CN103019097B - Optimal control system for steel rolling heating furnace - Google Patents
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- 238000010438 heat treatment Methods 0.000 title claims abstract description 101
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- 238000004519 manufacturing process Methods 0.000 claims abstract description 27
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- 238000002791 soaking Methods 0.000 claims description 39
- 238000000034 method Methods 0.000 claims description 24
- 238000005457 optimization Methods 0.000 claims description 24
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- 238000013528 artificial neural network Methods 0.000 claims description 19
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- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 5
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- 229910052760 oxygen Inorganic materials 0.000 description 5
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- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 3
- 238000012937 correction Methods 0.000 description 3
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- 239000003546 flue gas Substances 0.000 description 3
- 101100272279 Beauveria bassiana Beas gene Proteins 0.000 description 2
- 238000011217 control strategy Methods 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
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- 238000011161 development Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005098 hot rolling Methods 0.000 description 1
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses an optimal control system for a steel rolling heating furnace, relating to the technical field of steel rolling of a heating furnace. Firstly, an on-line furnace temperature setting device is established according to different billet types, production rhythm, billet initial temperature and billet tapping temperature; the billet tapping temperature is controlled by controlling the temperature of the furnace; based on heat efficiency models under various working conditions, the loading variation of the heating furnace is calculated and taken as a feedforward value of the furnace temperature, and the high-accuracy control on the temperature of the heating furnace under loading fluctuation is realized; and on the basis, an optimal air-fuel ratio is found by establishment of an air-fuel ratio optimal control model, so that the heating furnace achieves an optimal burning condition and the aims of saving fuel and lowering steel loss are achieved.
Description
Technical Field
The invention relates to the technical field of heating furnace optimization control, in particular to a steel rolling heating furnace optimization control system.
Background
The steel rolling heating furnace is a heating device which uses high-temperature flame and flue gas generated when fuel is burned in a hearth as heat sources to heat a steel billet flowing in the furnace to reach a specified process temperature. The production operation of the heating furnace device requires to ensure the control precision of the tapping temperature of the heated medium, the minimum fuel consumption, the minimum steel loss and the like.
In order to realize the productivity index of the heating furnace, the key technology in the control of the heating furnace is that the furnace temperature is adjusted according to the production rhythm, the feeding temperature of the steel billet and the discharging temperature of the steel billet, so that the discharging temperature of the steel billet meets the requirement of steel rolling, and the furnace temperature needs to be dynamically controlled; on the other hand, the heat efficiency of the heating furnace is maximized by searching a reasonable air-fuel ratio mainly from the aspects of energy conservation, material consumption and the like, and the steel loss is reduced by reducing the oxidizing atmosphere of a soaking section in the furnace. In the furnace temperature control process, the dynamic setting of the furnace temperature target value and the improvement of the furnace temperature control precision are key points of furnace temperature control.
The dynamic setting of the furnace temperature is realized by four steps, namely, a "dynamic setting control method of the furnace temperature of a hot rolling furnace" applied by Bao Steel products, Inc., application No. 20051002485.0, and publication No. CN 1840715A. The method comprises the following steps that firstly, a slab temperature forecasting model is adopted to calculate the end-of-segment temperature of a segment where a slab is located, and the method is forward recursion; secondly, dynamically calculating the target temperature of each section and the end of each slab according to the slab moving distance; thirdly, calculating the furnace gas temperature required by the current section of the plate blank; and fourthly, taking the difference of the current slabs into consideration to carry out expert experience weighted average. Due to the uncertainty of furnace operation, such as: the change of production rhythm, the difference of the temperature of the steel billet entering the furnace (cold billet and hot billet), the type difference of the steel billet and the like, so the temperature of the steel billet is difficult to predict by adopting a forward recursion method, or the predicted temperature is inaccurate, which greatly influences the effect of dynamically setting the furnace temperature.
