CN109002068A - A kind of temperature optimization control method of quirk - Google Patents
A kind of temperature optimization control method of quirk Download PDFInfo
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- CN109002068A CN109002068A CN201811042776.4A CN201811042776A CN109002068A CN 109002068 A CN109002068 A CN 109002068A CN 201811042776 A CN201811042776 A CN 201811042776A CN 109002068 A CN109002068 A CN 109002068A
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- temperature
- fire path
- fuzzy
- quirk
- path temperature
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
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- C—CHEMISTRY; METALLURGY
- C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
- C10B—DESTRUCTIVE DISTILLATION OF CARBONACEOUS MATERIALS FOR PRODUCTION OF GAS, COKE, TAR, OR SIMILAR MATERIALS
- C10B21/00—Heating of coke ovens with combustible gases
- C10B21/10—Regulating and controlling the combustion
Abstract
The invention discloses a kind of temperature optimization control methods of quirk to detect the quirk problem at current time, according to the target value of preset fire path temperature, using the temperature of variable universe fuzzy-adaptation PID control quirk using based on the fire path temperature detection model for improving PLS.Of the invention realizes the indirect measurement to fire path temperature based on the fire path temperature detection model for improving PLS, reduce cost, efficiency is improved, while using variable universe fuzzy, it can be achieved that the advantages that temperature settling tune is short, stable state accuracy is high, steady-state error is small, overshoot is small.
Description
Technical field
The present invention relates to the temperature optimization control methods of a kind of temprature control method more particularly to a kind of quirk.
Background technique
Coke oven is industrial furnace complicated in metallurgical industry, it is alternately arranged by multiple carbonization chambers and combustion chamber.
Coal gas and air enter combustion chamber diffusion, burning after regenerative chamber preheats, and the heat of generation is transmitted to carbonization chamber through furnace wall；Coal is in charcoal
Change and carry out high-temperature retorting in room, form coke, the exhaust gas for generation of burning is discharged after the recycling of regenerative chamber waste heat, handed over every 20min
Change the flow direction of a coal gas, air and exhaust gas.And in process of coking, fire path temperature is a vital technological parameter,
It is directly related to coke quality and converter life, but mainly passes through warm galvanic couple for the measurement of fire path temperature at present and surveys
Amount, but it is difficult to install, and artificial experience is relied on mostly since coking temperature is excessively high easy to damage, while to the control of fire path temperature,
Or simple pid control algorithm, precision is lower, and adjustment time is too long.
Summary of the invention
In order to solve the direct measurement cost of prior art fire path temperature it is high and easily go wrong, fire path temperature control it is not excellent enough
The problem of change, it is an object of the present invention to a kind of temperature optimization control method of quirk be proposed, by the quirk temperature for improving PLS
Detection model is spent to predict quirk Current Temperatures, realizes the optimal control to quirk problem using variable universe PID control method, it is real
The advantages that adjustment time is short, stable state accuracy is high, steady-state error is small, overshoot is small is showed.
Used technical solution is the present invention to solve above-mentioned technical problem:
A kind of temperature optimization control method of quirk, which comprises the steps of: (1) according to heating gas stream
Amount, regenerator top temperature, the historical data of coal gas main pipe pressure, producer gas flow, flue temperature, these variables, establish base
In the fire path temperature detection model for improving PLS；(2) above-mentioned variable is acquired in real time, according to described based on the fire path temperature for improving PLS
The quirk detected value of detection model and preset fire path temperature target value calculate fire path temperature deviation；(3) according to quirk
Temperature deviation value utilizes variable universe fuzzy-adaptation PID control fire path temperature.
The step of wherein foundation of the step (1) is based on the fire path temperature detection model for improving PLS is as follows:
(a) heating gas flow X1, regenerator top temperature X2, coal gas main pipe pressure X3, producer gas flow X4, cigarette are collected
The historical data of these variables of channel temp X5, as sample data, that is, input variable X of the model, fire path temperature Y1 is as the mould
The output variable Y of type；
(b) input variable X is pre-processed, firstly, being standardized transformation to input variable X, eliminates data
Secondly the influence of guiding principle amount rejects big error information；
(c) it according to pretreated input variable, establishes based on the fire path temperature detection model for improving PLS:
Assuming that input variable X and output variable Y is linearly related, i.e. 1., wherein B is regression coefficient matrix to Y=XB+E, and E is
Residual matrix；
When improving PLS modeling, X and Y is decomposed, is obtainedWherein T, U are score matrix, and P, Q are load moment
Battle array, E1, E2 are residual matrix；
2. according to formula, linear regression opening relationships: U=CT+R is utilized between vector, wherein R is residual matrix, and C is back
Attribution: C=UTT/(TTT), it is so as to calculate regression coefficient matrix
In order to guarantee the precision of prediction of model, model is verified using mean square error and root-mean-square error.
