-term preset model is as follows:
in the formula, <math>
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the dP, P, V, D, CW in the equation are expressed as Δ P,instead (over a certain sampling time) the equation is:
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wherein D isTDAnd DTWThe distance between the upper air knife nozzle and the transmission side and the working side of the strip steel is calculated; dBDAnd DBWThe distance between the lower air knife nozzle and the transmission side and the working side of the strip steel; CWTopAnd CWBotThe thicknesses of zinc layers on the upper surface and the lower surface of the strip steel are respectively set; pTopAnd PBotRespectively, the upper and lower surface air knife pressures.
Equation (11) is then converted to the form,
in the formula, x1s，x2s，x3sAre short-term adaptive model coefficients.
In the frequency domain, the transfer function of the system is:
GP(s)＝GV(s)+GD(s)+Gcw(s)(13)
coating thickness, strip steel speed, air knife and strip steel distance and pressure variation delta CW from step (k-1) to step kk，ΔVk，ΔDk，ΔPk；
The variation from step (k-2) to step (k-1) is Δ CWk-1，ΔVk-1，ΔDk-1，ΔPk-1Then the equation is:
equation (14) is converted into a matrix form,
Zk＝Hkθk(15)
in the formula, Zk＝[zkzk-1zk-2]T＝[ΔPkΔPk-1ΔPk-2]T，
θk＝[x1sx2sx3s]T。
Then, the short-term adaptive parameter adjustment and update adopts a Kalman filtering algorithm, and the formula is as follows:
Gk＝Sk-1xk(ρ+xk TSk-1xk)-1
wherein I is an identity matrix, GkIs a gain matrix, zkA matrix of actual values is measured for the knife pressure,transposing a matrix for the measured actual values of speed, tool distance and plating thickness,a short-term adaptive parameter preset value is set for the current moment,for short-term adaptive parameter presetting at a previous moment, SkTo verify the covariance matrix of the precision, Sk-1Covariance matrix for verification of accuracy at previous time, p is adaptiveThe rate should be adjusted.
When continuous strip steel passes through an air knife and the coating thickness specification of the strip steel is changed, the long-term self-adaptive method is used for presetting control of the thickness of a zinc layer, and when speed change interference occurs or the coating thickness has large deviation in the galvanizing process, the short-term self-adaptive method is used for feedforward, feedback and inclination control of the coating thickness.
The invention has the beneficial effects that:
the invention solves the control problems caused by overlarge coating thickness deviation and coating thickness detection lag due to the target thickness of the coating, the thickness of the strip steel substrate and the speed change of the strip steel in the zinc layer thickness control transition process, can improve the response speed of air knife control, effectively inhibits the influence of interference quantity in the zinc coating transition process on the coating thickness, and improves the uniformity of the thickness. Compared with the prior art, the method has the following characteristics:
1. manual operation is replaced, and automatic preset control of the thickness of the zinc coating is realized;
2. the system has fast response and has the characteristics of interference resistance and hysteresis resistance;
3. the thickness deviation of the zinc coating is effectively reduced, and the thickness control precision of the zinc coating is improved;
4. the system is convenient to operate, easy to maintain, stable and reliable;
5. can reduce the consumption of zinc, save the galvanizing cost and improve the economic benefit.
Drawings
FIG. 1 is a block diagram of a preset control strategy for coating thickness dynamic specification variation;
FIG. 2 is a diagram showing the functional components of the preset control for dynamically varying the thickness of the coating;
FIG. 3 is a flow chart of the preset control of the dynamic specification change of the coating thickness;
FIG. 4 is a comparison graph of the calculated value and the actual value of the preset control model with the dynamically variable specification of the coating thickness;
FIG. 5 is a graph of air knife pressure control as the line speed is varied;
FIG. 6 is a graph showing the control of the thickness of the coating on the upper and lower surfaces of the strip steel during dynamic gauge change.
Detailed Description
The present invention will be described in detail by taking a cold rolling mill 5# hot dip galvanized wire as an example.
The target plating thickness of the system is divided into six specifications which are respectively 80g/m2、100g/m2、120g/m2、180g/m2、220g/m2、276g/m2The strip steel speed is 60-130m/min, the distance between a coating thickness gauge and an air knife is 140m, the lag time is 80-300s, the sampling time T is 10ms, and the time constant T iscThe thickness of the steel strip is 0.4-3.0mm in 8-30s, and the transition time of the steel strip during specification conversion is 50 s. The adaptive control function of the system preset model is shown in fig. 1. The preset model output variables are air knife distance and air knife pressure. And (3) realizing the parameter adaptive adjustment, as shown in FIG. 2. The device mainly comprises a data acquisition and stability judgment analysis unit, a plating thickness setting model unit, a model long-term preset control unit, a model short-term preset control unit and a model parameter updating unit.