In the control method of furnace temperature, the currently adopted methods are double-crossing amplitude limiting control, fuzzy self-adaptive control technology, predictive control and feedforward control technology, etc. In the journal of industrial control technology, 4 months in 2009, a word written by zhangxiuli, wudinghui, the jiangsu tin industry group as a fuzzy adaptive control strategy for the combustion process of a steel rolling heating furnace, applies a fuzzy control concept to the heating furnace control technology, adopts a control algorithm which is still conventional PID, performs fuzzy calculation and setting on P, I, D parameters only, and has the technical difficulty of a fuzzy controller, wherein the technical difficulty is the composition of the fuzzy controller, and is reflected in the selection of input linguistic variables and output linguistic variables and the determination principle of a membership assignment table in a centralized manner, so that the process determined by the rules of fuzzy control is relatively complex and has no good universality; the invention, entitled "a method for integrated control of furnace outlet temperature" filed by university of qinghua, application No. 200810102875.7, publication No. CN 101256418a, applies feedback predictive control and feedforward control to control of furnace outlet temperature. The feedback prediction control is established on the basis of a plurality of assumptions, and the established feedback prediction model cannot accurately reflect the actual situation or is inaccurate; the feed-forward control strategy uses gas pressure or air pressure as feed-forward of flow change, but does not involve feed-forward control of load change.
In the control of the air-fuel ratio, the air-fuel ratio control system of the combustion heating furnace, which is applied by the japan insulator corporation, application No. 200810086051.5, publication No. CN 101270880a, requires that the operator set a fixed set value of the oxygen content of the flue gas, and the flow ratio of the air and the fuel is corrected by the oxygen content value. Because the optimum oxygen content is not fixed and unchanged under different working conditions, the optimum operation is difficult to realize really, and in addition, the oxygen content of the flue gas has the problem of measurement lag and is difficult to meet the real-time control, so the mode of adjusting the air-fuel ratio by the oxygen content meter is difficult to achieve the ideal result.
In view of the foregoing, the prior art has certain limitations and disadvantages that have resulted in the development of the present system.
Disclosure of Invention
The invention aims to solve the problems that: the invention discloses an optimized control system of a steel rolling heating furnace, which reduces the gas consumption and the steel loss on the premise of ensuring the stable temperature of a billet outlet under different loads of the heating furnace.
The technical scheme adopted by the invention for solving the technical problems is as follows: the system is provided with a furnace temperature on-line setter, a thermal load estimator, furnace temperature regulators at the upper part and the lower part of a soaking section and the upper part and the lower part of a heating section, a furnace temperature feedforward regulator, a gas flow regulator, an air-fuel ratio optimization controller, a furnace temperature measuring instrument, a steel billet position measuring instrument, a gas flow measuring instrument, an air flow measuring instrument, a gas flow regulating valve and an air flow regulating valve;
the output of the furnace temperature on-line setter is used as the given value of a furnace temperature regulator, and the regulator adopts a PID control algorithm; multiplying the output of the thermal load estimator by a load distribution coefficient to serve as a measured value of each furnace temperature feedforward regulator, wherein the regulator adopts a PD control algorithm, and the load distribution coefficient is given according to the size proportion of each thermal load according to operation experience; the sum of the output of the furnace temperature regulator and the output of the furnace temperature feedforward regulator is used as a set value of the gas flow regulator, and the gas flow regulator adopts a PID control algorithm to realize closed-loop control of gas flow; the product of the output of the air-fuel ratio optimization controller and the measured value of the gas flow is used as a set value of the air flow regulator, and the air flow regulator adopts a PID control algorithm to realize closed-loop control of the air flow;
(1) furnace temperature on-line setting device
Adopting a nearest neighbor clustering RBF neural network identification system model to calculate a furnace temperature set value on line; the model input variables include the temperature of the steel billet entering the furnaceTemperature of steel billet discharged from furnaceProduction rhythmOf the billet type(ii) a The output variables of the model comprise furnace temperature set values of the upper part and the lower part of the soaking section、The furnace temperature set values of the upper part and the lower part of the heating section、By collecting model input variables in the industrial field process and the furnace temperatures of the upper part and the lower part of the soaking section、The furnace temperature of the upper part and the lower part of the heating section、Training the neural network off line according to the deviation of the furnace temperature set value output by the model and the actual furnace temperature by using the training sample data in the furnace, and adjusting the weight of each layer of the neural network to obtain a neural network model of the furnace temperature set value of the heating furnace; the model can output the furnace temperature set value of the heating furnace by acquiring the temperature of the steel billet entering the furnace, the temperature of the steel billet leaving the furnace, the production rhythm and the real-time data of the steel billet type;
