CN103019097A - Optimal control system for steel rolling heating furnace - Google Patents

Optimal control system for steel rolling heating furnace Download PDF

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CN103019097A
CN103019097A CN2012104951261A CN201210495126A CN103019097A CN 103019097 A CN103019097 A CN 103019097A CN 2012104951261 A CN2012104951261 A CN 2012104951261A CN 201210495126 A CN201210495126 A CN 201210495126A CN 103019097 A CN103019097 A CN 103019097A
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temperature
steel billet
air
furnace
regulator
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CN103019097B (en
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于现军
李鹏
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BEIJING HEROOPSYS CONTROL TECHNOLOGY CO LTD
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BEIJING HEROOPSYS CONTROL TECHNOLOGY CO LTD
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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

A kind of heater for rolling steel Optimal Control System
Technical field
The present invention relates to heating furnace Optimized-control Technique field, relate in particular to a kind of heater for rolling steel Optimal Control System.
Background technology
Heater for rolling steel is to utilize thermal-flame that fuel produces when the burner hearth internal combustion and flue gas as thermal source, comes the steel billet that flows in the heating furnace, makes it reach the heating arrangement of regulation technological temperature.The control accuracy of heating furnace device production run operation requirements assurance heated medium tapping temperature, minimum firing rate and steel damage etc.
For realizing above-mentioned heating furnace productivity index, gordian technique is that steel billet advances furnace temperature according to rhythm of production on the one hand in the Heating Furnace Control, and the steel billet tapping temperature is adjusted furnace temperature, makes the tapping temperature of steel billet satisfy the demand of steel rolling, need to dynamically control furnace temperature; Mainly be to consider from aspects such as energy-conservation and material consumptions on the other hand, by seeking rational wind combustion ratio, so that the thermal efficiency of heating furnace is maximum, and by reducing the oxidizing atmosphere of soaking zone in the stove, decrease thereby reduce steel.Wherein in the Control for Kiln Temperature process, the raising of the dynamic setting of furnace temperature desired value and Control for Kiln Temperature precision is the emphasis of Control for Kiln Temperature.
On the dynamic setting of furnace temperature, " method for dynamic setting and control of hot-roll heating furnace temperature " of company limited of Baogang Stocks Trading Co. application arranged, application number 20051002485.0, publication number CN1840715A, it is to realize in four steps that this invention is divided into.The first step adopts the board briquette forecasting model to calculate the last temperature of section of slab place section, and its method is forward recursion; Second step is pressed slab displacement, the target temperature at each section of dynamic calculation slab section end; In the 3rd step, calculate the needed furnace gas temperature of slab present segment; In the 4th step, consider that the difference of current place slab is carried out the expertise weighted mean.Because the uncertainty of heating furnace work, as: the variation of rhythm of production, the difference (advancing cold base and hot base) of advancing the stove steel billet temperature, the type difference of steel billet etc., so adopt the forward recursion method to be difficult to the temperature of prediction steel billet, or predicted temperature is inaccurate, and these all will affect the effect of furnace temperature dynamic setting greatly.
On the control method of furnace temperature, adopt at present more method that the control of dual crossing amplitude limit, fuzzy self-adaption control technology, PREDICTIVE CONTROL and feedforward control technology etc. are arranged.On the industrial control technology magazine, phase in April, 2009, by the Jiangsu tin industry Zhang Xiuli of group, " the fuzzy self-adaption control strategy of Burning Process in Reheating Furnace " literary composition by name that Wu Dinghui writes, it applies to the Heating Furnace Control technology with the fuzzy control theory and gets on, the control algolithm that adopts remains conventional PID, only to P, I, the parameter of D has been carried out Fuzzy Calculation, set, the technological difficulties of fuzzy control technology are the formations of fuzzy controller, embody a concentrated reflection of the input language variable, the output language variable is selected, definite principle of degree of membership assignment table, so the process more complicated that the rule of fuzzy control is determined does not have good versatility; By " a kind of integrated control method of furnace outlet temperature " of Tsing-Hua University's application, application number 200810102875.7, publication number CN 101256418A, this invention is applied to feedback forecasting control and feedforward control in the control of furnace outlet furnace temperature.Its feedback forecasting control is to be based upon on many hypothesis, and the feedback forecasting model of foundation can not accurately reflect actual conditions, and the feedback forecasting model of perhaps setting up is inaccurate; Its Feed-forward Control Strategy is the feedforward as fluctuations in discharge of gas pressure or air pressure, but do not relate to the feedforward control to load variations.
In the control of wind combustion ratio, " air-fuel ratio control system of combustion heating furnace " application number 200810086051.5 by NGK Insulators Ltd's application, publication number CN 101270880A, need the given fixing oxygen content of smoke gas setting value of operating personnel in this invention, come the flowrate proportioning of correction air and fuel by the oxygen content value.Because best oxygen content is not changeless under different operating modes, therefore be difficult to really realize optimum operation, there is the problem that lags behind of measuring in Oxygen Amount in Flue Gas in addition, be difficult to satisfy the real-time of control, so this mode of adjusting the air fuel proportioning by the oxygen content table is difficult to do the trick.
In sum, existing technology still exists certain limitation and defective, therefore develops native system.
Summary of the invention
Problem to be solved by this invention is: invent a kind of heater for rolling steel Optimal Control System, guaranteeing to reduce gas consumption under the prerequisite that the steel billet outlet temperature is stable under the heating furnace different load, reduce steel and decrease.