Referring to fig. 3, the specific implementation steps are as follows:
1. data acquisition and stability assessment analysis
And carrying out online acquisition and classified storage on the actual data of the field production. According to different control requirements, one method is to collect the actual data of each coil of strip steel, the sampling time interval is 1s, then the data is analyzed, and the data which do not accord with the steady-state condition are removed, so as to ensure the accuracy of the subsequent model calculation. The stability judging condition comprises the following steps:
when the belt speed is unchanged and the position and the pressure of the air knife are unchanged in a period of time, the data are considered to be stable, the time is delayed until the thickness gauge obtains an accurate numerical value after the data are judged to be stable, the shortest and longest delay time is calculated according to the belt speed at the moment, and when the output of the unit is true, namely the system is in a stable state, stable and reliable data which can be used for subsequent calculation are output. And when a certain amount of samples are collected, storing the samples in a global variable array, collecting data of each coil of strip steel according to classification of different specifications, different speeds and different plate thicknesses, and storing the samples in a database in a classification manner after a certain amount of samples are collected. After the sample data of each category is stored in a fixed time range and a fixed data preparation amount, when the sample data amount of each category exceeds a limit value range, a model preset calculation updating unit is triggered, and model parameters are recalculated and set values are output.
2. Establishing a preset zinc coating thickness model
The preset model equation of the plating thickness is as follows:
this equation is linearized in logarithmic space:
lnP＝lnK+x1llnV+x2llnD+x3llnCW
then sampling according to a large amount of actual production data, and fitting equation coefficients x by adopting a logarithmic space least square fitting algorithm1l，x2l，x3lPerforming regression calculation, and obtaining a basic formula of a preset model equation of 6 coating thickness specifications according to the production process requirement: target coating thickness 80g/m2、100g/m2、120g/m2、180g/m2、220g/m2276g/m2 are as follows:
from the process point of view, on one hand, the air knife pressure and the strip steel thickness are in negative correlation with the weight of the zinc layer from the influence property, namely, the larger the air knife pressure and the strip steel thickness, the smaller the weight of the zinc layer; the distance from the air knife to the strip steel and the unit speed are positively correlated with the weight of the zinc layer, namely the larger the distance from the air knife to the strip steel is, the larger the unit speed is, and the larger the weight of zinc is. On the other hand, the influence of the thickness of the steel strip on the weight of the zinc layer is the smallest, the unit speed is the second, the distance from the air knife to the steel strip is the second, and the air knife pressure has the largest influence relatively, which is also consistent with the actual production situation on site.
3. Short-term adaptive adjustment of model parameters
In the galvanizing production process, a short-term self-adaptive model automatically and continuously adapts to the continuously changing working condition of a production line, the preset error is reduced by adjusting an on-line self-adaptive coefficient, when the interference quantity such as deviation or speed change of a strip steel rolling line occurs, the thickness of a zinc layer on the surface of strip steel can also change, the model on-line preset control unit needs to respond in time to eliminate the influence of the interference quantity on the galvanizing thickness, and the distance and the pressure need to be adjusted in time to keep the galvanizing thickness constant. Under the influence of time-varying offset of a system, sometimes even under the condition of the same coil, a rolling line deviates from the position of a strip steel center line, namely the strip steel is not positioned in the middle of two air knives at the moment, the actual position of the knife distance in the state can cause wrong preset value of the coating thickness due to the lack of a reliable measuring device, so the knife distance can be preset and adjusted only according to the actual value of the coating thickness, the strip speed and the actual value of the air knife pressure at the moment, and the self-adaptive set values of the upper and lower knife distances of the air knives are respectively:
and at this time, substituting the tool distance value into a calculation according to the short-term self-adaptive adjustment schedule of the plating thickness of the air knife to obtain a set value of the air knife pressure, wherein the calculation formula is as follows:
in the formula,the preset value of the upper and lower knife pressures of the air knife,is an actual measured value of the pressure of the upper knife and the lower knife of the air knife,is a preset value of the distance between an upper knife and a lower knife of the air knife,is an actual measured value of the thickness of the coating on the upper part and the lower part of the air knife, x1s,x2s,x3sFor short-term adaptive adjustment of the coefficients, the model coefficient k is constant (empirical value k is 1). According to the current process system and requirements of a galvanized wire, factors of increasing plating thickness OFFSET are considered for the production of strip steel with different thickness specifications, and according to the different thickness specifications, the actual values of the plating thickness OFFSET are as follows:
the target plating thickness is 80g/m2When OFFSET is-8, the target plating thickness is 100g/m2When the OFFSET is-4,
the target plating thickness is 120g/m2When OFFSET is-8, the target plating thickness is 180g/m2When the OFFSET is-2,
the target plating thickness is 220g/m2When OFFSET is-1, the target plating thickness is 276g/m2When the OFFSET is-1.