the production rhythm is characterized by the steel tapping amount in unit time, the unit is root/hour or piece/hour, the tapping interval of two steel billets is calculated in real time according to a photoelectric switch measuring signal for detecting the tapping of the steel billets, and the tapping amount R3 of the steel billets per hour is calculated;
(2) thermal load estimator
The thermal load estimator calculates the coal gas flow required under the current load based on the thermal efficiency of the heating furnace, the thermal efficiency adopts a nearest neighbor clustering RBF neural network model, the thermal load is calculated based on the thermal efficiency model, and the output of the thermal load estimator is the coal gas flow;
the input variable of the heat efficiency neural network model comprises the temperature of the billet steel entering the furnaceTemperature of steel billet discharged from furnaceProduction rhythmOf the billet typeThe model output variable is the heating furnace thermal efficiencyTraining sample data including model input variables and heating furnace actual heat efficiency calculated values in the industrial field process are collected, and heating furnace heat efficiency is output according to the modelAnd the actual heat efficiency of the heating furnaceOff-line training is carried out on the neural network, and the weight of each layer of the neural network is adjusted to obtain a heating furnace thermal efficiency neural network model; the model can output the heat of the heating furnace by acquiring the temperature of the steel billet entering the furnace, the temperature of the steel billet leaving the furnace, the production rhythm and the real-time data of the type of the steel billetEfficiency value；
1) Actual thermal efficiencyThe calculation is as follows:
wherein,the heat released by the combustion of the gas,the total flow of the coal gas is the total flow of the coal gas,the heating value of the coal gas is the heating value of the coal gas,、respectively the enthalpy value of the billet when entering and leaving the heating furnace,the heat absorbed by the billet steel in the heating furnace;
2) the thermal load estimator output is calculated as follows:
wherein,the heat quantity needed to be provided for the heating furnace,is a steel billet made ofRise toThe amount of heat that needs to be absorbed,is the specific heat of the steel billet,is the processing and treating capability of the processing, 、 respectively the initial temperature of the billet entering the furnace and the required temperature of the billet leaving the furnace,in order to realize the production rhythm,the quality of a single steel billet is the same as that of the single steel billet,the gas flow required by the heating furnace;
(3) air-fuel ratio optimizing controller
1) The optimization objective function is:
wherein,in order to be the weighting coefficients,respectively the furnace temperatures of the upper part and the lower part of the soaking section and the furnace temperatures of the upper part and the lower part of the heating section;
2) setting an optimized step size
Setting up,，，
Wherein,the gas flow rates of the upper part and the lower part of the soaking section and the upper part and the lower part of the heating section are respectively,respectively the air flow of the upper part and the lower part of the soaking section and the air flow of the upper part and the lower part of the heating section,the air-fuel ratios of the upper part and the lower part of the soaking section and the upper part and the lower part of the heating section are respectively;
setting up，，WhereinFor the manually adjusted coefficients, according to this relationship, a total air quantity adjustment is given each timeIf so, corresponding to an air-fuel ratio increment;
wherein,is the air-fuel ratio increment at the upper part of the heating section;
calculating the air flow increment of the upper part and the lower part of the soaking section and the air flow increment of the upper part and the lower part of the heating section, namely the step length, which correspond to each optimization, respectively:
wherein,、、andair flow increment of the upper part and the lower part of the soaking section and the upper part and the lower part of the heating section respectively;
3) calculating an optimized air-fuel ratio by adopting a self-optimizing algorithm of a forward-backward method;
under the condition of constant gas flow, the air flow is changed, after the system responds, the change of the optimized target value J under the front and rear working conditions is compared, if the variable quantity of the optimized target value J is increased and obvious, the adjustment is beneficial, and the air flow is continuously adjusted according to the original direction; if the variation of the optimized target value J is reduced and obvious, adjusting the air flow according to the reverse direction of the original air flow adjusting direction; and when the change of the optimization target value J is not obvious, stopping adjusting the air flow, and indicating that the current actual air-fuel ratio is the optimal air-fuel ratio.