The technical scheme that the present invention solves its technical matters employing is: be provided with furnace temperature on-line setup device in the native system, the thermal load estimator, the furnace temperature regulator of soaking zone upper and lower part and bringing-up section upper and lower part, furnace temperature feedforward regulator, gas flow regulator, air regulator, the optimization of air-fuel ratio controller, the actuator such as the measurement instrument such as furnace temperature, steel billet temperature, steel billet position, gas flow, air mass flow and gas flow variable valve, air flow rate adjustment valve;
The output of furnace temperature on-line setup device is as the set-point of furnace temperature regulator, and this regulator adopts pid control algorithm; The output of thermal load estimator be multiply by the load distribution coefficient as the measurement of each furnace temperature feedforward regulator, and this regulator adopts the PD control algolithm, and wherein the load distribution coefficient provides according to operating experience according to each several part load of heat ratio; The output sum of the output of furnace temperature regulator and furnace temperature feedforward regulator is as the setting value of gas flow regulator, and the gas flow regulator adopts pid control algorithm to realize the closed-loop control of gas flow; The product of the output of optimization of air-fuel ratio controller and measurement of gas flow value is as the setting value of air regulator, and air regulator adopts pid control algorithm to realize the closed-loop control of air mass flow;
(1) furnace temperature on-line setup device
Adopt nearest neighbor classifier RBF neural network identification system model in line computation furnace temperature setting value; The mode input variable includes the stove steel billet temperature
Figure 184290DEST_PATH_IMAGE001
, the steel billet temperature of coming out of the stove , rhythm of production , the steel billet type
Figure 569638DEST_PATH_IMAGE004
The model output variable comprises soaking zone upper and lower part furnace temperature setting value
Figure 991523DEST_PATH_IMAGE005
, , bringing-up section upper and lower part furnace temperature setting value
Figure 972434DEST_PATH_IMAGE007
,
Figure 940390DEST_PATH_IMAGE008
, by gathering mode input variable and the soaking zone upper and lower part temperature in the industry spot process
Figure 36522DEST_PATH_IMAGE009
,
Figure 59711DEST_PATH_IMAGE010
, bringing-up section upper and lower part temperature
Figure 924899DEST_PATH_IMAGE011
,
Figure 63756DEST_PATH_IMAGE012
In interior training sample data, according to the furnace temperature setting value of model output and the deviation of actual furnace temperature, neural network is carried out off-line training, adjust the weights of each layer of neural network, obtain furnace temperature of heating furnace setting value neural network model; Enter the stove steel billet temperature by collection, the steel billet temperature of coming out of the stove, rhythm of production, steel billet type real time data, model is with regard to exportable furnace temperature of heating furnace setting value;
Wherein rhythm of production is the tap that characterizes in the unit interval, and unit be " root or Parts Per Hour ", the optoelectronic switch measuring-signal that foundation is come out of the stove for detection of steel billet, and the interval of coming out of the stove of calculating in real time two steel billets per hour calculates the amount of the coming out of the stove R3 of steel billet;
(2) thermal load estimator
The thermal load estimator calculates the gas flow that needs based on thermal efficiency of heating furnace under preload, the thermal efficiency adopts nearest neighbor classifier RBF neural network model, and based on thermal efficiency model heat load calculation, the thermal load estimator is output as gas flow;
The input variable of thermal efficiency neural network model includes the stove steel billet temperature
Figure 709501DEST_PATH_IMAGE001
, the steel billet temperature of coming out of the stove , rhythm of production
Figure 944490DEST_PATH_IMAGE003
, the steel billet type
Figure 67298DEST_PATH_IMAGE004
, the model output variable is thermal efficiency of heating furnace
Figure 138022DEST_PATH_IMAGE013
, by gathering mode input variable in the industry spot process and the hot actual efficiency calculated value of heating furnace in interior training sample data, according to model output thermal efficiency of heating furnace
Figure 191429DEST_PATH_IMAGE013
With the heating furnace actual thermal efficiency
Figure 93526DEST_PATH_IMAGE014
Deviation, neural network is carried out off-line training, adjust the weights of each layer of neural network, obtain the thermal efficiency of heating furnace neural network model; Enter the stove steel billet temperature by collection, the steel billet temperature of coming out of the stove, rhythm of production, steel billet type real time data, model is with regard to exportable thermal efficiency of heating furnace value
Figure 574186DEST_PATH_IMAGE013
1) actual thermal efficiency
Figure 132206DEST_PATH_IMAGE014
Be calculated as follows:
Figure 723724DEST_PATH_IMAGE015
Figure 729595DEST_PATH_IMAGE016
Figure 646736DEST_PATH_IMAGE017
Wherein,
Figure 488790DEST_PATH_IMAGE018
Be the coal gas total flow, Be the coal gas calorific capacity,
Figure 698371DEST_PATH_IMAGE020
Be respectively that steel billet enters and the enthalpy when leaving heating furnace;
2) output of thermal load estimator is calculated as follows:
Figure 333883DEST_PATH_IMAGE021
Figure 600916DEST_PATH_IMAGE022
Figure 531012DEST_PATH_IMAGE024