The short-term adaptive parameter updating of the model adopts a Kalman filtering algorithm, and the preset value of the model parameter at the previous moment of the system is as follows:
in the formula, thetak-1For the optimum value of the model parameter at the previous moment, FK-1To act onThe state-transformation matrix of (a) above,presetting parameters, U, for the model at the previous momentkIs the control quantity at the current moment.
Here, we use an error correlation matrix, i.e. a covariance matrix, to measure the preset accuracy of model preset control, and the formula is as follows:
in the formula,covariance matrix, S, for measuring model parameter preset accuracyK-1Covariance matrix, F, which is the systematic process noiseK-1Is acting onThe state transformation transpose matrix above.
Then, the model parameter at the current moment can be updated by obtaining the model preset value at the previous moment and the actual production value at the current moment, and finally the optimal model parameter preset value is obtained, wherein the formula is as follows:
k herekUpdating the covariance matrix at the current moment for the optimal gain of the model under the condition of the optimal gain, wherein the formula is as follows:
wherein k is the current time, k-1 is the previous time, Zk+1For measured actual values of the cutting pressure parameter in the model, Hk' is the transpose matrix of the actual values of the speed, the tool spacing and the plating thickness measurement in the model,presetting parameters for the model at the current moment,to the updated covariance matrix, KKFor model optimal gain, I is the identity matrix.
4. Long-term adaptive adjustment of model parameters
According to the field practice situation, when a strip steel welding seam reaches 15s before an air knife, a preset model is adopted to give a preset value of the air knife distance, when the specification of a galvanized product is changed, the air knife pressure and the preset value of the knife distance are calculated according to a long-term self-adaptive adjustment model due to the change of basic data such as target coating thickness, strip speed and the like, and at the moment, the set model of the air knife pressure is as follows:
PR＝exp[x1llnV+x2llnD+x3lln(CWT+OFFSET)]
in the formula (CW)TFor the target zinc layer thickness, OFFSET is the OFFSET given according to the actual production process requirement, and is the same as the OFFSET in the calculation step 2, wherein x is1l,x2l,x3lAnd (4) adjusting coefficients for the model in a long-term self-adaptive manner.
In the galvanizing production, a model parameter long-term self-adaptive method forms on line and periodically updates a tool distance parameter table corresponding to each specification in the galvanizing production. The tool distance initial setting table is set aiming at different tool distances corresponding to different plate thicknesses and target plating thicknesses, effective data are stored in a computer memory in a two-dimensional matrix form consisting of constant arrays, and when the actual coil change is switched and the specification is dynamically changed, the index value of a row and a column where the tool distance parameter table is located is returned through a threshold interpolation method according to the target plating thickness and the plate thickness value, so that the tool distance preset value is given through online table lookup of the index value and issued to the first-level basic automation. The actuating mechanism for setting the knife distance is a stepping motor on two sides of the air knife, and a certain time is needed from the time when the control system issues a set value to the time when the stepping motor finishes the action, namely, when the coil change pulse signal is switched, the set signal needs to be kept for several seconds and then reset. The calculation process of the threshold interpolation method is as follows: the plating range is divided into 40,50,60,90,110 and 138, the plate thickness range is 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.1 and 1.5, the threshold value of the row is a target plating thickness value, the threshold value of the column is a plate thickness value, the actual data and the index are linearly interpolated according to the threshold value to obtain an actual index position in the table, the index numbers are all from 0, then a preset value of the knife distance is given, and then the air knife pressure value at the moment is calculated according to a preset model of the air knife pressure and is issued to the first stage.