The invention has the advantages that:
the furnace temperature on-line setter dynamically sets the furnace temperature set values of all the sections according to the change conditions of all the process parameters in the production and the steel billet tapping temperature, thereby ensuring the control precision of the steel billet tapping temperature.
The furnace temperature feed-forward regulator can react the change of the load to the gas flow in advance, and can effectively overcome the adverse effect caused by process lag, so that the rapidity and the stability of furnace temperature control can be ensured when the working condition changes.
The neural network used in the furnace temperature on-line setter and the heating furnace thermal efficiency on-line modeling has the characteristics of high identification precision and capability of accurately approximating any nonlinear function, does not depend on a specific device, and has good universality during modeling.
The optimization strategy of the advancing and retreating method is to find the optimal process parameters according to the combustion effect to obtain the economy of the combustion process, and the optimal air-fuel ratio can be found without depending on any precise instrument or a fuel heat value analysis instrument, so that the maximum optimization control of the combustion heat efficiency is realized.
(4) Description of the drawings
FIG. 1 is a block diagram of an on-line furnace temperature setter;
FIG. 2 is a block diagram of a model of the thermal efficiency of the furnace;
FIG. 3 is a block diagram of a furnace optimization control system;
FIG. 4 is a block diagram of an RBF neural network;
FIG. 5 is a flow chart of a RBF neural network nearest neighbor clustering algorithm;
FIG. 6 is a block diagram of a self-optimizing routine;
(5) detailed description of the preferred embodiments
Example (b):
the system control block diagram is shown in fig. 3.
1. Furnace temperature on-line setting device
1) Establishing production rhythm model
The production rhythm is characterized by the steel tapping amount in unit time, the unit is root/hour or piece/hour, the tapping interval of two steel billets is calculated in real time according to a photoelectric switch measuring signal for detecting the tapping of the steel billets, and the tapping amount R3 of the steel billets per hour is calculated;
R3=3600/ (t (k)-t (k-1))
t (k) is the moment when the power-off switch detects that the steel billet is discharged, and t (k-1) is the moment when the steel billet is discharged last time;
2) type of steel billet
The billet types are given a number, e.g., 1,2,3,4, … …, one at a time according to the billet type, with the billet type as a quantified value. Wherein, the selection of the billet type needs manual input.
3) Training sample acquisition
Obtaining the temperature of the steel billet in the furnaceTemperature of steel billet discharged from furnaceProduction rhythmOf the billet typeFurnace temperature of upper and lower parts of soaking zone、The furnace temperature of the upper part and the lower part of the heating section、50 inner groups of production process data with a large operation range are taken as training samples of the thermal efficiency neural network model, and the data together with 150 groups of historical data collected on an industrial field are taken as 200 groups of data in total;
4) neural network model of furnace temperature on-line setter
Selecting a clustering radius by adopting a learning algorithm of RBF neural network nearest neighbor clusteringRadius correction step sizeError threshold valueThe model is shown as attached figure 1;
the inputs to the model are: temperature of steel billet entering furnaceSteel tappingTemperature of the blankProduction rhythmOf the billet typeThe model output is: upper and lower temperature of soaking zone、Upper and lower temperature of the heating section、. Output according to model、、、The temperature of the upper part and the lower part of the actual soaking section、Upper and lower temperature of the heating section、And (4) adjusting the weight of each layer of the neural network, and establishing an optimization model of the furnace temperature set value dynamic.
The nearest neighbor clustering learning algorithm is an online self-adaptive clustering learning algorithm, the RBF network basis function center is selected by the clustering algorithm, the width of the Gaussian function is determined by the clustering radius, and the weight from the hidden layer to the output layer is determined by the arithmetic mean of each output vector. The specific algorithm process is as follows: as shown in figure 5
Firstly, determining a proper clustering radiusRadius correction step lengthAnd error thresholdDefining a vectorStoring the sum of outputs belonging to each class, defining a counterCounting the number of each type of samples,storing the weightWhereinAs the number of the categories,first, theCenter of a class.