Wherein
Figure 524376DEST_PATH_IMAGE025
The specific heat of steel billet,
Figure 278705DEST_PATH_IMAGE026
The processing processing power,
Figure 327302DEST_PATH_IMAGE027
,
Figure 850687DEST_PATH_IMAGE028
Respectively that steel billet advances the initial temperature of stove and the temperature that requires of coming out of the stove,
Figure 280531DEST_PATH_IMAGE029
Be rhythm of production,
Figure 522157DEST_PATH_IMAGE030
Be single billet quality;
(3) optimization of air-fuel ratio controller
1) the optimization aim function is:
Figure 859597DEST_PATH_IMAGE031
Wherein,
Figure 503068DEST_PATH_IMAGE032
Be weighting coefficient,
Figure 838235DEST_PATH_IMAGE033
Be respectively the furnace temperature of soaking zone upper and lower part and the furnace temperature of bringing-up section upper and lower part;
2) set Optimal Step Size
Set
Figure 380205DEST_PATH_IMAGE034
, ,
Figure 19314DEST_PATH_IMAGE036
,
Wherein,
Figure 53184DEST_PATH_IMAGE038
Be respectively the gas flow of soaking zone upper and lower part and bringing-up section upper and lower part,
Figure 935689DEST_PATH_IMAGE039
Be respectively the air mass flow of soaking zone upper and lower part and bringing-up section upper and lower part,
Figure 350490DEST_PATH_IMAGE040
It is respectively the air-fuel ratio of soaking zone upper and lower part and bringing-up section upper and lower part;
Set
Figure 293038DEST_PATH_IMAGE041
,
Figure 730973DEST_PATH_IMAGE042
,
Figure 151590DEST_PATH_IMAGE043
, wherein The coefficient that the behaviour wage adjustment is whole according to this relation, provides a total air adjustment amount at every turn
Figure 550659DEST_PATH_IMAGE045
The time, corresponding air-fuel ratio increment then;
Figure 475889DEST_PATH_IMAGE046
Corresponding soaking zone upper and lower part and bringing-up section upper and lower part air mass flow increment when calculating every suboptimization, i.e. step-length is respectively:
Figure 824011DEST_PATH_IMAGE048
Figure 108362DEST_PATH_IMAGE049
Figure 832473DEST_PATH_IMAGE050
Figure 860472DEST_PATH_IMAGE051
3) adopt advance and retreat method adaptive searching optimal algorithm, calculation optimization air-fuel ratio;
Under constant gas flow condition, by increasing (or minimizing) air mass flow, after system responses, the variation of optimization target values J under the operating mode relatively, if optimization target values J variable quantity increases and significantly, illustrates that this adjustment is useful, continue to adjust air mass flow by original direction; If optimization target values J variable quantity is minimizing and remarkable, adjusts the opposite direction of direction by original air mass flow and adjust air mass flow; When optimization target values J changes when not obvious, then stop to adjust air mass flow, show that the air-fuel ratio of current reality is optimal air-fuel ratio.
Usefulness of the present invention is:
Furnace temperature on-line setup device is according to the situation of change of each procedure parameter in producing, and steel billet tapping temperature is as required set the furnace temperature setting value of each section dynamically, thereby has guaranteed the control accuracy of steel billet tapping temperature.
The furnace temperature feedforward regulator can with the variation antedating response of load to gas flow, can effectively overcome the adverse effect that process lag brings, so that Control for Kiln Temperature rapidity and stationarity are guaranteed when working conditions change.
The neural network of in furnace temperature on-line setup device and thermal efficiency of heating furnace line modeling, using, it has the identification precision height, characteristics that can point-device Approximation of Arbitrary Nonlinear Function, and neural network do not rely on certain concrete equipment, has good versatility when modeling.
Advance and retreat method optimisation strategy is according to combustion efficiency, seeks best technological parameter, obtain the economy of combustion process, do not rely on the instrument of any precision, do not rely on the fuel value analysis meter and can find optimal air-fuel ratio, realize the control of combustion thermal efficiency largest optimization.
(4) description of drawings
Accompanying drawing 1 furnace temperature on-line setup device block diagram;
Accompanying drawing 2 thermal efficiency of heating furnace model framework charts;
Accompanying drawing 3 optimized control system for heating stove block diagrams;
Accompanying drawing 4 RBF neural network structure figure;
Accompanying drawing 5 RBF neural network nearest neighbor classifier algorithm flow charts;
Accompanying drawing 6 is from the optimizing flow chart;
(5) embodiment
Embodiment:
System's control block diagram as shown in Figure 3.
1, furnace temperature on-line setup device
1) sets up the rhythm of production model
Rhythm of production is the tap that characterizes in the unit interval, and unit be " root or Parts Per Hour ", the optoelectronic switch measuring-signal that foundation is come out of the stove for detection of steel billet, and the interval of coming out of the stove of calculating in real time two steel billets per hour calculates the amount of the coming out of the stove R3 of steel billet;
R3=3600/t(k)-t(k-1)）
T (k) detects the moment that steel billet is come out of the stove for photoelectricity switch, the moment that t (k-1) comes out of the stove for last steel billet;
2) steel billet type
Once distinguish a given sequence number according to the steel billet kind, such as 1,2,3,4 ..., the steel billet type is as the numerical value of a quantification simultaneously.Wherein, the selection of steel billet type needs artificial input.