The current online applied tool distance parameter initial setting table is as follows:
the parameters of the air knife pressure and knife distance setting model are updated on line by adopting an exponential smoothing filter algorithm, and the formula is as follows:
xp＝αxn+(1-α)xn-1(24)
in the formula, xn-1A model parameter value at a previous time; x is the number ofnThe model parameter value at the current moment is taken as the model parameter value; x is the number ofpThe self-learning effect is better because the initial value of the alpha smoothing coefficient is 0.85 of the empirical value for updating the model parameter value, and the smoothing coefficient value is changed once in a period of time if necessary. Generally, too large a smoothing coefficient value causes oscillation of a prediction value, so that the prediction value is high and low; too small a smoothing factor value slows the rate at which the predicted value approaches the target value. For this reason, at the initial stage of the preset control, the smoothing coefficient value is appropriately increased to accelerate the learning speed, and is gradually decreased as the number of measurements increases to avoid oscillation. Furthermore, the utility modelAnd storing the newly finished model coefficients into a computer memory and an external memory according to short-term and long-term classification, so that whether the long-term coefficients or the short-term coefficients are used is determined according to whether the specification is dynamically changed or not when the subsequent strip steel is subjected to coil change.
According to the above calculation steps, the fitting curve of the preset model of the plating thickness and the actual production measurement curve are compared as shown in fig. 4. The fitting value is basically close to the actual value, the accuracy of the preset model of the plating thickness is proved, and the preset requirement of the plating thickness on site can be completely met. Meanwhile, in actual production, the speed of the strip steel is frequently changed due to process requirements, and if the pressure control of the air knife is not timely and quickly adjusted, the actual plating thickness has great deviation. Selecting target specification from the plating solution, and plating the plating solution to form a single-sided plating layer with the thickness of 40g/m2For example, when the strip steel speed frequently changes in the interval of 80-115m/min, in order to ensure the target plating thickness, the model system automatically starts a short-term adaptive adjustment method to track the speed change in real time and adjust the air knife pressure, as shown in fig. 5, from the field application data of the galvanizing production line in fig. 5, the adjustment of the air knife pressure follows the change of the strip steel speed, and the method has the characteristic of fast response time and can realize the stable transition of the air knife pressure.
After the on-site application of the preset model of the coating thickness and the long-term adaptive adjustment method, when the production process is dynamically changed into specifications, for example, when the production is 80g/m of coating thickness from the target specification2The transition to plating thickness is 276g/m2During the process, the deviation between the actual measured value of the coating thickness and the target coating thickness is basically controlled within 3 percent (see figure 6), and the system control is over-adjusted to be small, which shows that when the specification is dynamically changed in the production process, the stable transition of the system control can be realized, the thickness deviation of the zinc coating can be effectively reduced, and the thickness control precision of the hot dip zinc coating is improved.
Claims (1)
1. A hot dip galvanizing coating thickness dynamic variable specification presetting control method is characterized in that a principal component analysis method is applied, the strip steel speed, the air knife pressure and the distance from the air knife to the strip steel surface are used as main influence factors of the coating thickness, a coating thickness presetting model is established by adopting a logarithmic space least square fitting algorithm, and the presetting control precision of the zinc coating thickness is improved; aiming at the influence of the variable speed, the target thickness and the thickness interference of the strip steel substrate on the thickness of a plating layer, which often occur in the dynamic specification-changing process of the galvanizing production, the long-term self-adaption and short-term self-adaption adjustment of the thickness of the plating layer are carried out, and the self-adaption capability of a galvanizing layer thickness model is improved; in the control of the coating thickness, a Kalman filtering algorithm and a smoothing filtering algorithm are applied to update and calculate the coating thickness model parameters on line, so that the preset precision of the model parameters is improved, and the accuracy and the stability of the control of the coating thickness are ensured; the method comprises the following specific steps:
(1) establishing a preset model of the thickness of the coating
And (3) applying principal component analysis and an online application angle of the model, selecting three factors which have great influence on the coating thickness CW, namely air knife pressure P, strip steel linear velocity V and distance D from an air knife to the surface of strip steel, and establishing a coating thickness preset model:
in the formula: k is a model constant; x is the number of1、x2、x3The model coefficients can be obtained through regression analysis of actual measurement data;