② to the first pair of dataMake it self-form a class, i.e. centerSimultaneously order，And = 1. For the RBF network with only one hidden unit, the center of the hidden unit isThe weight from the hidden unit to the output layer is。
Third to the second pair of dataTo find outToIs a distance of. If it isThenIs composed ofIs clustered with nearest neighbors, order，，(ii) a If it isThen will beAs a new cluster center, and，，and = 1. Adding a hidden layer unit in the established RBF network, wherein the weight from the hidden layer unit to an output layer is。
Fourthly, considerSample data pairWhen the temperature of the water is higher than the set temperature,assume to have already existedA cluster center, wherein the center points are,,…, The RBF network established above is already availableA hidden layer unit using the formula:
find outTo this endDistance of each cluster centerAt the minimum of these distances, i.e.Is composed ofThen: if it isThen will beAs a new cluster center, the cluster center is,，， ，1, in front ofOf a kindAndkeeping the value unchanged, and adding the second one to the established RBF networkAnd a hidden layer unit. If it isThe calculation is as follows: ，1, maintenanceAnd the value is unchanged. The weight from the hidden unit to the output layer is 。
After all input samples are considered, calculating the output of the RBF network as follows:
setting a temperature of a billet entering a furnace through a trained neural networkTemperature of steel billet discharged from furnaceProduction rhythmOf the billet typeAll have a temperature setting value of the upper part and the lower part of a soaking section、Temperature set values of upper and lower portions of the heating section、And correspondingly.
2. Thermal load estimator
And calculating the coal gas flow required by the change of the thermal load under the working condition by utilizing the thermal efficiency and heat balance principle obtained by the heating furnace thermal efficiency regression model, and outputting the coal gas flow as the measured value of the coal gas flow regulator.
1) Training sample acquisition
Obtaining the temperature of the steel billet in the furnaceTemperature of steel billet discharged from furnaceProduction rhythmOf the billet type50 inner groups of production process data with larger operation range are combined with 150 groups of historical data collected on the industrial site, and the corresponding actual thermal efficiency under each group of data is calculatedAnd totaling 200 groups of data as a training sample of a heat efficiency neural network model, wherein the actual heat efficiencyThe calculation is as follows:
wherein,the heat released by the combustion of the gas,the total flow of the coal gas is the total flow of the coal gas,the heating value of the coal gas is the heating value of the coal gas,respectively the enthalpy value of the billet when entering and leaving the heating furnace,the heat absorbed by the billet steel in the heating furnace;
2) establishing a thermal efficiency model
Selecting RBF neural network as thermal efficiency model, wherein the input and output of the neural network are shown in FIG. 2, and selecting cluster radius of parametersRadius correction step sizeError threshold valueThe algorithm is consistent with the furnace temperature online setter neural network model;
setting a temperature of a billet entering a furnace through a trained neural networkTemperature of steel billet discharged from furnaceProduction rhythmOf the billet typeAll have a heating furnace thermal efficiencyAnd correspondingly.
3) The thermal load estimator output is calculated as follows:
wherein,the heat quantity needed to be provided for the heating furnace,is a steel billet made ofRise toThe amount of heat that needs to be absorbed,is the specific heat of the steel billet,is the processing and treating capability of the processing, 、 respectively the initial temperature of the billet entering the furnace and the required temperature of the billet leaving the furnace,in order to realize the production rhythm,the quality of a single steel billet is the same as that of the single steel billet,the gas flow required by the heating furnace;
3. air-fuel ratio optimizing controller
1) Optimizing an objective function
Wherein is takingThe value range is 0.2-0.3
2) Self-optimizing algorithm of forward and backward method
As shown in fig. 6, the steps are as follows:
firstly, setting an optimized step size SOP ==γ*(the value range of gamma is 1% -2%), whereinThe value range of the current total air flow and the allowable error is 1-2, and a counter
Firstly, a constant gas flow is obtained, the current air flow is output to an air flow valve position calculator as an air flow set value, an operation flag of an optimization controller is set to be ON, and an optimization objective function value is recorded after 1-2 minutes；
② selecting to increase air flow rateIncrement of air flow set point by set step sizeAn SOP outputting an air flow set value to an air flow valve position calculator; modifying the current air-fuel ratio and turning to the step (viii);
③ ifWhen the direction is correct, the user continues to search along the direction, and then the user can search the directionIs given toTurning to the second step; if it is notWhen the time is up, whether the optimization is the first time is judged, namelyIf, ifAnd if so, saying that the name is wrong in direction searching, and turning to the fourth step. If it is notWhen is not in use, andand if so, setting the current air-fuel ratio to be in the optimal state, finishing the optimization, and setting the operation flag of the optimization controller to be OFF. If it is notWhen is not in use, andand then, changing the step length to search for the SOP = -0.25 × SOP, and turning to the second step.