When receiving new collecting sample, need to new samples carry out breakdown judge calculating, if by standardization after this
The input variable of two sample datas is respectively Zi={ zi1,zi1,,zim},Zj={ zj1,zj1,,zjm, breakdown judge and two
Coefficient is related, wherein coefficient 1 are as follows:Lij=1 is more consistent for 2 samples, Lij=0 is two samples
It is inconsistent；Coefficient 2 are as follows:Wherein EmaxFor setting value, Mij=1 is 2 samples
It is more identical, Mij=0 is two sample differences.According to above-mentioned two coefficient, calculated value is obtained are as follows: J=α Lij+(1-α)Mij,
Middle α ∈ (0,1).By the calculating to the calculated value, after the threshold value that it meets setting, just it is carried out based on improvement PLS's
Fire path temperature detection, can more effectively guarantee the accuracy of temperature prediction, prevent from depositing in the sensor in case of a fault, cause
Error control improves the stability contorting of system.
Wherein the design of variable universe fuzzy comprises the following steps:
(A) selection of the contraction-expansion factor of input and output domain, if the error e of fire path temperature and error rate ec's is basic
Domain is respectively as follows:Then inputting contraction-expansion factor isWherein (0,1) λ ∈,
It is the value range coefficient of domain, reflects the precision of control system, λ is bigger, and discourse of universe compression is more obvious；Export contraction-expansion factor
(B) Fuzzy processing: 7 fuzzy subsets { NB, NM, NS, ZE, PS, PM, PB } is selected to carry out Fuzzy processing；
(C) subordinating degree function is determined: the subordinating degree function selection Triangleshape grade of membership function at zero point, close to fuzzy
Domain boundary selects Gaussian subordinating degree function；
(D) fuzzy control rule table and fuzzy reasoning are established:
When e is larger, increase Kp, accelerates response speed, while avoiding overshoot, also need suitably to increase Kd；It is full to reduce integral
With reduction Ki；
When e is medium, reduce Kp, reduce overshoot, increase Ki, improve stability, Kd value is moderate；
When e is smaller, to guarantee stability, reduces Kp, improve stable state accuracy, Ki need to be increased, and when ec is larger, reduce
When Kd, ec are smaller, increase Kd；
(E) fuzzy quantity sharpening can obtain PID adjusting amount Δ Kp、ΔKi、ΔKd, then pid parameter are as follows:Wherein Kp0、Ki0、Kd0For pid parameter initial value, the initial parameter of PID is set are as follows: Kp0=7, Ki0=5,
Kd0=2.
Detailed description of the invention
Fig. 1 is the temperature optimization control principle drawing of quirk.
Specific embodiment
Further description of the technical solution of the present invention with reference to the accompanying drawings and examples.
As shown in Figure 1, a kind of temperature optimization control method of quirk, which comprises the steps of: (1) basis
Heating gas flow, regenerator top temperature, coal gas main pipe pressure, producer gas flow, flue temperature, these variables history number
According to foundation is based on the fire path temperature detection model for improving PLS；(2) above-mentioned variable is acquired in real time, according to described based on improvement PLS
Fire path temperature detection model quirk detected value and preset fire path temperature target value, calculate fire path temperature deviation；
(3) according to fire path temperature deviation, variable universe fuzzy-adaptation PID control fire path temperature is utilized.