3) training sample obtains
Obtain and include the stove steel billet temperature
Figure 42055DEST_PATH_IMAGE001
, the steel billet temperature of coming out of the stove
Figure 294044DEST_PATH_IMAGE002
, rhythm of production
Figure 193867DEST_PATH_IMAGE003
, the steel billet type
Figure 759978DEST_PATH_IMAGE004
, soaking zone upper and lower part furnace temperature
Figure 874695DEST_PATH_IMAGE052
,
Figure 235270DEST_PATH_IMAGE053
, bringing-up section upper and lower part furnace temperature
Figure 622389DEST_PATH_IMAGE011
, In 50 groups of interior production process data that opereating specification is larger, and with 150 groups of historical datas that these data gather together with industry spot, amount to 200 groups of data as the training sample of thermal efficiency neural network model;
3) furnace temperature on-line setup device neural network model
Adopt the learning algorithm of RBF neural network nearest neighbor classifier, choose cluster radius
Figure 945103DEST_PATH_IMAGE054
, radius correction step-length , error threshold
Figure 662578DEST_PATH_IMAGE056
, model as shown in Figure 1;
Being input as of model: enter the stove steel billet temperature
Figure 836070DEST_PATH_IMAGE001
, the steel billet temperature of coming out of the stove , rhythm of production
Figure 814707DEST_PATH_IMAGE003
, the steel billet type
Figure 973156DEST_PATH_IMAGE004
, model is output as: soaking zone top and the bottom temperature
Figure 684760DEST_PATH_IMAGE057
,
Figure 549948DEST_PATH_IMAGE058
, bringing-up section top and the bottom temperature
Figure 501855DEST_PATH_IMAGE059
,
Figure 85283DEST_PATH_IMAGE060
Export according to model ,
Figure 382589DEST_PATH_IMAGE058
,
Figure 692348DEST_PATH_IMAGE059
,
Figure 74656DEST_PATH_IMAGE060
With actual soaking zone top and the bottom temperature
Figure 128063DEST_PATH_IMAGE052
,
Figure 967843DEST_PATH_IMAGE053
, bringing-up section top and the bottom temperature
Figure 510820DEST_PATH_IMAGE011
,
Figure 803261DEST_PATH_IMAGE012
Deviation, adjust the weights of each layer of neural network, set up the dynamic Optimized model of furnace temperature setting value.
The nearest neighbor classifier learning algorithm is a kind of online adaptive clustering learning algorithm, and RBF network Basis Function Center is chosen by clustering algorithm, and the width of Gaussian function is determined that by cluster radius hidden layer is determined by the arithmetic mean of each output vector to the weights of output layer.The algorithm detailed process is as follows: as shown in Figure 5
1. determine a suitable cluster radius , radius correction step-length And error threshold
Figure 881572DEST_PATH_IMAGE063
, define a vector
Figure 238473DEST_PATH_IMAGE064
Deposit the output sum that belongs to all kinds of, define a counter
Figure 633682DEST_PATH_IMAGE065
Statistics Different categories of samples number,
Figure 182476DEST_PATH_IMAGE066
Deposit weights
Figure 270517DEST_PATH_IMAGE067
, wherein
Figure 599867DEST_PATH_IMAGE030
Be the classification number,
Figure 798768DEST_PATH_IMAGE068
The The center of individual class.
2. to first pair of data
Figure 274059DEST_PATH_IMAGE070
, make it constitute a class by itself, i.e. the center
Figure 28389DEST_PATH_IMAGE071
, with season
Figure 827718DEST_PATH_IMAGE072
,
Figure 351103DEST_PATH_IMAGE073
=1.To this RBF network that only has a hidden unit, the center of hidden unit is
Figure 780947DEST_PATH_IMAGE074
, hidden unit to the weights of output layer is
Figure 68578DEST_PATH_IMAGE075
3. to second pair of data
Figure 609281DEST_PATH_IMAGE076
, obtain
Figure 252752DEST_PATH_IMAGE077
Arrive
Figure 587918DEST_PATH_IMAGE078
Distance
Figure 379156DEST_PATH_IMAGE079
If
Figure 457971DEST_PATH_IMAGE080
Then
Figure 955948DEST_PATH_IMAGE078
For
Figure 275065DEST_PATH_IMAGE077
Nearest neighbor classifier, the order
Figure 491283DEST_PATH_IMAGE081
,
Figure 373788DEST_PATH_IMAGE082
,
Figure 726272DEST_PATH_IMAGE075
If
Figure 731137DEST_PATH_IMAGE083
, then will
Figure 169072DEST_PATH_IMAGE084
As a new cluster centre, and order , , =1.Add a Hidden unit in the RBF of above-mentioned foundation network, this Hidden unit to the weights of output layer is again
Figure 209261DEST_PATH_IMAGE088
4. consider
Figure 433569DEST_PATH_IMAGE069
Individual sample data pair
Figure 760645DEST_PATH_IMAGE089
The time,
Figure 858045DEST_PATH_IMAGE090
, suppose existing
Figure 270572DEST_PATH_IMAGE026
Individual cluster centre, its mid point is respectively ,
Figure 214574DEST_PATH_IMAGE084
..., , the RBF network of above-mentioned foundation is existing Individual Hidden unit, utilize following formula:
Figure 198077DEST_PATH_IMAGE092
Obtain
Figure 545750DEST_PATH_IMAGE089
Arrive this The distance of individual cluster centre is established
Figure 559023DEST_PATH_IMAGE093
Be the minimum value of these distances, namely
Figure 725562DEST_PATH_IMAGE094
For
Figure 881737DEST_PATH_IMAGE089
Nearest neighbor classifier, so: if
Figure 678791DEST_PATH_IMAGE095
, then will
Figure 287627DEST_PATH_IMAGE089
As a new cluster centre,
Figure 274169DEST_PATH_IMAGE096
,
Figure 284850DEST_PATH_IMAGE097
,
Figure 252806DEST_PATH_IMAGE098
Figure 411255DEST_PATH_IMAGE099
, 1, to front
Figure 988047DEST_PATH_IMAGE101
Individual class
Figure 126904DEST_PATH_IMAGE102
With Value remains unchanged, and adds in the RBF of above-mentioned foundation network again
Figure 271633DEST_PATH_IMAGE026
Individual Hidden unit.If
Figure 256906DEST_PATH_IMAGE104
, be calculated as follows:
Figure 628982DEST_PATH_IMAGE105
Figure 699706DEST_PATH_IMAGE099
,
Figure 753113DEST_PATH_IMAGE106
1, keep
Figure 405942DEST_PATH_IMAGE102
With
Figure 886602DEST_PATH_IMAGE103
Figure 179043DEST_PATH_IMAGE107
Be worth constant.Hidden unit to the weights of output layer is
Figure 36140DEST_PATH_IMAGE108
Figure 792744DEST_PATH_IMAGE109
5. after all input samples have been considered, calculate the RBF network and be output as:
Figure 444305DEST_PATH_IMAGE110
Figure 489621DEST_PATH_IMAGE111
By the neural network that trains, given one enters the stove steel billet temperature
Figure 196415DEST_PATH_IMAGE001
, the steel billet temperature of coming out of the stove
Figure 745208DEST_PATH_IMAGE002
, rhythm of production , the steel billet type
Figure 162600DEST_PATH_IMAGE004
, a soaking zone top and the bottom desired temperature is arranged
Figure 95921DEST_PATH_IMAGE057
,
Figure 764800DEST_PATH_IMAGE058
, bringing-up section top and the bottom desired temperature
Figure 23743DEST_PATH_IMAGE059
,
Figure 325542DEST_PATH_IMAGE060
Corresponding.