Selecting and increasing air flowIncrement of air flow set point by set step size(-SOP) outputting an air flow set point to an air flow valve position calculator; modifying the current air-fuel ratio and turning to the step (viii);
fifthly, ifWhen the direction is correct, the user continues to search along the direction, and then the user can search the direction correctlyIs given toTurning to the fourth step;
sixthly, ifWhen is not in use, andand if so, setting the current air-fuel ratio to be in the optimal state, finishing the optimization, and setting the operation flag of the optimization controller to be OFF.
Seventhly, ifWhen is not in use, andand then, changing the step length to search for the SOP = -0.25 × SOP, and turning to the second step.
Calculation of objective function values
Calculating the objective function value according to the optimized objective function by using the steady-state real-time process measured value after dynamic response. That is, the calculation is started after a period of time after the optimized output is applied to the device, the time depends on the dynamic response time of the process, and the time is generally 1 to 1 for the heating furnaceFor 2 minutes. And returning to the original reversed position.
3) Optimized air-fuel ratio calculation for each part
Increase in total air flow from 2)Calculating the air flow increment of the upper part of the soaking section, the lower part of the soaking section, the upper part of the heating section and the lower part of the heating section;
taking the ratio coefficient of the air-fuel ratio of the soaking section and the heating section
The air increment distributed by the soaking section and the heating section is respectively
Calculating the optimized air-fuel ratio of the upper part of the soaking section, the lower part of the soaking section, the upper part of the heating section and the lower part of the heating section
4. Regulator design
(1) Furnace temperature regulator
The setting of the furnace temperature regulator at the upper part of the soaking section is output from the furnace temperature on-line setter, the furnace temperature regulator adopts a PID control mode, the P value range is 80-120, the I value range is 200-300, and the D value range is 30-50.
The setting of the furnace temperature regulator at the lower part of the soaking section is output from the furnace temperature on-line setter, the furnace temperature regulator adopts a PID control mode, the P value range is 90-130, the I value range is 220-310, and the D value range is 40-50.
The setting of the furnace temperature regulator at the upper part of the heating section is output from the furnace temperature on-line setter, the furnace temperature regulator adopts a PID control mode, the P value range is 70-100, the I value range is 180-250, and the D value range is 30-50.
The setting of the furnace temperature regulator at the lower part of the heating section is output from the furnace temperature on-line setter, the furnace temperature regulator adopts a PID control mode, the P value range is 90-120, the I value range is 210-290, and the D value range is 40-50.
(2) Furnace temperature feedforward regulator
The load distribution coefficient takes the values:。
whereinThe distribution coefficient of the load at the upper part of the soaking section,The distribution coefficient of the load at the lower part of the soaking section,The distribution coefficient of the upper load of the heating section,And distributing coefficients for the load at the lower part of the heating section.
A PD control algorithm is adopted by the furnace temperature feedforward regulator at the upper part of the soaking section, wherein the value range of P is 30-60, and the value of D is 50-70;
a PD control algorithm is adopted by a furnace temperature feedforward regulator at the lower part of the soaking section, wherein the value range of P is 40-70, and the value of D is 60-80;
a PD control algorithm is adopted by the furnace temperature feedforward regulator on the upper part of the heating section, wherein the value range of P is 30-60, and the value of D is 50-70;
a PD control algorithm is adopted by a furnace temperature feedforward regulator at the lower part of the heating section, wherein the value range of P is 40-70, and the value of D is 50-80;
(3) gas flow regulator
The set value of the gas flow regulator at the upper part of the soaking section is the sum of the output of the furnace temperature regulator and the output of the furnace temperature feedforward regulator, and the gas flow regulator adopts a PID control mode, wherein the value range of P is 60-100, the value range of I is 150-200, and the value of D is 20-40.