The step of wherein foundation of the step (1) is based on the fire path temperature detection model for improving PLS is as follows:
(a) heating gas flow X1, regenerator top temperature X2, coal gas main pipe pressure X3, producer gas flow X4, cigarette are collected
The historical data of these variables of channel temp X5, as sample data, that is, input variable X of the model, fire path temperature Y1 is as the mould
The output variable Y of type；
(b) input variable X is pre-processed, firstly, being standardized transformation to input variable X, eliminates data
Secondly the influence of guiding principle amount rejects big error information；
(c) it according to pretreated input variable, establishes based on the fire path temperature detection model for improving PLS:
Assuming that input variable X and output variable Y is linearly related, i.e. 1., wherein B is regression coefficient matrix to Y=XB+E, and E is
Residual matrix；
When improving PLS modeling, X and Y is decomposed, is obtainedWherein T, U are score matrix, and P, Q are load moment
Battle array, E1, E2 are residual matrix；
2. according to formula, linear regression opening relationships: U=CT+R is utilized between vector, wherein R is residual matrix, and C is back
Attribution: C=UTT/(TTT), it is so as to calculate regression coefficient matrix
In order to guarantee the precision of prediction of model, model is verified using mean square error and root-mean-square error.
When receiving new collecting sample, need to new samples carry out breakdown judge calculating, if by standardization after this
The input variable of two sample datas is respectively Zi={ zi1,zi1,,zim},Zj={ zj1,zj1,,zjm, breakdown judge and two
Coefficient is related, wherein coefficient 1 are as follows:Lij=1 is more consistent for 2 samples, Lij=0 is two samples
It is inconsistent；Coefficient 2 are as follows:Wherein EmaxFor setting value, Mij=1 is 2 samples
It is more identical, Mij=0 is two sample differences.According to above-mentioned two coefficient, calculated value is obtained are as follows: J=α Lij+(1-α)Mij,
Middle α ∈ (0,1).By the calculating to the calculated value, after the threshold value that it meets setting, just it is carried out based on improvement PLS's
Fire path temperature detection, can more effectively guarantee the accuracy of temperature prediction, prevent from depositing in the sensor in case of a fault, cause
Error control improves the stability contorting of system.
Wherein the design of variable universe fuzzy comprises the following steps:
(A) selection of the contraction-expansion factor of input and output domain, if the error e of fire path temperature and error rate ec's is basic
Domain is respectively as follows:Then inputting contraction-expansion factor isWherein (0,1) λ ∈,
It is the value range coefficient of domain, reflects the precision of control system, λ is bigger, and discourse of universe compression is more obvious；Export contraction-expansion factor
(B) Fuzzy processing: 7 fuzzy subsets { NB, NM, NS, ZE, PS, PM, PB } is selected to carry out Fuzzy processing；
(C) subordinating degree function is determined: the subordinating degree function selection Triangleshape grade of membership function at zero point, close to fuzzy
Domain boundary selects Gaussian subordinating degree function；
(D) fuzzy control rule table and fuzzy reasoning are established: realizing that pid parameter is adjusted according to following experience,
When e is larger, increase Kp, accelerates response speed, while avoiding overshoot, also need suitably to increase Kd；It is full to reduce integral
With reduction Ki；
When e is medium, reduce Kp, reduce overshoot, increase Ki, improve stability, Kd value is moderate；
When e is smaller, to guarantee stability, reduces Kp, improve stable state accuracy, Ki need to be increased, and when ec is larger, reduce
When Kd, ec are smaller, increase Kd；
Fuzzy control rule table is obtained according to above-mentioned experience, as shown in table 1:
1 fuzzy control rule table of table
(E) fuzzy quantity sharpening can obtain PID adjusting amount Δ Kp、ΔKi、ΔKd, then pid parameter are as follows:Wherein Kp0、Ki0、Kd0For pid parameter initial value, the initial parameter of PID is set are as follows: Kp0=7, Ki0=5,
Kd0=2.
Claims (4)
1. a kind of temperature optimization control method of quirk, which comprises the steps of:
(1) according to heating gas flow, regenerator top temperature, coal gas main pipe pressure, producer gas flow, flue temperature, these
The historical data of variable is established based on the fire path temperature detection model for improving PLS；
(2) above-mentioned variable is acquired in real time, according to the quirk detected value based on the fire path temperature detection model for improving PLS and in advance
The fire path temperature target value first set calculates fire path temperature deviation；
(3) according to fire path temperature deviation, variable universe fuzzy-adaptation PID control fire path temperature is utilized.