2, thermal load estimator
The thermal efficiency and the principle of heat balance design condition that utilize the thermal efficiency of heating furnace regression model to obtain change thermal load demand gas flow, and its output coal gas flow is as the measured value of gas flow regulator.
1) training sample obtains
Obtain and include the stove steel billet temperature
Figure 328133DEST_PATH_IMAGE001
, the steel billet temperature of coming out of the stove
Figure 851519DEST_PATH_IMAGE002
, rhythm of production
Figure 78101DEST_PATH_IMAGE003
, the steel billet type
Figure 319726DEST_PATH_IMAGE004
, in 50 groups of interior production process data that opereating specification is larger, and with the 150 group historical datas of these data together with the industry spot collection, and calculate actual thermal efficiency corresponding under every group of data
Figure 860429DEST_PATH_IMAGE014
, amount to 200 groups of data as the training sample of thermal efficiency neural network model, wherein actual thermal efficiency
Figure 151422DEST_PATH_IMAGE014
Be calculated as follows:
Figure 752167DEST_PATH_IMAGE112
Figure 215510DEST_PATH_IMAGE113
Figure 559903DEST_PATH_IMAGE114
Wherein,
Figure 120198DEST_PATH_IMAGE115
Be the coal gas total flow,
Figure 626265DEST_PATH_IMAGE019
Be the coal gas calorific capacity,
Figure 842483DEST_PATH_IMAGE116
Be respectively that steel billet enters and the enthalpy when leaving heating furnace;
2) set up thermal efficiency model
Select the RBF neural network as thermal efficiency model, wherein the input of neural network and output as shown in Figure 2, the adopting parameters cluster radius
Figure 538038DEST_PATH_IMAGE054
, radius correction step-length
Figure 890522DEST_PATH_IMAGE117
, error threshold
Figure 567491DEST_PATH_IMAGE118
, algorithm is consistent with furnace temperature on-line setup device neural network model;
By the neural network that trains, given one enters the stove steel billet temperature
Figure 5425DEST_PATH_IMAGE001
, the steel billet temperature of coming out of the stove
Figure 753938DEST_PATH_IMAGE002
, rhythm of production
Figure 960929DEST_PATH_IMAGE003
, the steel billet type
Figure 74378DEST_PATH_IMAGE004
, a thermal efficiency of heating furnace is arranged
Figure 311194DEST_PATH_IMAGE119
Corresponding.
3) output of thermal load estimator is calculated as follows:
Figure 535501DEST_PATH_IMAGE120
Figure 862578DEST_PATH_IMAGE121
Figure 209245DEST_PATH_IMAGE122
Wherein
Figure 384192DEST_PATH_IMAGE013
Model output thermal efficiency of heating furnace,
Figure 565774DEST_PATH_IMAGE025
The specific heat of steel billet,
Figure 834076DEST_PATH_IMAGE026
The processing processing power,
Figure 733899DEST_PATH_IMAGE027
, Respectively that steel billet advances the initial temperature of stove and the temperature that requires of coming out of the stove, Be rhythm of production,
Figure 24569DEST_PATH_IMAGE030
Be single billet quality;
3, optimization of air-fuel ratio controller
1) optimization aim function
Figure 146108DEST_PATH_IMAGE031
Wherein, get
Figure 827494DEST_PATH_IMAGE032
Span 0.2～0.3
2) advance and retreat method adaptive searching optimal algorithm
As shown in Figure 6, the following ε of step:
At first set the step-length SOP=of optimizing =γ *
Figure 780724DEST_PATH_IMAGE123
(span 1% ~ 2% of γ), wherein
Figure 389560DEST_PATH_IMAGE123
Be current air total flow, permissible error ε, the span of ε is 1 ~ 2, counter
Figure 359790DEST_PATH_IMAGE124
1. constant gas flow definite value outputs to air mass flow valve position counter with current air mass flow as the air mass flow setting value, and the optimal controller running mark is set to ON, optimization aim functional value of record after 1 ~ 2 minute
Figure 370471DEST_PATH_IMAGE125
2. selecting increases air mass flow, puts
Figure 338427DEST_PATH_IMAGE126
, the step-length of air mass flow setting value increment for arranging
Figure 247608DEST_PATH_IMAGE127
SOP, the delivery air flow setting value is to air mass flow valve position counter; Revise current air-fuel ratio, turned to for the 8. step;
If 3. The time, seek in the right direction is described, continue along this direction finding,
Figure 89980DEST_PATH_IMAGE129
Value be assigned to
Figure 228837DEST_PATH_IMAGE130
, turned to for the 2. step; If The time, judge to be for the first time optimizing, namely
Figure 124298DEST_PATH_IMAGE132
If,
Figure 109571DEST_PATH_IMAGE133
The time, then say a name investigation mistake, turned to for the 4. step.If
Figure 730914DEST_PATH_IMAGE134
The time, and
Figure 536059DEST_PATH_IMAGE135
The time, then current air-fuel ratio is optimum condition, and this optimizing finishes, and the optimal controller running mark is set to OFF.If
Figure 589466DEST_PATH_IMAGE134
The time, and
Figure 491563DEST_PATH_IMAGE136
The time, then variable step is sought and to be put SOP=-0.25*SOP, turns to for the 2. step.