The set value of the gas flow regulator at the lower part of the soaking section is the sum of the output of the furnace temperature regulator and the output of the furnace temperature feedforward regulator, and the gas flow regulator adopts a PID control mode, wherein the value range of P is 70-90, the value range of I is 120-150, and the value of D is 30-40.
The set value of the gas flow regulator on the upper part of the heating section is the sum of the output of the furnace temperature regulator and the output of the furnace temperature feedforward regulator, and the gas flow regulator adopts a PID control mode, wherein the value range of P is 60-100, the value range of I is 150-200, and the value of D is 20-40.
The set value of the gas flow regulator at the lower part of the heating section is the sum of the output of the furnace temperature regulator and the output of the furnace temperature feedforward regulator, and the gas flow regulator adopts a PID control mode, wherein the value range of P is 70-90, the value range of I is 120-150, and the value of D is 30-40.
(4) Air flow regulator
The set value of the air flow regulator at the upper part of the soaking section is the product of the air-fuel ratio optimization controller and the current coal gas flow, the measured value is the measured value of the current air flow, and the air valve is regulated by adopting a PID control mode. The value range of the air flow regulator P is 50-80, the value range of I is 100-150, and the value of D is 30-40.
The set value of the air flow regulator at the lower part of the soaking section is the product of the air-fuel ratio optimization controller and the current coal gas flow, the measured value is the measured value of the current air flow, and the air valve is regulated by adopting a PID control mode. The air flow regulator P ranges from 60 to 90, the I ranges from 110 to 140, and the D ranges from 20 to 30.
The set value of the air flow regulator on the upper part of the heating section is the product of the air-fuel ratio optimizing controller and the current gas flow, the measured value is the current air flow measured value, and the air valve is regulated by adopting a PID control mode. The value range of the air flow regulator P is 50-80, the value range of I is 100-150, and the value of D is 30-40.
The set value of the air flow regulator at the lower part of the heating section is the product of the air-fuel ratio optimizing controller and the current gas flow, the measured value is the current air flow measured value, and the air valve is regulated by adopting a PID control mode. The air flow regulator P ranges from 60 to 90, the I ranges from 110 to 140, and the D ranges from 20 to 30.
Claims (1)
1. An optimized control system of a steel rolling heating furnace is characterized by being provided with a furnace temperature online setter, a thermal load estimator, furnace temperature regulators at the upper part and the lower part of a soaking section and at the upper part and the lower part of a heating section, a furnace temperature feedforward regulator, a gas flow regulator, an air-fuel ratio optimized controller, a furnace temperature measuring instrument, a steel billet position measuring instrument, a gas flow measuring instrument, an air flow measuring instrument, a gas flow regulating valve and an air flow regulating valve;
the output of the furnace temperature on-line setter is used as the given value of a furnace temperature regulator, and the regulator adopts a PID control algorithm; multiplying the output of the thermal load estimator by a load distribution coefficient to serve as a measured value of each furnace temperature feedforward regulator, wherein the regulator adopts a PD control algorithm, and the load distribution coefficient is given according to the size proportion of each thermal load according to operation experience; the sum of the output of the furnace temperature regulator and the output of the furnace temperature feedforward regulator is used as a set value of the gas flow regulator, and the gas flow regulator adopts a PID control algorithm to realize closed-loop control of gas flow; the product of the output of the air-fuel ratio optimization controller and the measured value of the gas flow is used as a set value of the air flow regulator, and the air flow regulator adopts a PID control algorithm to realize closed-loop control of the air flow;
(1) furnace temperature on-line setting device
Adopting a nearest neighbor clustering RBF neural network identification system model to calculate a furnace temperature set value on line; the model input variables include the temperature of the steel billet entering the furnaceTemperature of steel billet discharged from furnaceProduction rhythmOf the billet type(ii) a The output variables of the model comprise furnace temperature set values of the upper part and the lower part of the soaking section、The furnace temperature set values of the upper part and the lower part of the heating section、By collecting model input variables in the industrial field process and the furnace temperatures of the upper part and the lower part of the soaking section、The furnace temperature of the upper part and the lower part of the heating section、Training the neural network off line according to the deviation of the furnace temperature set value output by the model and the actual furnace temperature by using the training sample data in the furnace, and adjusting the weight of each layer of the neural network to obtain a neural network model of the furnace temperature set value of the heating furnace; the model can output the furnace temperature set value of the heating furnace by acquiring the temperature of the steel billet entering the furnace, the temperature of the steel billet leaving the furnace, the production rhythm and the real-time data of the steel billet type;