2. the temperature optimization control method of quirk according to claim 1, which is characterized in that the foundation of the step (1)
The step of based on the fire path temperature detection model for improving PLS, is as follows:
(a) heating gas flow X1, regenerator top temperature X2, coal gas main pipe pressure X3, producer gas flow X4, flue temperature are collected
The historical data of these variables of X5 is spent, as sample data, that is, input variable X of the model, fire path temperature Y1 is as the model
Output variable Y；
(b) input variable X is pre-processed, firstly, being standardized transformation to input variable X, eliminates data guiding principle amount
Influence, secondly big error information is rejected；
(c) it according to pretreated input variable, establishes based on the fire path temperature detection model for improving PLS:
Assuming that input variable X and output variable Y is linearly related, i.e. 1., wherein B is regression coefficient matrix to Y=XB+E, and E is residual error
Matrix；
When improving PLS modeling, X and Y is decomposed, is obtainedWherein T, U are score matrix, and P, Q are matrix of loadings,
E1, E2 are residual matrix；
2. according to formula, between vector utilize linear regression opening relationships: U=CT+R, wherein R be residual matrix, C be return because
Son: C=UTT/(TTT), it is so as to calculate regression coefficient matrix
3. the temperature optimization control method of quirk according to claim 2, which is characterized in that (3) are according to quirk temperature
Deviation is spent, using variable universe fuzzy-adaptation PID control fire path temperature, wherein being comprised the following steps to the design of variable universe fuzzy:
(A) selection of the contraction-expansion factor of input and output domain, if the basic domain of the error e of fire path temperature and error rate ec
It is respectively as follows:Then inputting contraction-expansion factor isWherein (0,1) λ ∈ is opinion
The value range coefficient in domain, reflects the precision of control system, and λ is bigger, and discourse of universe compression is more obvious；Export contraction-expansion factor
(B) Fuzzy processing: 7 fuzzy subsets { NB, NM, NS, ZE, PS, PM, PB } is selected to carry out Fuzzy processing；
(C) subordinating degree function is determined: the subordinating degree function selection Triangleshape grade of membership function at zero point, close to fuzzy domain
Boundary selects Gaussian subordinating degree function；
(D) fuzzy control rule table and fuzzy reasoning are established:
When e is larger, increase Kp, accelerates response speed, while avoiding overshoot, also need suitably to increase Kd；To reduce integral saturation, subtract
Small Ki；
When e is medium, reduce Kp, reduce overshoot, increase Ki, improve stability, Kd value is moderate；
When e is smaller, to guarantee stability, reduces Kp, improve stable state accuracy, Ki need to be increased, and when ec is larger, reduce Kd, ec
When smaller, increase Kd；
(E) fuzzy quantity sharpening can obtain PID adjusting amount Δ Kp、ΔKi、ΔKd, then pid parameter are as follows:Wherein
Kp0、Ki0、Kd0For pid parameter initial value.
4. the temperature optimization control method of quirk according to claim 2, which is characterized in that pid parameter initial value are as follows: Kp0
=7, Ki0=5, Kd0=2.
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| CN111534308A (en) * | 2020-05-08 | 2020-08-14 | 马鞍山钢铁股份有限公司 | Method for controlling temperature of ultrahigh coke oven |
| CN111528532A (en) * | 2020-04-24 | 2020-08-14 | 云南中烟工业有限责任公司 | Temperature control system of device for heating non-combustible cigarettes and temperature control method thereof |
| CN112961709A (en) * | 2021-02-02 | 2021-06-15 | 鹤岗市征楠煤化工有限公司 | Tar removal process based on tail gas containing tar furnace |
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Cited By (7)
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| CN112961709A (en) * | 2021-02-02 | 2021-06-15 | 鹤岗市征楠煤化工有限公司 | Tar removal process based on tail gas containing tar furnace |
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| CN113110635B (en) * | 2021-03-26 | 2023-08-18 | 北京北方华创微电子装备有限公司 | Temperature control system, method and controller for semiconductor equipment and external ignition device |
| CN114510092A (en) * | 2022-02-17 | 2022-05-17 | 太原理工大学 | Transition packet internal temperature control system and method based on fuzzy PID (proportion integration differentiation) of prediction variable universe |
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