4. selecting increases air mass flow, puts
Figure 972223DEST_PATH_IMAGE126
, the step-length of air mass flow setting value increment for arranging (-SOP), the delivery air flow setting value is to air mass flow valve position counter; Revise current air-fuel ratio, turned to for the 8. step;
If 5.
Figure 121761DEST_PATH_IMAGE128
The time, illustrate that investigation is correct, continue to seek along this direction,
Figure 894676DEST_PATH_IMAGE129
Value be assigned to
Figure 546238DEST_PATH_IMAGE137
, turned to for the 4. step;
If 6. The time, and
Figure 783501DEST_PATH_IMAGE135
The time, then current air-fuel ratio is optimum condition, and this optimizing finishes, and the optimal controller running mark is set to OFF.
If 7.
Figure 597873DEST_PATH_IMAGE131
The time, and
Figure 420336DEST_PATH_IMAGE136
The time, then variable step is sought and to be put SOP=-0.25*SOP, turns to for the 2. step.
8. target function value calculates
With the stable state real-time process measured value behind the dynamic response, by optimization aim function calculation target function value Begin to calculate after namely optimizing a period of time behind the output action auto levelizer, the time is depended on the dynamic response time of process, generally gets concerning heating furnace 1 ~ 2 minute.Return the original position that turns over.
3) each several part is optimized air-fuel ratio calculating
By 2) the total air mass flow increment that obtains
Figure 197854DEST_PATH_IMAGE045
, calculate the air mass flow increment of soaking zone top, soaking zone bottom, bringing-up section top, bringing-up section bottom;
Figure 866732DEST_PATH_IMAGE047
Get the ratio coefficient of the air-fuel ratio of soaking zone and bringing-up section
The air increment that then has soaking zone and bringing-up section to distribute is respectively
Figure 676742DEST_PATH_IMAGE048
Figure 202719DEST_PATH_IMAGE050
Figure 180033DEST_PATH_IMAGE051
Calculate the optimization air-fuel ratio of soaking zone top, soaking zone bottom, bringing-up section top, bringing-up section bottom
Figure 421659DEST_PATH_IMAGE139
Figure 962361DEST_PATH_IMAGE140
Figure 402570DEST_PATH_IMAGE141
Figure 3316DEST_PATH_IMAGE142
4, design of Regulator
(1) furnace temperature regulator
The setting of soaking zone top furnace temperature regulator is from the output of furnace temperature on-line setup device, and the furnace temperature regulator adopts pid control mode, P span 80 ~ 120, I span 200 ~ 300, D value 30 ~ 50.
The setting of soaking zone lower furnace temperature regulator is from the output of furnace temperature on-line setup device, and the furnace temperature regulator adopts pid control mode, P span 90 ~ 130, I span 220 ~ 310, D value 40 ~ 50.
The setting of bringing-up section top furnace temperature regulator is from the output of furnace temperature on-line setup device, and the furnace temperature regulator adopts pid control mode, P span 70 ~ 100, I span 180 ~ 250, D value 30 ~ 50.
The setting of bringing-up section lower furnace temperature regulator is from the output of furnace temperature on-line setup device, and the furnace temperature regulator adopts pid control mode, P span 90 ~ 120, I span 210 ~ 290, D value 40 ~ 50.
(2) furnace temperature feedforward regulator
The load distribution coefficient value is:
Figure 466658DEST_PATH_IMAGE143
Wherein
Figure 122636DEST_PATH_IMAGE144
For soaking zone top load distribution coefficient,
Figure 355034DEST_PATH_IMAGE145
For soaking zone bottom load distribution coefficient,
Figure 126681DEST_PATH_IMAGE146
For bringing-up section top load distribution coefficient,
Figure 77320DEST_PATH_IMAGE147
Be bringing-up section bottom load distribution coefficient.