the production rhythm is characterized by the steel tapping amount in unit time, the unit is root or piece/hour, the tapping interval of two steel billets is calculated in real time according to a photoelectric switch measuring signal for detecting the tapping of the steel billets, and the tapping amount R3 of the steel billets per hour is calculated;
(2) thermal load estimator
The thermal load estimator calculates the coal gas flow required under the current load based on the thermal efficiency of the heating furnace, the thermal efficiency adopts a nearest neighbor clustering RBF neural network model, the thermal load is calculated based on the thermal efficiency model, and the output of the thermal load estimator is the coal gas flow;
the input variable of the heat efficiency neural network model comprises the temperature of the billet steel entering the furnaceSteel tappingTemperature of the blankProduction rhythmOf the billet typeThe model output variable is the heating furnace thermal efficiencyTraining sample data including model input variables and heating furnace heat actual efficiency calculated values in the industrial field process are collected, and heating furnace heat efficiency is output according to the modelAnd the actual heat efficiency of the heating furnaceOff-line training is carried out on the neural network, and the weight of each layer of the neural network is adjusted to obtain a heating furnace thermal efficiency neural network model; the model can output the thermal efficiency value of the heating furnace by acquiring the temperature of the steel billet entering the furnace, the temperature of the steel billet leaving the furnace, the production rhythm and the real-time data of the type of the steel billet；
1) Actual thermal efficiencyThe calculation is as follows:
wherein,the heat released by the combustion of the gas,the total flow of the coal gas is the total flow of the coal gas,the heating value of the coal gas is the heating value of the coal gas,respectively the enthalpy value of the billet when entering and leaving the heating furnace,the heat absorbed by the billet steel in the heating furnace;
2) the thermal load estimator output is calculated as follows:
wherein,the heat quantity needed to be provided for the heating furnace,is a steel billet made ofRise toThe amount of heat that needs to be absorbed,is the specific heat of the steel billet,is the processing and treating capability of the processing, 、 respectively the initial temperature of the billet entering the furnace and the required temperature of the billet leaving the furnace,in order to realize the production rhythm,the quality of a single steel billet is the same as that of the single steel billet,the gas flow required by the heating furnace;
(3) air-fuel ratio optimizing controller
1) The optimization objective function is:
wherein,in order to be the weighting coefficients,respectively the furnace temperatures of the upper part and the lower part of the soaking section and the furnace temperatures of the upper part and the lower part of the heating section;
2) setting an optimized step size
Setting up, ，，
Wherein,the gas flow rates of the upper part and the lower part of the soaking section and the upper part and the lower part of the heating section are respectively,respectively the air flow of the upper part and the lower part of the soaking section and the air flow of the upper part and the lower part of the heating section,the air-fuel ratios of the upper part and the lower part of the soaking section and the upper part and the lower part of the heating section are respectively;
setting up，，WhereinFor the manually adjusted coefficients, according to this relationship, a total air quantity adjustment is given each timeWhen it is determined that the air-fuel ratio is increased by one
Wherein,is the air-fuel ratio increment at the upper part of the heating section;
calculating the air flow increment of the upper part and the lower part of the soaking section and the air flow increment of the upper part and the lower part of the heating section, namely the step length, which correspond to each optimization, respectively:
wherein,、、andair flow increment of the upper part and the lower part of the soaking section and the upper part and the lower part of the heating section respectively;
3) calculating an optimized air-fuel ratio by adopting a self-optimizing algorithm of a forward-backward method;
under the condition of constant gas flow, the air flow is changed, after the system responds, the change of the optimized target value J under the front and rear working conditions is compared, if the variable quantity of the optimized target value J is increased and obvious, the adjustment is beneficial, and the air flow is continuously adjusted according to the original direction; if the variation of the optimized target value J is reduced and remarkable, adjusting the air flow according to the reverse direction of the original air flow adjusting direction; and when the change of the optimization target value J is not obvious, stopping adjusting the air flow, and indicating that the current actual air-fuel ratio is the optimal air-fuel ratio.
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