according to the actual process parameters, establishing a linear preset model equation of a nonlinear model conversion logarithmic space as follows:
lnP＝lnK+x1lnV+x2lnD+x3lnCW(2)
(2) long-term self-adaptive adjustment method for preset model parameters
On the basis of a coating thickness preset model, adding preset model parameters for long-term adaptive adjustment, and putting the parameters into setting calculation to realize the tracking of the specification change of a hot galvanizing product; after a batch of strip steel with the same specification is galvanized, updating the preset model parameters with the same specification; the measured data in the model are the average values of the whole volume and form K data sets QkEach matrix is divided into n numbers according to the thickness of a zinc layer, from Q1To QNThe new and old data replacement formula is realized according to the first-in first-out principle as follows:
matrix QkThe column average of (c):
in the formula,is the average value of the air knife pressure,Is the average linear speed of the strip steel,The average value of the distance from the air knife to the surface of the strip steel,the average value of the thickness of the plating layer is obtained;
when the linear preset model equation (2) of the conversion logarithmic space is a linearized equation, the long-term preset model is as follows:
P＝exp(lnKl+x1llnV+x2llnD+x3llnCW)(5)
in the formula, x1l，x2l，x3lIs a long term preset model parameter; klPresetting a model constant for a long term;
will be provided withIs substituted into the above equation and K is addedlSubstituting 1 to obtain:
Pk＝Kl+x1l·Vk+x2l·Dk+x3l·CWk(6)
the statistical matrix changes to Bk＝Ak·xk(7)
Wherein,
here, the parameter xkPresetting parameters by using a least square method;
in the long-term preset model, the system parameter update has self-adaptive capacity for the control variable, and the system parameter update is automatically corrected along with the change of the data set, namely, in the running process of the system, the input and output data of the system are continuously measured, the parameter value in the model is continuously preset and corrected according to the data, the online update of the model parameter adopts an exponential smoothing filter algorithm, and the formula is as follows:
xp＝αxn+(1-α)xn-1(9)
in the formula, xn-1The model parameter value at the previous moment is taken as the model parameter value; x is the number ofnThe model parameter value at the current moment is taken as the model parameter value; x is the number ofpIs an updated model parameter value; alpha is a smooth coefficient and takes a value of 0.85;
(3) short-term adaptive adjustment method for preset model parameters
The method can quickly respond and control the deviation according to the preset method of the current volume; assuming that the plating thickness change of the surface of the strip steel is very small relative to the change of input variables of the tool distance, the pressure and the strip steel speed in the current same rolled strip steel, the galvanizing process can be regarded as a linear change process, namely, the nonlinear control in the galvanizing process is processed by adopting a linear control algorithm; the short-term preset control method firstly replaces an absolute variable in a formula (1) with a variable, and then differentiates two sides of the formula (1), so that a short-term preset model is as follows:
in the formula，
The dP, P, V, D, CW in the equation are expressed as Δ P,and within a certain sampling timeInstead, the equation is:
in the formula,
wherein D isTDAnd DTWThe distance between the upper air knife nozzle and the transmission side and the working side of the strip steel is calculated; dBDAnd DBWThe distance between the lower air knife nozzle and the transmission side and the working side of the strip steel; CWTopAnd CWBotThe thicknesses of zinc layers on the upper surface and the lower surface of the strip steel are respectively set; pTopAnd PBotRespectively the air knife pressure of the upper surface and the lower surface;
then, formula (11) is converted into the following form:
in the formula, x1s，x2s，x3sIs a short-term adaptive model coefficient;
in the frequency domain, the transfer function of the system is,
GP(s)＝GV(s)+GD(s)+Gcw(s)(13)
coating thickness, strip steel speed, air knife and strip steel distance and pressure variation delta CW from step (k-1) to step kk，ΔVk，ΔDk，ΔPk；
The variation from step (k-2) to step (k-1) is Δ CWk-1，ΔVk-1，ΔDk-1，ΔPk-1Then the equation is:
converting equation (14) to matrix form:
Zk＝Hkθk(15)
in the formula, Zk＝[zkzk-1zk-2]T＝[ΔPkΔPk-1ΔPk-2]T，
θk＝[x1sx2sx3s]T；
Then, the short-term adaptive parameter adjustment and update adopts a Kalman filtering algorithm, and the formula is as follows:
wherein I is an identity matrix, GkIs a gain matrix, zkMeasuring the actual value matrix for the knife pressure, HkIs a matrix of measured actual values of speed, tool distance and plating thickness,transposing matrix for measuring actual values of speed, tool distance and plating thickness，A short-term adaptive parameter preset value is set for the current moment,for short-term adaptive parameter presetting at a previous moment, SkTo verify the covariance matrix of the precision, Sk-1A covariance matrix for verifying precision at a previous moment, wherein rho is an adaptive regulation rate;
when continuous strip steel passes through an air knife and the coating thickness specification of the strip steel is changed, the long-term self-adaptive method is used for presetting control of the thickness of a zinc layer, and when speed change interference occurs or the coating thickness has large deviation in the galvanizing process, the short-term self-adaptive method is used for feedforward, feedback and inclination control of the coating thickness.
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