Soaking zone top furnace temperature feedforward regulator adopts the PD control algolithm, and wherein the P span 30 ~ 60, D value 50 ~ 70;
Soaking zone bottom furnace temperature feedforward regulator adopts the PD control algolithm, and wherein the P span 40 ~ 70, D value 60 ~ 80;
Bringing-up section top furnace temperature feedforward regulator adopts the PD control algolithm, and wherein the P span 30 ~ 60, D value 50 ~ 70;
Bringing-up section bottom furnace temperature feedforward regulator adopts the PD control algolithm, and wherein the P span 40 ~ 70, D value 50 ~ 80;
(3) gas flow regulator
The setting value of soaking zone top gas flow regulator is the output of furnace temperature regulator and the output sum of furnace temperature feedforward regulator, and the gas flow regulator adopts pid control mode, and wherein the P span 60 ~ 100, I span 150 ~ 200, D value 20 ~ 40.
The setting value of soaking zone lower coal airshed regulator is the output of furnace temperature regulator and the output sum of furnace temperature feedforward regulator, and the gas flow regulator adopts pid control mode, and wherein the P span 70 ~ 90, I span 120 ~ 150, D value 30 ~ 40.
The setting value of bringing-up section top gas flow regulator is the output of furnace temperature regulator and the output sum of furnace temperature feedforward regulator, and the gas flow regulator adopts pid control mode, and wherein the P span 60 ~ 100, I span 150 ~ 200, D value 20 ~ 40.
The setting value of bringing-up section lower coal airshed regulator is the output of furnace temperature regulator and the output sum of furnace temperature feedforward regulator, and the gas flow regulator adopts pid control mode, and wherein the P span 70 ~ 90, I span 120 ~ 150, D value 30 ~ 40.
(4) air regulator
The setting value of soaking zone upper air flow regulator is empty right product than optimal controller and current gas flow, and measured value is current air-flow measurement value, adopts the control mode of PID to regulate air door.Air regulator P span 50 ~ 80, I span 100 ~ 150, D value 30 ~ 40.
The setting value of soaking zone bottom air regulator is empty right product than optimal controller and current gas flow, and measured value is current air-flow measurement value, adopts the control mode of PID to regulate air door.Air regulator P span 60 ~ 90, I span 110 ~ 140, D value 20 ~ 30.
The setting value of bringing-up section upper air flow regulator is empty right product than optimal controller and current gas flow, and measured value is current air-flow measurement value, adopts the control mode of PID to regulate air door.Air regulator P span 50 ~ 80, I span 100 ~ 150, D value 30 ~ 40.
The setting value of bringing-up section bottom air regulator is empty right product than optimal controller and current gas flow, and measured value is current air-flow measurement value, adopts the control mode of PID to regulate air door.Air regulator P span 60 ~ 90, I span 110 ~ 140, D value 20 ~ 30.

Claims (1)

1. heater for rolling steel Optimal Control System, it is characterized in that being provided with furnace temperature on-line setup device, the thermal load estimator, the furnace temperature regulator of soaking zone upper and lower part and bringing-up section upper and lower part, furnace temperature feedforward regulator, gas flow regulator, air regulator, the optimization of air-fuel ratio controller, the actuator such as the measurement instrument such as furnace temperature, steel billet temperature, steel billet position, gas flow, air mass flow and gas flow variable valve, air flow rate adjustment valve;
The output of furnace temperature on-line setup device is as the set-point of furnace temperature regulator, and this regulator adopts pid control algorithm; The output of thermal load estimator be multiply by the load distribution coefficient as the measurement of each furnace temperature feedforward regulator, and this regulator adopts the PD control algolithm, and wherein the load distribution coefficient provides according to operating experience according to each several part load of heat ratio; The output sum of the output of furnace temperature regulator and furnace temperature feedforward regulator is as the setting value of gas flow regulator, and the gas flow regulator adopts pid control algorithm to realize the closed-loop control of gas flow; The product of the output of optimization of air-fuel ratio controller and measurement of gas flow value is as the setting value of air regulator, and air regulator adopts pid control algorithm to realize the closed-loop control of air mass flow;
(1) furnace temperature on-line setup device
Adopt nearest neighbor classifier RBF neural network identification system model in line computation furnace temperature setting value; The mode input variable includes the stove steel billet temperature
Figure 683221DEST_PATH_IMAGE001
, the steel billet temperature of coming out of the stove
Figure 412142DEST_PATH_IMAGE002
, rhythm of production
Figure 490957DEST_PATH_IMAGE003
, the steel billet type
Figure 51251DEST_PATH_IMAGE004
The model output variable comprises soaking zone upper and lower part furnace temperature setting value
Figure 822898DEST_PATH_IMAGE005
,
Figure 773536DEST_PATH_IMAGE006
, bringing-up section upper and lower part furnace temperature setting value
Figure 967626DEST_PATH_IMAGE007
,
Figure 320110DEST_PATH_IMAGE008
, by gathering mode input variable and the soaking zone upper and lower part temperature in the industry spot process
Figure 262658DEST_PATH_IMAGE009
,
Figure 700593DEST_PATH_IMAGE010
, bringing-up section upper and lower part temperature
Figure 183527DEST_PATH_IMAGE011
,
Figure 656096DEST_PATH_IMAGE012
In interior training sample data, according to the furnace temperature setting value of model output and the deviation of actual furnace temperature, neural network is carried out off-line training, adjust the weights of each layer of neural network, obtain furnace temperature of heating furnace setting value neural network model; Enter the stove steel billet temperature by collection, the steel billet temperature of coming out of the stove, rhythm of production, steel billet type real time data, model is with regard to exportable furnace temperature of heating furnace setting value;
Wherein rhythm of production is the tap that characterizes in the unit interval, and unit be " root or Parts Per Hour ", the optoelectronic switch measuring-signal that foundation is come out of the stove for detection of steel billet, and the interval of coming out of the stove of calculating in real time two steel billets per hour calculates the amount of the coming out of the stove R3 of steel billet;
(2) thermal load estimator
The thermal load estimator calculates the gas flow that needs based on thermal efficiency of heating furnace under preload, the thermal efficiency adopts nearest neighbor classifier RBF neural network model, and based on thermal efficiency model heat load calculation, the thermal load estimator is output as gas flow;
The input variable of thermal efficiency neural network model includes the stove steel billet temperature
Figure 769546DEST_PATH_IMAGE001
, the steel billet temperature of coming out of the stove
Figure 507826DEST_PATH_IMAGE002
, rhythm of production
Figure 732134DEST_PATH_IMAGE003
, the steel billet type
Figure 793631DEST_PATH_IMAGE004
, the model output variable is thermal efficiency of heating furnace
Figure 77982DEST_PATH_IMAGE013
, by gathering mode input variable in the industry spot process and the hot actual efficiency calculated value of heating furnace in interior training sample data, according to model output thermal efficiency of heating furnace
Figure 552825DEST_PATH_IMAGE013
With the heating furnace actual thermal efficiency
Figure 580824DEST_PATH_IMAGE014
Deviation, neural network is carried out off-line training, adjust the weights of each layer of neural network, obtain the thermal efficiency of heating furnace neural network model; Enter the stove steel billet temperature by collection, the steel billet temperature of coming out of the stove, rhythm of production, steel billet type real time data, model is with regard to exportable thermal efficiency of heating furnace value
Figure 762407DEST_PATH_IMAGE013
1) actual thermal efficiency
Figure 263664DEST_PATH_IMAGE014
Be calculated as follows:
Figure 163487DEST_PATH_IMAGE015
Figure 729598DEST_PATH_IMAGE016
Figure 31266DEST_PATH_IMAGE017
Wherein,
Figure 454157DEST_PATH_IMAGE018
Be the coal gas total flow,
Figure 841276DEST_PATH_IMAGE019
Be the coal gas calorific capacity,
Figure 211077DEST_PATH_IMAGE020
Be respectively that steel billet enters and the enthalpy when leaving heating furnace;
2) output of thermal load estimator is calculated as follows:
Figure 914722DEST_PATH_IMAGE021
Figure 711777DEST_PATH_IMAGE022
Figure 320613DEST_PATH_IMAGE023
Wherein The specific heat of steel billet,
Figure 535060DEST_PATH_IMAGE026
The processing processing power,
Figure 631192DEST_PATH_IMAGE027
,
Figure 654380DEST_PATH_IMAGE028
Respectively that steel billet advances the initial temperature of stove and the temperature that requires of coming out of the stove,
Figure 519568DEST_PATH_IMAGE029
Be rhythm of production,
Figure 658425DEST_PATH_IMAGE030
Be single billet quality;
(3) optimization of air-fuel ratio controller
1) the optimization aim function is:
Figure 304170DEST_PATH_IMAGE031
Wherein, Be weighting coefficient,
Figure 539159DEST_PATH_IMAGE033
Be respectively the furnace temperature of soaking zone upper and lower part and the furnace temperature of bringing-up section upper and lower part;
2) set Optimal Step Size
Set
Figure 848918DEST_PATH_IMAGE034
, ,
Figure 786098DEST_PATH_IMAGE036
,
Wherein,
Figure 168855DEST_PATH_IMAGE038
Be respectively the gas flow of soaking zone upper and lower part and bringing-up section upper and lower part,
Figure 461296DEST_PATH_IMAGE039
Be respectively the air mass flow of soaking zone upper and lower part and bringing-up section upper and lower part,
Figure 318394DEST_PATH_IMAGE040
It is respectively the air-fuel ratio of soaking zone upper and lower part and bringing-up section upper and lower part;
Set ,
Figure 975826DEST_PATH_IMAGE042
,
Figure 21142DEST_PATH_IMAGE043
, wherein
Figure 416352DEST_PATH_IMAGE044
The coefficient that the behaviour wage adjustment is whole according to this relation, provides a total air adjustment amount at every turn
Figure 27461DEST_PATH_IMAGE045
The time, corresponding air-fuel ratio increment then;
Figure 115503DEST_PATH_IMAGE046
Figure 382536DEST_PATH_IMAGE047
Corresponding soaking zone upper and lower part and bringing-up section upper and lower part air mass flow increment when calculating every suboptimization, i.e. step-length is respectively:
Figure 394486DEST_PATH_IMAGE048
Figure 56728DEST_PATH_IMAGE050
Figure 873375DEST_PATH_IMAGE051
3) adopt advance and retreat method adaptive searching optimal algorithm, calculation optimization air-fuel ratio;
Under constant gas flow condition, by increasing (or minimizing) air mass flow, after system responses, the variation of optimization target values J under the operating mode relatively, if optimization target values J variable quantity increases and significantly, illustrates that this adjustment is useful, continue to adjust air mass flow by original direction; If optimization target values J variable quantity is minimizing and remarkable, adjusts the opposite direction of direction by original air mass flow and adjust air mass flow; When optimization target values J changes when not obvious, then stop to adjust air mass flow, show that the air-fuel ratio of current reality is optimal air-fuel ratio.
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CN108694288A (en) * 2018-05-29 2018-10-23 中南大学 The method of temperature is set under quick obtaining walking beam furnace difference yield
CN108694288B (en) * 2018-05-29 2021-04-30 中南大学 Method for rapidly acquiring set temperatures of walking beam type billet heating furnace under different yields
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